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“We’re almost guests in their clinical care”: Inpatient provider attitudes toward chronic disease management
Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,1 and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.2 While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.3
When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.5 However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.7 One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.8 This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.8 However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.
The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.
METHODS
In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.
We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.
We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.
All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.
We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;9 we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.
A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,10 which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.
Qualitative data were analyzed using a constant comparative analytic technique.11 The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.12 All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.9 Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).
RESULTS
We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.
We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.
Perceived Impact on Patient Outcomes
Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:
“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”
However, some providers acknowledged that there were potential risks to such management, including “prolonging hospital stays for nonemergent indications” and treatment with therapies that had previously led to an “adverse reaction that wasn’t clearly documented.” Providers were also concerned that treating chronic conditions may take focus away from acute conditions, which could lead to worse patient-centered outcomes. One attending in medicine described it:
“If you do potentially focus on those chronic issues, and there’s already a lot of other stuff going on with the patient, you might not be prioritizing the patient’s active issues appropriately. The patient’s saying, ‘I’m in pain. I’m in pain. I’m in pain,’ and you’re saying, ‘Thank you very much. Look, your heart failure, you didn’t get your beta-blocker.’ There could be a disconnect between patient’s goals, expectations, and your goals and expectations.”
Hospital Structural Characteristics
For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”
Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.
Provider Knowledge and Self-Efficacy
Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.
For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.
A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”
Hospital Priorities
Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”
Continuity and Communication
According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.
Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”
Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”
DISCUSSION
Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,13 clinical inertia,14 difficulty with close monitoring of patients,15 and care fragmentation.16 Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,7 providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.
Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,7 our study and others8 have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.17 The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.
For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.19
Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.
The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.
In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.
Disclosures
This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.
1. Friedman B, Jiang HJ, Elixhauser A, Segal A. Hospital inpatient costs for adults with multiple chronic conditions. Med Care Res Rev. 2006;63(3):327-346. PubMed
2. Steiner CA, Friedman B. Hospital utilization, costs, and mortality for adults with multiple chronic conditions, Nationwide Inpatient Sample, 2009. Prev Chronic Dis. 2013;10:E62. PubMed
3. Blecker S, Paul M, Taksler G, Ogedegbe G, Katz S. Heart failure-associated hospitalizations in the United States. J Am Coll Cardiol. 2013;61(12):1259-1267. PubMed
4. Fonarow GC. Role of in-hospital initiation of carvedilol to improve treatment rates and clinical outcomes. Am J Cardiol. 2004;93(9A):77B-81B. PubMed
5. Touze E, Coste J, Voicu M, et al. Importance of in-hospital initiation of therapies and therapeutic inertia in secondary stroke prevention: IMplementation of Prevention After a Cerebrovascular evenT (IMPACT) Study. Stroke. 2008;39(6):1834-1843. PubMed
6. Ovbiagele B, Saver JL, Fredieu A, et al. In-hospital initiation of secondary stroke prevention therapies yields high rates of adherence at follow-up. Stroke. 2004;35(12):2879-2883. PubMed
7. Steinman MA, Auerbach AD. Managing chronic disease in hospitalized patients. JAMA Intern Med. 2013;173(20):1857-1858. PubMed
8. Breu AC, Allen-Dicker J, Mueller S, Palamara K, Hinami K, Herzig SJ. Hospitalist and primary care physician perspectives on medication management of chronic conditions for hospitalized patients. J Hosp Med. 2014;9(5):303-309. PubMed
9. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. PubMed
10. Morse JM. The significance of saturation. Qualitative Health Research. 1995;5(2):147-149.
11. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Services Research. 2007;42(4):1758-1772. PubMed
12. Riegel B, Dickson VV, Topaz M. Qualitative analysis of naturalistic decision making in adults with chronic heart failure. Nurs Res. 2013;62(2):91-98. PubMed
13. Linzer M, Konrad TR, Douglas J, et al. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med. 2000;15(7):441-450. PubMed
14. Phillips LS, Branch WT, Cook CB, et al. Clinical inertia. Ann Intern Med. 2001;135(9):825-834.
15. Dev S, Hoffman TK, Kavalieratos D, et al. Barriers to adoption of mineralocorticoid receptor antagonists in patients with heart failure: A mixed-methods study. J Am Heart Assoc. 2016;4(3):e002493. PubMed
16. Stange KC. The problem of fragmentation and the need for integrative solutions. Ann Fam Med. 2009;7(2):100-103. PubMed
17. Fonarow GC, Abraham WT, Albert NM, et al. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA. 2007;297(1):61-70. PubMed
18. Shah M, Norwood CA, Farias S, Ibrahim S, Chong PH, Fogelfeld L. Diabetes transitional care from inpatient to outpatient setting: pharmacist discharge counseling. J Pharm Pract. 2013;26(2):120-124. PubMed
19. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831-841. PubMed
Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,1 and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.2 While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.3
When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.5 However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.7 One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.8 This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.8 However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.
The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.
METHODS
In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.
We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.
We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.
All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.
We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;9 we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.
A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,10 which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.
Qualitative data were analyzed using a constant comparative analytic technique.11 The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.12 All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.9 Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).
RESULTS
We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.
We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.
Perceived Impact on Patient Outcomes
Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:
“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”
However, some providers acknowledged that there were potential risks to such management, including “prolonging hospital stays for nonemergent indications” and treatment with therapies that had previously led to an “adverse reaction that wasn’t clearly documented.” Providers were also concerned that treating chronic conditions may take focus away from acute conditions, which could lead to worse patient-centered outcomes. One attending in medicine described it:
“If you do potentially focus on those chronic issues, and there’s already a lot of other stuff going on with the patient, you might not be prioritizing the patient’s active issues appropriately. The patient’s saying, ‘I’m in pain. I’m in pain. I’m in pain,’ and you’re saying, ‘Thank you very much. Look, your heart failure, you didn’t get your beta-blocker.’ There could be a disconnect between patient’s goals, expectations, and your goals and expectations.”
Hospital Structural Characteristics
For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”
Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.
Provider Knowledge and Self-Efficacy
Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.
For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.
A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”
Hospital Priorities
Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”
Continuity and Communication
According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.
Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”
Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”
DISCUSSION
Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,13 clinical inertia,14 difficulty with close monitoring of patients,15 and care fragmentation.16 Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,7 providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.
Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,7 our study and others8 have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.17 The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.
For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.19
Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.
The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.
In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.
Disclosures
This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.
Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,1 and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.2 While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.3
When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.5 However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.7 One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.8 This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.8 However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.
The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.
METHODS
In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.
We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.
We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.
All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.
We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;9 we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.
A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,10 which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.
Qualitative data were analyzed using a constant comparative analytic technique.11 The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.12 All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.9 Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).
RESULTS
We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.
We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.
Perceived Impact on Patient Outcomes
Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:
“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”
However, some providers acknowledged that there were potential risks to such management, including “prolonging hospital stays for nonemergent indications” and treatment with therapies that had previously led to an “adverse reaction that wasn’t clearly documented.” Providers were also concerned that treating chronic conditions may take focus away from acute conditions, which could lead to worse patient-centered outcomes. One attending in medicine described it:
“If you do potentially focus on those chronic issues, and there’s already a lot of other stuff going on with the patient, you might not be prioritizing the patient’s active issues appropriately. The patient’s saying, ‘I’m in pain. I’m in pain. I’m in pain,’ and you’re saying, ‘Thank you very much. Look, your heart failure, you didn’t get your beta-blocker.’ There could be a disconnect between patient’s goals, expectations, and your goals and expectations.”
Hospital Structural Characteristics
For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”
Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.
Provider Knowledge and Self-Efficacy
Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.
For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.
A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”
Hospital Priorities
Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”
Continuity and Communication
According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.
Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”
Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”
DISCUSSION
Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,13 clinical inertia,14 difficulty with close monitoring of patients,15 and care fragmentation.16 Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,7 providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.
Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,7 our study and others8 have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.17 The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.
For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.19
Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.
The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.
In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.
Disclosures
This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.
1. Friedman B, Jiang HJ, Elixhauser A, Segal A. Hospital inpatient costs for adults with multiple chronic conditions. Med Care Res Rev. 2006;63(3):327-346. PubMed
2. Steiner CA, Friedman B. Hospital utilization, costs, and mortality for adults with multiple chronic conditions, Nationwide Inpatient Sample, 2009. Prev Chronic Dis. 2013;10:E62. PubMed
3. Blecker S, Paul M, Taksler G, Ogedegbe G, Katz S. Heart failure-associated hospitalizations in the United States. J Am Coll Cardiol. 2013;61(12):1259-1267. PubMed
4. Fonarow GC. Role of in-hospital initiation of carvedilol to improve treatment rates and clinical outcomes. Am J Cardiol. 2004;93(9A):77B-81B. PubMed
5. Touze E, Coste J, Voicu M, et al. Importance of in-hospital initiation of therapies and therapeutic inertia in secondary stroke prevention: IMplementation of Prevention After a Cerebrovascular evenT (IMPACT) Study. Stroke. 2008;39(6):1834-1843. PubMed
6. Ovbiagele B, Saver JL, Fredieu A, et al. In-hospital initiation of secondary stroke prevention therapies yields high rates of adherence at follow-up. Stroke. 2004;35(12):2879-2883. PubMed
7. Steinman MA, Auerbach AD. Managing chronic disease in hospitalized patients. JAMA Intern Med. 2013;173(20):1857-1858. PubMed
8. Breu AC, Allen-Dicker J, Mueller S, Palamara K, Hinami K, Herzig SJ. Hospitalist and primary care physician perspectives on medication management of chronic conditions for hospitalized patients. J Hosp Med. 2014;9(5):303-309. PubMed
9. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. PubMed
10. Morse JM. The significance of saturation. Qualitative Health Research. 1995;5(2):147-149.
11. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Services Research. 2007;42(4):1758-1772. PubMed
12. Riegel B, Dickson VV, Topaz M. Qualitative analysis of naturalistic decision making in adults with chronic heart failure. Nurs Res. 2013;62(2):91-98. PubMed
13. Linzer M, Konrad TR, Douglas J, et al. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med. 2000;15(7):441-450. PubMed
14. Phillips LS, Branch WT, Cook CB, et al. Clinical inertia. Ann Intern Med. 2001;135(9):825-834.
15. Dev S, Hoffman TK, Kavalieratos D, et al. Barriers to adoption of mineralocorticoid receptor antagonists in patients with heart failure: A mixed-methods study. J Am Heart Assoc. 2016;4(3):e002493. PubMed
16. Stange KC. The problem of fragmentation and the need for integrative solutions. Ann Fam Med. 2009;7(2):100-103. PubMed
17. Fonarow GC, Abraham WT, Albert NM, et al. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA. 2007;297(1):61-70. PubMed
18. Shah M, Norwood CA, Farias S, Ibrahim S, Chong PH, Fogelfeld L. Diabetes transitional care from inpatient to outpatient setting: pharmacist discharge counseling. J Pharm Pract. 2013;26(2):120-124. PubMed
19. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831-841. PubMed
1. Friedman B, Jiang HJ, Elixhauser A, Segal A. Hospital inpatient costs for adults with multiple chronic conditions. Med Care Res Rev. 2006;63(3):327-346. PubMed
2. Steiner CA, Friedman B. Hospital utilization, costs, and mortality for adults with multiple chronic conditions, Nationwide Inpatient Sample, 2009. Prev Chronic Dis. 2013;10:E62. PubMed
3. Blecker S, Paul M, Taksler G, Ogedegbe G, Katz S. Heart failure-associated hospitalizations in the United States. J Am Coll Cardiol. 2013;61(12):1259-1267. PubMed
4. Fonarow GC. Role of in-hospital initiation of carvedilol to improve treatment rates and clinical outcomes. Am J Cardiol. 2004;93(9A):77B-81B. PubMed
5. Touze E, Coste J, Voicu M, et al. Importance of in-hospital initiation of therapies and therapeutic inertia in secondary stroke prevention: IMplementation of Prevention After a Cerebrovascular evenT (IMPACT) Study. Stroke. 2008;39(6):1834-1843. PubMed
6. Ovbiagele B, Saver JL, Fredieu A, et al. In-hospital initiation of secondary stroke prevention therapies yields high rates of adherence at follow-up. Stroke. 2004;35(12):2879-2883. PubMed
7. Steinman MA, Auerbach AD. Managing chronic disease in hospitalized patients. JAMA Intern Med. 2013;173(20):1857-1858. PubMed
8. Breu AC, Allen-Dicker J, Mueller S, Palamara K, Hinami K, Herzig SJ. Hospitalist and primary care physician perspectives on medication management of chronic conditions for hospitalized patients. J Hosp Med. 2014;9(5):303-309. PubMed
9. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. PubMed
10. Morse JM. The significance of saturation. Qualitative Health Research. 1995;5(2):147-149.
11. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Services Research. 2007;42(4):1758-1772. PubMed
12. Riegel B, Dickson VV, Topaz M. Qualitative analysis of naturalistic decision making in adults with chronic heart failure. Nurs Res. 2013;62(2):91-98. PubMed
13. Linzer M, Konrad TR, Douglas J, et al. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med. 2000;15(7):441-450. PubMed
14. Phillips LS, Branch WT, Cook CB, et al. Clinical inertia. Ann Intern Med. 2001;135(9):825-834.
15. Dev S, Hoffman TK, Kavalieratos D, et al. Barriers to adoption of mineralocorticoid receptor antagonists in patients with heart failure: A mixed-methods study. J Am Heart Assoc. 2016;4(3):e002493. PubMed
16. Stange KC. The problem of fragmentation and the need for integrative solutions. Ann Fam Med. 2009;7(2):100-103. PubMed
17. Fonarow GC, Abraham WT, Albert NM, et al. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA. 2007;297(1):61-70. PubMed
18. Shah M, Norwood CA, Farias S, Ibrahim S, Chong PH, Fogelfeld L. Diabetes transitional care from inpatient to outpatient setting: pharmacist discharge counseling. J Pharm Pract. 2013;26(2):120-124. PubMed
19. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831-841. PubMed
© 2017 Society of Hospital Medicine
The unmet need for postacute rehabilitation among medicare observation patients: A single-center study
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
© 2017 Society of Hospital Medicine
Hospital medicine resident training tracks: Developing the hospital medicine pipeline
The field of hospital medicine (HM) is rapidly expanding in the areas of clinical medicine, administration, and quality improvement (QI).1 Emerging with this growth is a gap in the traditional internal medicine (IM) training and skills needed to be effective in HM.1,2 These skills include clinical and nonclinical aptitudes, such as process improvement, health care economics, and leadership.1-3 However, resident education on these topics must compete with other required curricular content in IM residency training.2,4 Few IM residencies offer focused HM training that emphasizes key components of successful HM careers.3,5
Within the past decade, designated HM tracks within IM residency programs have been proposed as a potential solution. Initially, calls for such tracks focused on gaps in the clinical competencies required of hospitalists.1 Tracks have since evolved to also include skills required to drive high-value care, process improvement, and scholarship. Designated HM tracks address these areas through greater breadth of curricula, additional time for reflection, participation in group projects, and active application to clinical care.4 We conducted a study to identify themes that could inform the ongoing evolution of dedicated HM tracks.
METHODS
Programs were initially identified through communication among professional networks. The phrases hospital medicine residency track and internal medicine residency hospitalist track were used in broader Google searches, as there is no database of such tracks. Searches were performed quarterly during the 2015–2016 academic year. The top 20 hits were manually filtered to identify tracks affiliated with major academic centers. IM residency program websites provided basic information for programs with tracks. We excluded tracks focused entirely on QI6 because, though a crucial part of HM, QI training alone is probably insufficient for preparing residents for success as hospitalists on residency completion. Similarly, IM residencies with stand-alone HM clinical rotations without longitudinal HM curricula were excluded.
Semistructured interviews with track directors were conducted by e-mail or telephone for all tracks except one, the details of which are published.7 We tabulated data and reviewed qualitative information to identify themes among the different tracks. As this study did not involve human participants, Institutional Review Board approval was not needed.
RESULTS
We identified 11 HM residency training programs at major academic centers across the United States: Cleveland Clinic, Stanford University, Tulane University, University of California Davis, University of California Irvine, University of Colorado, University of Kentucky, University of Minnesota, University of New Mexico, Virginia Commonwealth University, and Wake Forest University (Table 1). We reviewed the websites of about 10 other programs, but none suggested existence of a track. Additional programs contacted reported no current track.
Track Participants and Structure
HM tracks mainly target third-year residents (Table 1). Some extend into the second year of residency, and 4 have opportunities for intern involvement, including a separate match number at Colorado. Tracks accept up to 12 residents per class. Two programs, at Colorado and Virginia, are part of IM programs in which all residents belong to a track (eg, HM, primary care, research).
HM track structures vary widely and are heavily influenced by the content delivery platforms of their IM residency programs. Several HM track directors emphasized the importance of fitting into existing educational frameworks to ensure access to residents and to minimize the burden of participation. Four programs deliver the bulk of their nonclinical content in dedicated blocks; 6 others use brief recurring sessions to deliver smaller aliquots longitudinally (Table 1). The number of protected hours for content delivery ranges from 10 to more than 40 annually. All tracks use multiple content delivery modes, including didactic sessions and journal clubs. Four tracks employ panel discussions to explore career options within HM. Several also use online platforms, including discussions, readings, and modules.
Quality Improvement
The vast majority of curricula prominently feature experiential QI project involvement (Table 2). These mentored longitudinal projects allow applied delivery of content, such as QI methods and management skills. Four tracks use material from the Institute for Healthcare Improvement.8 Several also offer dedicated QI rotations that immerse residents in ongoing QI efforts.
Institutional partnerships support these initiatives at several sites. The Minnesota track is a joint venture of the university and Regions Hospital, a nonprofit community hospital. The Virginia track positions HM residents to lead university-wide interdisciplinary QI teams. For project support, the Colorado and Kentucky tracks partner with local QI resources—the Institute for Healthcare Quality, Safety, and Efficiency at Colorado and the Office of Value and Innovation in Healthcare Delivery at Kentucky.
Health Care Economics and Value
Many programs leverage the rapidly growing emphasis on health care “value” as an opportunity for synergy between IM programs and HM tracks. Examples include involving residents in efforts to improve documentation or didactic instruction on topics such as health care finance. The New Mexico and Wake Forest tracks offer elective rotations on health care economics. Several track directors mentioned successfully expanding curricula on health care value from the HM track into IM residency programs at large, providing a measurable service to the residency programs while ensuring content delivery and freeing up additional time for track activities.
Scholarship and Career Development
Most programs provide targeted career development for residents. Six tracks provide sessions on job procurement skills, such as curriculum vitae preparation and interviewing (Table 2). Many also provide content on venues for disseminating scholarly activity. The Colorado, Kentucky, New Mexico, and Tulane programs feature content on abstract and poster creation. Leadership development is addressed in several tracks through dedicated track activities or participation in discrete, outside-track events. Specifically, Colorado offers a leadership track for residents interested in hospital administration, Cleveland has a leadership journal club, Wake Forest enrolls HM residents in leadership training available through the university, and Minnesota sends residents to the Society of Hospital Medicine’s Leadership Academy (Table 2).
Clinical Rotations
Almost all tracks include a clinical rotation, typically pairing residents directly with hospitalist attendings to encourage autonomy and mentorship. Several also offer elective rotations in various disciplines within HM (Table 2). The Kentucky and Virginia tracks incorporate working with advanced practice providers into their practicums. The Cleveland, Minnesota, Tulane, and Virginia tracks offer HM rotations in community hospitals or postacute settings.
HM rotations also pair clinical experiences with didactic education on relevant topics (eg, billing and coding). The Cleveland, Minnesota, and Virginia tracks developed clinical rotations reflecting the common 7-on and 7-off schedule with nonclinical obligations, such as seminars linking specific content to clinical experiences, during nonclinical time.
DISCUSSION
Our investigation into the current state of HM training found that HM track curricula focus largely on QI, health care economics, and professional development. This focus likely developed in response to hospitalists’ increasing engagement in related endeavors. HM tracks have dynamic and variable structures, reflecting an evolving field and the need to fit into existing IM residency program structures. Similarly, the content covered in HM tracks is tightly linked to perceived opportunities within IM residency curricula. The heterogeneity of content suggests the breadth and ambiguity of necessary competencies for aspiring hospitalists. One of the 11 tracks has not had any residents enroll within the past few years—a testament to the continued effort necessary to sustain such tracks, including curricular updates and recruiting. Conversely, many programs now share track content with the larger IM residency program, suggesting HM tracks may be near the forefront of medical education in some areas.
Our study had several limitations. As we are unaware of any databases of HM tracks, we discussed tracks with professional contacts, performed Internet searches, and reviewed IM residency program websites. Our search, however, was not exhaustive; despite our best efforts, we may have missed or mischaracterized some track offerings. Nevertheless, we think that our analysis represents the first thorough compilation of HM tracks and that it will be useful to institutions seeking to create or enhance HM-specific training.
As the field continues to evolve, we are optimistic about the future of HM training. We suspect that HM residency training tracks will continue to expand. More work is needed so these tracks can adjust to the changing HM and IM residency program landscapes and supply well-trained physicians for the HM workforce.
Acknowledgment
The authors thank track directors Alpesh Amin, David Gugliotti, Rick Hilger, Karnjit Johl, Nasir Majeed, Georgia McIntosh, Charles Pizanis, and Jeff Wiese for making this study possible.
Disclosure
Nothing to report.
1. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs [published correction appears in J Gen Intern Med. 2008;23(11):1931]. J Gen Intern Med. 2008;23(7):1110-1115. PubMed
2. Arora V, Guardiano S, Donaldson D, Storch I, Hemstreet P. Closing the gap between internal medicine training and practice: recommendations from recent graduates. Am J Med. 2005;118(6):680-685. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008;75(5):436-441. PubMed
4. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008;23(7):1122-1123. PubMed
5. Glasheen JJ, Epstein KR, Siegal E, Kutner JS, Prochazka AV. The spectrum of community-based hospitalist practice: a call to tailor internal medicine residency training. Arch Intern Med. 2007;167(7):727-728. PubMed
6. Patel N, Brennan PJ, Metlay J, Bellini L, Shannon RP, Myers JS. Building the pipeline: the creation of a residency training pathway for future physician leaders in health care quality. Acad Med. 2015;90(2):185-190. PubMed
7. Kumar A, Smeraglio A, Witteles R, et al. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11(9):646-649. PubMed
8. Institute for Healthcare Improvement website. http://www.ihi.org. Accessed December 15, 2015.
The field of hospital medicine (HM) is rapidly expanding in the areas of clinical medicine, administration, and quality improvement (QI).1 Emerging with this growth is a gap in the traditional internal medicine (IM) training and skills needed to be effective in HM.1,2 These skills include clinical and nonclinical aptitudes, such as process improvement, health care economics, and leadership.1-3 However, resident education on these topics must compete with other required curricular content in IM residency training.2,4 Few IM residencies offer focused HM training that emphasizes key components of successful HM careers.3,5
Within the past decade, designated HM tracks within IM residency programs have been proposed as a potential solution. Initially, calls for such tracks focused on gaps in the clinical competencies required of hospitalists.1 Tracks have since evolved to also include skills required to drive high-value care, process improvement, and scholarship. Designated HM tracks address these areas through greater breadth of curricula, additional time for reflection, participation in group projects, and active application to clinical care.4 We conducted a study to identify themes that could inform the ongoing evolution of dedicated HM tracks.
METHODS
Programs were initially identified through communication among professional networks. The phrases hospital medicine residency track and internal medicine residency hospitalist track were used in broader Google searches, as there is no database of such tracks. Searches were performed quarterly during the 2015–2016 academic year. The top 20 hits were manually filtered to identify tracks affiliated with major academic centers. IM residency program websites provided basic information for programs with tracks. We excluded tracks focused entirely on QI6 because, though a crucial part of HM, QI training alone is probably insufficient for preparing residents for success as hospitalists on residency completion. Similarly, IM residencies with stand-alone HM clinical rotations without longitudinal HM curricula were excluded.
Semistructured interviews with track directors were conducted by e-mail or telephone for all tracks except one, the details of which are published.7 We tabulated data and reviewed qualitative information to identify themes among the different tracks. As this study did not involve human participants, Institutional Review Board approval was not needed.
RESULTS
We identified 11 HM residency training programs at major academic centers across the United States: Cleveland Clinic, Stanford University, Tulane University, University of California Davis, University of California Irvine, University of Colorado, University of Kentucky, University of Minnesota, University of New Mexico, Virginia Commonwealth University, and Wake Forest University (Table 1). We reviewed the websites of about 10 other programs, but none suggested existence of a track. Additional programs contacted reported no current track.
Track Participants and Structure
HM tracks mainly target third-year residents (Table 1). Some extend into the second year of residency, and 4 have opportunities for intern involvement, including a separate match number at Colorado. Tracks accept up to 12 residents per class. Two programs, at Colorado and Virginia, are part of IM programs in which all residents belong to a track (eg, HM, primary care, research).
HM track structures vary widely and are heavily influenced by the content delivery platforms of their IM residency programs. Several HM track directors emphasized the importance of fitting into existing educational frameworks to ensure access to residents and to minimize the burden of participation. Four programs deliver the bulk of their nonclinical content in dedicated blocks; 6 others use brief recurring sessions to deliver smaller aliquots longitudinally (Table 1). The number of protected hours for content delivery ranges from 10 to more than 40 annually. All tracks use multiple content delivery modes, including didactic sessions and journal clubs. Four tracks employ panel discussions to explore career options within HM. Several also use online platforms, including discussions, readings, and modules.
Quality Improvement
The vast majority of curricula prominently feature experiential QI project involvement (Table 2). These mentored longitudinal projects allow applied delivery of content, such as QI methods and management skills. Four tracks use material from the Institute for Healthcare Improvement.8 Several also offer dedicated QI rotations that immerse residents in ongoing QI efforts.
Institutional partnerships support these initiatives at several sites. The Minnesota track is a joint venture of the university and Regions Hospital, a nonprofit community hospital. The Virginia track positions HM residents to lead university-wide interdisciplinary QI teams. For project support, the Colorado and Kentucky tracks partner with local QI resources—the Institute for Healthcare Quality, Safety, and Efficiency at Colorado and the Office of Value and Innovation in Healthcare Delivery at Kentucky.
Health Care Economics and Value
Many programs leverage the rapidly growing emphasis on health care “value” as an opportunity for synergy between IM programs and HM tracks. Examples include involving residents in efforts to improve documentation or didactic instruction on topics such as health care finance. The New Mexico and Wake Forest tracks offer elective rotations on health care economics. Several track directors mentioned successfully expanding curricula on health care value from the HM track into IM residency programs at large, providing a measurable service to the residency programs while ensuring content delivery and freeing up additional time for track activities.
Scholarship and Career Development
Most programs provide targeted career development for residents. Six tracks provide sessions on job procurement skills, such as curriculum vitae preparation and interviewing (Table 2). Many also provide content on venues for disseminating scholarly activity. The Colorado, Kentucky, New Mexico, and Tulane programs feature content on abstract and poster creation. Leadership development is addressed in several tracks through dedicated track activities or participation in discrete, outside-track events. Specifically, Colorado offers a leadership track for residents interested in hospital administration, Cleveland has a leadership journal club, Wake Forest enrolls HM residents in leadership training available through the university, and Minnesota sends residents to the Society of Hospital Medicine’s Leadership Academy (Table 2).
Clinical Rotations
Almost all tracks include a clinical rotation, typically pairing residents directly with hospitalist attendings to encourage autonomy and mentorship. Several also offer elective rotations in various disciplines within HM (Table 2). The Kentucky and Virginia tracks incorporate working with advanced practice providers into their practicums. The Cleveland, Minnesota, Tulane, and Virginia tracks offer HM rotations in community hospitals or postacute settings.
HM rotations also pair clinical experiences with didactic education on relevant topics (eg, billing and coding). The Cleveland, Minnesota, and Virginia tracks developed clinical rotations reflecting the common 7-on and 7-off schedule with nonclinical obligations, such as seminars linking specific content to clinical experiences, during nonclinical time.
DISCUSSION
Our investigation into the current state of HM training found that HM track curricula focus largely on QI, health care economics, and professional development. This focus likely developed in response to hospitalists’ increasing engagement in related endeavors. HM tracks have dynamic and variable structures, reflecting an evolving field and the need to fit into existing IM residency program structures. Similarly, the content covered in HM tracks is tightly linked to perceived opportunities within IM residency curricula. The heterogeneity of content suggests the breadth and ambiguity of necessary competencies for aspiring hospitalists. One of the 11 tracks has not had any residents enroll within the past few years—a testament to the continued effort necessary to sustain such tracks, including curricular updates and recruiting. Conversely, many programs now share track content with the larger IM residency program, suggesting HM tracks may be near the forefront of medical education in some areas.
Our study had several limitations. As we are unaware of any databases of HM tracks, we discussed tracks with professional contacts, performed Internet searches, and reviewed IM residency program websites. Our search, however, was not exhaustive; despite our best efforts, we may have missed or mischaracterized some track offerings. Nevertheless, we think that our analysis represents the first thorough compilation of HM tracks and that it will be useful to institutions seeking to create or enhance HM-specific training.
As the field continues to evolve, we are optimistic about the future of HM training. We suspect that HM residency training tracks will continue to expand. More work is needed so these tracks can adjust to the changing HM and IM residency program landscapes and supply well-trained physicians for the HM workforce.
Acknowledgment
The authors thank track directors Alpesh Amin, David Gugliotti, Rick Hilger, Karnjit Johl, Nasir Majeed, Georgia McIntosh, Charles Pizanis, and Jeff Wiese for making this study possible.
Disclosure
Nothing to report.
The field of hospital medicine (HM) is rapidly expanding in the areas of clinical medicine, administration, and quality improvement (QI).1 Emerging with this growth is a gap in the traditional internal medicine (IM) training and skills needed to be effective in HM.1,2 These skills include clinical and nonclinical aptitudes, such as process improvement, health care economics, and leadership.1-3 However, resident education on these topics must compete with other required curricular content in IM residency training.2,4 Few IM residencies offer focused HM training that emphasizes key components of successful HM careers.3,5
Within the past decade, designated HM tracks within IM residency programs have been proposed as a potential solution. Initially, calls for such tracks focused on gaps in the clinical competencies required of hospitalists.1 Tracks have since evolved to also include skills required to drive high-value care, process improvement, and scholarship. Designated HM tracks address these areas through greater breadth of curricula, additional time for reflection, participation in group projects, and active application to clinical care.4 We conducted a study to identify themes that could inform the ongoing evolution of dedicated HM tracks.
METHODS
Programs were initially identified through communication among professional networks. The phrases hospital medicine residency track and internal medicine residency hospitalist track were used in broader Google searches, as there is no database of such tracks. Searches were performed quarterly during the 2015–2016 academic year. The top 20 hits were manually filtered to identify tracks affiliated with major academic centers. IM residency program websites provided basic information for programs with tracks. We excluded tracks focused entirely on QI6 because, though a crucial part of HM, QI training alone is probably insufficient for preparing residents for success as hospitalists on residency completion. Similarly, IM residencies with stand-alone HM clinical rotations without longitudinal HM curricula were excluded.
Semistructured interviews with track directors were conducted by e-mail or telephone for all tracks except one, the details of which are published.7 We tabulated data and reviewed qualitative information to identify themes among the different tracks. As this study did not involve human participants, Institutional Review Board approval was not needed.
RESULTS
We identified 11 HM residency training programs at major academic centers across the United States: Cleveland Clinic, Stanford University, Tulane University, University of California Davis, University of California Irvine, University of Colorado, University of Kentucky, University of Minnesota, University of New Mexico, Virginia Commonwealth University, and Wake Forest University (Table 1). We reviewed the websites of about 10 other programs, but none suggested existence of a track. Additional programs contacted reported no current track.
Track Participants and Structure
HM tracks mainly target third-year residents (Table 1). Some extend into the second year of residency, and 4 have opportunities for intern involvement, including a separate match number at Colorado. Tracks accept up to 12 residents per class. Two programs, at Colorado and Virginia, are part of IM programs in which all residents belong to a track (eg, HM, primary care, research).
HM track structures vary widely and are heavily influenced by the content delivery platforms of their IM residency programs. Several HM track directors emphasized the importance of fitting into existing educational frameworks to ensure access to residents and to minimize the burden of participation. Four programs deliver the bulk of their nonclinical content in dedicated blocks; 6 others use brief recurring sessions to deliver smaller aliquots longitudinally (Table 1). The number of protected hours for content delivery ranges from 10 to more than 40 annually. All tracks use multiple content delivery modes, including didactic sessions and journal clubs. Four tracks employ panel discussions to explore career options within HM. Several also use online platforms, including discussions, readings, and modules.
Quality Improvement
The vast majority of curricula prominently feature experiential QI project involvement (Table 2). These mentored longitudinal projects allow applied delivery of content, such as QI methods and management skills. Four tracks use material from the Institute for Healthcare Improvement.8 Several also offer dedicated QI rotations that immerse residents in ongoing QI efforts.
Institutional partnerships support these initiatives at several sites. The Minnesota track is a joint venture of the university and Regions Hospital, a nonprofit community hospital. The Virginia track positions HM residents to lead university-wide interdisciplinary QI teams. For project support, the Colorado and Kentucky tracks partner with local QI resources—the Institute for Healthcare Quality, Safety, and Efficiency at Colorado and the Office of Value and Innovation in Healthcare Delivery at Kentucky.
Health Care Economics and Value
Many programs leverage the rapidly growing emphasis on health care “value” as an opportunity for synergy between IM programs and HM tracks. Examples include involving residents in efforts to improve documentation or didactic instruction on topics such as health care finance. The New Mexico and Wake Forest tracks offer elective rotations on health care economics. Several track directors mentioned successfully expanding curricula on health care value from the HM track into IM residency programs at large, providing a measurable service to the residency programs while ensuring content delivery and freeing up additional time for track activities.
Scholarship and Career Development
Most programs provide targeted career development for residents. Six tracks provide sessions on job procurement skills, such as curriculum vitae preparation and interviewing (Table 2). Many also provide content on venues for disseminating scholarly activity. The Colorado, Kentucky, New Mexico, and Tulane programs feature content on abstract and poster creation. Leadership development is addressed in several tracks through dedicated track activities or participation in discrete, outside-track events. Specifically, Colorado offers a leadership track for residents interested in hospital administration, Cleveland has a leadership journal club, Wake Forest enrolls HM residents in leadership training available through the university, and Minnesota sends residents to the Society of Hospital Medicine’s Leadership Academy (Table 2).
Clinical Rotations
Almost all tracks include a clinical rotation, typically pairing residents directly with hospitalist attendings to encourage autonomy and mentorship. Several also offer elective rotations in various disciplines within HM (Table 2). The Kentucky and Virginia tracks incorporate working with advanced practice providers into their practicums. The Cleveland, Minnesota, Tulane, and Virginia tracks offer HM rotations in community hospitals or postacute settings.
HM rotations also pair clinical experiences with didactic education on relevant topics (eg, billing and coding). The Cleveland, Minnesota, and Virginia tracks developed clinical rotations reflecting the common 7-on and 7-off schedule with nonclinical obligations, such as seminars linking specific content to clinical experiences, during nonclinical time.
DISCUSSION
Our investigation into the current state of HM training found that HM track curricula focus largely on QI, health care economics, and professional development. This focus likely developed in response to hospitalists’ increasing engagement in related endeavors. HM tracks have dynamic and variable structures, reflecting an evolving field and the need to fit into existing IM residency program structures. Similarly, the content covered in HM tracks is tightly linked to perceived opportunities within IM residency curricula. The heterogeneity of content suggests the breadth and ambiguity of necessary competencies for aspiring hospitalists. One of the 11 tracks has not had any residents enroll within the past few years—a testament to the continued effort necessary to sustain such tracks, including curricular updates and recruiting. Conversely, many programs now share track content with the larger IM residency program, suggesting HM tracks may be near the forefront of medical education in some areas.
Our study had several limitations. As we are unaware of any databases of HM tracks, we discussed tracks with professional contacts, performed Internet searches, and reviewed IM residency program websites. Our search, however, was not exhaustive; despite our best efforts, we may have missed or mischaracterized some track offerings. Nevertheless, we think that our analysis represents the first thorough compilation of HM tracks and that it will be useful to institutions seeking to create or enhance HM-specific training.
As the field continues to evolve, we are optimistic about the future of HM training. We suspect that HM residency training tracks will continue to expand. More work is needed so these tracks can adjust to the changing HM and IM residency program landscapes and supply well-trained physicians for the HM workforce.
Acknowledgment
The authors thank track directors Alpesh Amin, David Gugliotti, Rick Hilger, Karnjit Johl, Nasir Majeed, Georgia McIntosh, Charles Pizanis, and Jeff Wiese for making this study possible.
Disclosure
Nothing to report.
1. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs [published correction appears in J Gen Intern Med. 2008;23(11):1931]. J Gen Intern Med. 2008;23(7):1110-1115. PubMed
2. Arora V, Guardiano S, Donaldson D, Storch I, Hemstreet P. Closing the gap between internal medicine training and practice: recommendations from recent graduates. Am J Med. 2005;118(6):680-685. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008;75(5):436-441. PubMed
4. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008;23(7):1122-1123. PubMed
5. Glasheen JJ, Epstein KR, Siegal E, Kutner JS, Prochazka AV. The spectrum of community-based hospitalist practice: a call to tailor internal medicine residency training. Arch Intern Med. 2007;167(7):727-728. PubMed
6. Patel N, Brennan PJ, Metlay J, Bellini L, Shannon RP, Myers JS. Building the pipeline: the creation of a residency training pathway for future physician leaders in health care quality. Acad Med. 2015;90(2):185-190. PubMed
7. Kumar A, Smeraglio A, Witteles R, et al. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11(9):646-649. PubMed
8. Institute for Healthcare Improvement website. http://www.ihi.org. Accessed December 15, 2015.
1. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs [published correction appears in J Gen Intern Med. 2008;23(11):1931]. J Gen Intern Med. 2008;23(7):1110-1115. PubMed
2. Arora V, Guardiano S, Donaldson D, Storch I, Hemstreet P. Closing the gap between internal medicine training and practice: recommendations from recent graduates. Am J Med. 2005;118(6):680-685. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008;75(5):436-441. PubMed
4. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008;23(7):1122-1123. PubMed
5. Glasheen JJ, Epstein KR, Siegal E, Kutner JS, Prochazka AV. The spectrum of community-based hospitalist practice: a call to tailor internal medicine residency training. Arch Intern Med. 2007;167(7):727-728. PubMed
6. Patel N, Brennan PJ, Metlay J, Bellini L, Shannon RP, Myers JS. Building the pipeline: the creation of a residency training pathway for future physician leaders in health care quality. Acad Med. 2015;90(2):185-190. PubMed
7. Kumar A, Smeraglio A, Witteles R, et al. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11(9):646-649. PubMed
8. Institute for Healthcare Improvement website. http://www.ihi.org. Accessed December 15, 2015.
© 2017 Society of Hospital Medicine
Family report compared to clinician-documented diagnoses for psychiatric conditions among hospitalized children
Psychiatric conditions affect 1 in 5 children,1,2 and having a comorbid psychiatric condition is associated with worse outcomes in children hospitalized for medical or surgical indications.3-7 Although little is known about interventions for improving outcomes for hospitalized children with psychiatric conditions,8 several interventions that integrate medical and psychiatric care are known to improve ambulatory patient outcomes.9-14 The success of initiatives that test whether integrated medical and psychiatric care models can improve pediatric hospital outcomes depends on reliable identification of comorbid psychiatric conditions and family and clinician having a shared understanding of a patient’s psychiatric diagnoses.
Mental health care system fragmentation, stigma, and privacy issues15-20 may contribute to clinical teams and families having disparate views of psychiatric comorbidities. Evidence suggests that hospital clinicians caring for pediatric medical and surgical inpatients are often unaware of a psychiatric condition that has been diagnosed or managed in the ambulatory setting,3,6 even in cases in which the patient and family are aware of the diagnosis. Conversely, for other patients, clinicians may be aware of a psychiatric diagnosis, but patient and family may not share that understanding or reliably report a psychiatric diagnosis.21-23 Although hospitalization may not be the ideal setting for identifying a new psychiatric diagnosis, given the short-term relationship between patient and clinical care team, addressing and managing a psychiatric comorbidity that is known to family or clinician are important elements of patient-centered hospital care.
The success of interventions in improving hospital outcomes for hospitalized children with psychiatric comorbidity depends on patients, families, and clinicians having a shared understanding of which patients have psychiatric conditions, and on accurate estimates of the scope of the population in need of psychiatric care during pediatric hospitalization.
We conducted a study to compare estimates of point prevalence of psychiatric comorbidity identified by family report (FR) or clinician documentation (CD) and to determine the degree of FR–CD agreement regarding the presence of psychiatric comorbidity in hospitalized children.
METHODS
We estimated point prevalence and determined FR–CD agreement regarding diagnosed psychiatric comorbidities in a cross-sectional sample of pediatric medical and surgical hospitalizations at Children’s Hospital of Philadelphia (CHOP). CHOP is a free-standing 535-bed children’s hospital that serves as a community hospital for the city of Philadelphia; a regional referral center for eastern Pennsylvania, Delaware, and southern New Jersey; and a national and international quaternary referral center. This study was approved by CHOP’s institutional review board.
Patients eligible for inclusion in the study were 4 to 21 years old and hospitalized for a medical or surgical indication. Patients were ineligible if they were hospitalized for a primary psychiatric indication, were medically unstable (eg, received end-of-life care or escalating interventions for a life-threatening condition), had significant cognitive impairment precluding communication (eg, history of severe hypoxic-ischemic encephalopathy), or did not speak English (pertains to consenting parent, guardian, or patient).
The cross-sectional patient sample was selected using a point prevalence recruitment strategy. All eligible patients on each of CHOP’s 20 inpatient medical, surgical, and critical care units were approached for study participation on 2 dates between July 2015 and March 2016. To avoid enrolling the same patient multiple times for a single hospitalization, we separated recruitment dates on each unit by at least 3 months. A goal sample size of 100 to 150 patients was selected to provide precision sufficient to achieve a confidence interval (CI) of 10% around an estimate of the point prevalence of any mental health condition.
To obtain family report of prior psychiatric diagnoses, we interviewed patients and/or their parents during the hospitalization. For 18- to 21-year-old patients, the adolescent patient completed the interview. For patients under 18 years old, parents completed the interview, and for 14- to 17-year-old adolescents,either the parent, the patient, or both could complete the interview. Adolescents were asked to complete the interview confidentially without a parent present. The structured interview included questions derived from the National Survey of Children’s Health24 and the Services Assessment for Children and Adolescents22 to report the patient’s active psychiatric conditions. Interviewees reported whether the patient had ever been diagnosed with any psychiatric disorder, whether the condition was ongoing in the year prior to hospitalization, and whether the patient received any mental health services in clinical settings or school in the 12 months prior to hospitalization.
For CD, we identified a psychiatric diagnosis associated with the index hospitalization if a psychiatric diagnosis was noted in the patient’s admission note, discharge summary, or hospital problem list, or if an International Classification of Diseases (ICD) code for a psychiatric diagnosis was submitted for billing for the index hospitalization. The Healthcare Cost and Utilization Project condition classification system was used to sort psychiatric condition codes25-27 into 5 categories: attention-deficit/hyperactivity disorder (ADHD), anxiety disorders, depression, disruptive behavior disorders, and autism spectrum disorders. A residual category of other, less common psychiatric conditions included eating disorders, attachment disorders, and bipolar disorder.
For each condition category, we determined the point prevalence of having a psychiatric diagnosis identified by FR and having a diagnosis identified by CD. We used McNemar tests to compare point prevalence estimates, the Clopper-Pearson method to calculate CIs around the estimates,28 and Cohen κ statistics to estimate FR–CD agreement regarding psychiatric diagnoses, grouping patients by type of psychiatric diagnosis and by clinical and demographic characteristics. All statistical tests were 2-sided, and P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Version 13.1 (StataCorp, College Station, Texas).
RESULTS
Of 640 patients hospitalized on study recruitment dates, 411 were ineligible for the study (282 were <4 or >21 years old, 42 were not English speakers, 37 had cognitive impairment, 30 were not medically stable, and 20 were admitted for a primary psychiatric diagnosis). Of the 229 eligible patients, 119 (52%) enrolled. Included patients were 57% female; 9% Hispanic; and 35% black, 55% white, and 15% other race. Forty-eight percent of the enrollees had Medicaid (48%), and 52% had private health insurance. Mean age was 12.3 years. Of enrolled patients, 38% were admitted to subspecialty medical services. Enrollee demographics were representative of hospital-level demographics for the study-eligible population; there were no significant differences in age, sex, race, ethnicity, payer type, or hospital service admission type between enrollees and patients who declined to participate (all Ps > 0.05). Table 1 lists demographic and clinical characteristics of the complete study sample and of the groups with FR- or CD-identified psychiatric diagnosis.
Of 119 enrollees, 26 (22%; 95% CI, 15%-30%) had at least 1 FR-identified comorbid psychiatric diagnosis, and 30 (25%; 95% CI, 17%-33%) had at least 1 CD-identified diagnosis. In 13 cases, adolescents (age, 14-17 years) and their parents both completed the structured interview; there were no discrepancies between interview results.
In total, 39 of 119 patients (33%, 95% CI: 24-42%) had either a family-reported or clinician-documented psychiatric diagnosis at the time of hospitalization. For 17 of 119 patients (14%; 95% CI: 9-22%), family-report and clinician-documentation both identified the patient as having a comorbid psychiatric diagnosis. For 9 of 119 patients (8%; 95% CI: 4-14%) families reported a psychiatric diagnosis, but clinicians did not document one. Conversely, for another 13 of 119 patients (11%; 95% CI: 6-18%), a clinician documented a psychiatric diagnosis but the family did not report one. The Figure shows the point prevalence of family-reported psychiatric diagnoses and clinician-documented psychiatric diagnoses for 5 common psychiatric condition categories.
The most common psychiatric conditions reported by families or documented by clinicians were ADHD (n=16, 13%),
Although point prevalence estimates were similar for FR- and CD-identified comorbid psychiatric conditions, FR–CD agreement was modest. It was fair for any psychiatric diagnosis (κ = .49; 95% CI, .30-.67), highest for ADHD (κ = .79; 95% CI, .61-.96), and fair or poor for other psychiatric conditions (κ range, .11-.48). Table 3 lists the FR–CD agreement data for psychiatric diagnoses for hospitalized children and adolescents.
We compared the distribution of FR and CD psychiatric diagnoses with FR use of mental health services. Of the 119 patients, 47 (39%; 95% CI, 31%-49%) had used mental health services within the year before hospitalization. Of these 47 patients, 15 (32%; 95% CI, 19%-47%) had a psychiatric diagnosis identified by both FR and CD, 6 (13%; 95% CI, 5%-26%) had a diagnosis identified only by FR, 8 (17%; 95% CI, 8%-30%) had a diagnosis identified only by CD, and 18 (38%; 95% CI, 25%-54%) had no FR- or CD-identified diagnosis. For 5 (38%; 95% CI, 14%-68%) of the 13 patients with a CD-only diagnosis, the family reported no use of mental health services within the year before hospitalization.
DISCUSSION
At a tertiary-care children’s hospital, we found high point prevalence of comorbid psychiatric conditions and low agreement between FR- and CD-identified psychiatric conditions. Estimates of the prevalence of psychiatric comorbidity among pediatric medical and surgical inpatients were similar for FR- and CD-identified psychiatric conditions, though each method missed about one third of the cases identified by the other method. FR only and CD only each identified about 1 in 4 or 5 hospitalized children and adolescents with a psychiatric comorbidity. When FR and CD were combined, a comorbid psychiatric diagnosis was identified in about 1 in 3 medical and surgical inpatients aged 4 to 21 years. FR–CD agreement was substantial only for ADHD and was fair to slight for most other psychiatric conditions, including autism, depression, anxiety, and disruptive behavior disorders (eg, conduct disorder, oppositional defiant disorder).
Our finding that psychiatric conditions were more commonly reported by families and documented by clinicians for white patients is consistent with a large body of evidence showing that racial or ethnic minority patients experience more stigma related to mental health diagnoses and use mental health services less.29-33 Families were more likely to report use of mental health services than a known mental health diagnosis. This finding may reflect families’ willingness to use services even if they do not understand or experience stigma related to psychiatric diagnoses. Alternatively, use of mental health services without a diagnosis may reflect clinicians’ willingness to refer a child for services when the child is perceived to have an impairment even in the absence of a clear psychiatric diagnosis.
The low FR–CD agreement regarding psychiatric conditions in hospitalized children and adolescents raises 3 issues for pediatric hospital care. First, earlier studies likely underestimated the prevalence of these conditions. A 2014 study of a national sample found that 13% of children hospitalized for a physical health condition had psychiatric comorbidity.25 That study and other large-scale studies showing a high and increasing prevalence of primary psychiatric conditions in hospitalized children and adolescents have relied on administrative data derived from clinician-documented diagnoses.25-27 Our study findings suggest that reliance on administrative data could result in underestimation of the prevalence of psychiatric comorbidity in hospitalized children by as much as 40%. Pediatric hospitals are reporting a shortage of pediatric mental health specialists.34 Augmenting estimates of the prevalence of psychiatric comorbidity in hospitalized children with reports from other sources, including families or outpatient administrative records, may aid health systems in allocating mental health resources for pediatric inpatients.
The second issue is that the present data suggest that families and clinicians do not share the same information about a child’s psychiatric diagnoses when the child is hospitalized for a medical condition or surgical procedure. Low FR–CD agreement regarding psychiatric diagnoses suggests families and clinical teams are not always “on the same page” about psychiatric needs during hospitalization. Implications of this finding are relevant to inpatient and ambulatory care settings. In cases in which a clinician recognizes a psychiatric condition but the family does not, the family may not seek outpatient treatment. In the present study, one third of patients with a psychiatric diagnosis identified by CD but not FR were not engaged in ambulatory treatment for the condition. Conversely, a psychiatric diagnosis identified by FR but not CD suggests clinical teams lack the skills and knowledge needed to elicit information about psychiatric conditions and their potential relevance to inpatient care. As a result, clinicians may miss opportunities to provide interventions that may improve physical or mental health outcomes. For example, clinical teams with information about a patient’s anxiety disorder may be better able to provide brief interventions to prevent medical treatments from triggering anxiety symptoms and to mitigate the risk for traumatic stress symptoms related to the hospitalization.
The third issue is that anxiety disorders were most likely to be the subject of FR–CD disagreement. This finding identifies children with anxiety disorders as a priority population for research into differences between families and clinicians in understanding patients’ psychiatric diagnoses. Our findings suggest families and clinicians have different views of patients’ anxiety symptoms. Anxiety disorders are a risk factor for worse outcomes in children with chronic physical conditions,3,35-37 and acute hospitalization is associated with posthospital anxiety symptoms.38,39 Thus, anxiety disorders are particularly relevant to hospital care and are a priority for research on the differences between families’ and clinicians’ perspectives on children’s psychiatric diagnoses.
Our findings should be interpreted in the context of study limitations. First, because of resource limitations, we did not obtain psychiatric diagnostic evaluations or records to confirm FR- and CD-identified psychiatric diagnoses. Although this lack of clinical confirmation could have resulted in misclassification bias, the risk of bias was no higher than in many other studies that have successfully used hospital records21,25 and family reports to identify psychiatric comorbidity.40 Second, because the study included only English-speaking patients and families, results cannot be generalized to non-English-speaking populations. Third, this was a single-center study, conducted in a free-standing tertiary-care children’s hospital. Sample size was small, particularly for estimating the prevalence of individual psychiatric conditions. Patient characteristics and clinical practice patterns may differ at other types of hospitals. Larger multicenter studies are warranted. Despite these limitations, our results provide important new information that can further our understanding of the epidemiology of psychiatric conditions in hospitalized children. This information should interest clinical teams caring for children with comorbid physical and mental health conditions.
CONCLUSIONS
Low FR–CD agreement regarding hospitalized children’s psychiatric comorbidities suggests that patients and their families and clinicians do not always share the same information about these comorbidities, and that the prevalence of psychiatric comorbidity in hospitalized children is likely underestimated. To allocate adequate resources for these children, health systems may need to obtain information from multiple sources. Furthermore, we need to better our understanding of strategies for communicating about hospitalized children’s psychiatric conditions so that we can develop interventions to improve hospital outcomes for this vulnerable population.
Disclosures
The direct costs of this project were funded by an internal pilot grant from the Center for Pediatric Health Disparities at Children’s Hospital of Philadelphia. Dr. Doupnik was supported by Ruth L. Kirschstein National Research Service Award T32-HP010026-11, funded by the National Institutes of Health. The sponsors had no role in study design; collection, analysis, or interpretation of data; manuscript writing; or deciding to submit this article for publication.
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32. Flores G; Committee on Pediatric Research. Technical report—racial and ethnic disparities in the health and health care of children. Pediatrics. 2010;125(4):e979-e1020. PubMed
33. Turner EA, Jensen-Doss A, Heffer RW. Ethnicity as a moderator of how parents’ attitudes and perceived stigma influence intentions to seek child mental health services. Cultur Divers Ethnic Minor Psychol. 2015;21(4):613-618. PubMed
34. Shaw RJ, Wamboldt M, Bursch B, Stuber M. Practice patterns in pediatric consultation-liaison psychiatry: a national survey. Psychosomatics. 2006;47(1):43-49. PubMed
35. Benton TD, Ifeagwu JA, Smith-Whitley K. Anxiety and depression in children and adolescents with sickle cell disease. Curr Psychiatry Rep. 2007;9(2):114-121. PubMed
36. Chavira DA, Garland AF, Daley S, Hough R. The impact of medical comorbidity on mental health and functional health outcomes among children with anxiety disorders. J Dev Behav Pediatr. 2008;29(5):394-402. PubMed
37. Knight A, Weiss P, Morales K, et al. Depression and anxiety and their association with healthcare utilization in pediatric lupus and mixed connective tissue disease patients: a cross-sectional study. Pediatr Rheumatol Online J. 2014;12:42. PubMed
38. Marsac ML, Kassam-Adams N, Delahanty DL, F. Widaman K, Barakat LP. Posttraumatic stress following acute medical trauma in children: a proposed model of bio-psycho-social processes during the peri-trauma period. Clin Child Fam Psychol Rev. 2014;17(4):399-411. PubMed
39. Marsac ML, Hildenbrand AK, Kohser KL, Winston FK, Li Y, Kassam-Adams N. Preventing posttraumatic stress following pediatric injury: a randomized controlled trial of a web-based psycho-educational intervention for parents. J Pediatr Psychol. 2013;38(10):1101-1111. PubMed
40. Petersen MC, Kube DA, Whitaker TM, Graff JC, Palmer FB. Prevalence of developmental and behavioral disorders in a pediatric hospital. Pediatrics. 2009;123(3):e490-e495. PubMed
Psychiatric conditions affect 1 in 5 children,1,2 and having a comorbid psychiatric condition is associated with worse outcomes in children hospitalized for medical or surgical indications.3-7 Although little is known about interventions for improving outcomes for hospitalized children with psychiatric conditions,8 several interventions that integrate medical and psychiatric care are known to improve ambulatory patient outcomes.9-14 The success of initiatives that test whether integrated medical and psychiatric care models can improve pediatric hospital outcomes depends on reliable identification of comorbid psychiatric conditions and family and clinician having a shared understanding of a patient’s psychiatric diagnoses.
Mental health care system fragmentation, stigma, and privacy issues15-20 may contribute to clinical teams and families having disparate views of psychiatric comorbidities. Evidence suggests that hospital clinicians caring for pediatric medical and surgical inpatients are often unaware of a psychiatric condition that has been diagnosed or managed in the ambulatory setting,3,6 even in cases in which the patient and family are aware of the diagnosis. Conversely, for other patients, clinicians may be aware of a psychiatric diagnosis, but patient and family may not share that understanding or reliably report a psychiatric diagnosis.21-23 Although hospitalization may not be the ideal setting for identifying a new psychiatric diagnosis, given the short-term relationship between patient and clinical care team, addressing and managing a psychiatric comorbidity that is known to family or clinician are important elements of patient-centered hospital care.
The success of interventions in improving hospital outcomes for hospitalized children with psychiatric comorbidity depends on patients, families, and clinicians having a shared understanding of which patients have psychiatric conditions, and on accurate estimates of the scope of the population in need of psychiatric care during pediatric hospitalization.
We conducted a study to compare estimates of point prevalence of psychiatric comorbidity identified by family report (FR) or clinician documentation (CD) and to determine the degree of FR–CD agreement regarding the presence of psychiatric comorbidity in hospitalized children.
METHODS
We estimated point prevalence and determined FR–CD agreement regarding diagnosed psychiatric comorbidities in a cross-sectional sample of pediatric medical and surgical hospitalizations at Children’s Hospital of Philadelphia (CHOP). CHOP is a free-standing 535-bed children’s hospital that serves as a community hospital for the city of Philadelphia; a regional referral center for eastern Pennsylvania, Delaware, and southern New Jersey; and a national and international quaternary referral center. This study was approved by CHOP’s institutional review board.
Patients eligible for inclusion in the study were 4 to 21 years old and hospitalized for a medical or surgical indication. Patients were ineligible if they were hospitalized for a primary psychiatric indication, were medically unstable (eg, received end-of-life care or escalating interventions for a life-threatening condition), had significant cognitive impairment precluding communication (eg, history of severe hypoxic-ischemic encephalopathy), or did not speak English (pertains to consenting parent, guardian, or patient).
The cross-sectional patient sample was selected using a point prevalence recruitment strategy. All eligible patients on each of CHOP’s 20 inpatient medical, surgical, and critical care units were approached for study participation on 2 dates between July 2015 and March 2016. To avoid enrolling the same patient multiple times for a single hospitalization, we separated recruitment dates on each unit by at least 3 months. A goal sample size of 100 to 150 patients was selected to provide precision sufficient to achieve a confidence interval (CI) of 10% around an estimate of the point prevalence of any mental health condition.
To obtain family report of prior psychiatric diagnoses, we interviewed patients and/or their parents during the hospitalization. For 18- to 21-year-old patients, the adolescent patient completed the interview. For patients under 18 years old, parents completed the interview, and for 14- to 17-year-old adolescents,either the parent, the patient, or both could complete the interview. Adolescents were asked to complete the interview confidentially without a parent present. The structured interview included questions derived from the National Survey of Children’s Health24 and the Services Assessment for Children and Adolescents22 to report the patient’s active psychiatric conditions. Interviewees reported whether the patient had ever been diagnosed with any psychiatric disorder, whether the condition was ongoing in the year prior to hospitalization, and whether the patient received any mental health services in clinical settings or school in the 12 months prior to hospitalization.
For CD, we identified a psychiatric diagnosis associated with the index hospitalization if a psychiatric diagnosis was noted in the patient’s admission note, discharge summary, or hospital problem list, or if an International Classification of Diseases (ICD) code for a psychiatric diagnosis was submitted for billing for the index hospitalization. The Healthcare Cost and Utilization Project condition classification system was used to sort psychiatric condition codes25-27 into 5 categories: attention-deficit/hyperactivity disorder (ADHD), anxiety disorders, depression, disruptive behavior disorders, and autism spectrum disorders. A residual category of other, less common psychiatric conditions included eating disorders, attachment disorders, and bipolar disorder.
For each condition category, we determined the point prevalence of having a psychiatric diagnosis identified by FR and having a diagnosis identified by CD. We used McNemar tests to compare point prevalence estimates, the Clopper-Pearson method to calculate CIs around the estimates,28 and Cohen κ statistics to estimate FR–CD agreement regarding psychiatric diagnoses, grouping patients by type of psychiatric diagnosis and by clinical and demographic characteristics. All statistical tests were 2-sided, and P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Version 13.1 (StataCorp, College Station, Texas).
RESULTS
Of 640 patients hospitalized on study recruitment dates, 411 were ineligible for the study (282 were <4 or >21 years old, 42 were not English speakers, 37 had cognitive impairment, 30 were not medically stable, and 20 were admitted for a primary psychiatric diagnosis). Of the 229 eligible patients, 119 (52%) enrolled. Included patients were 57% female; 9% Hispanic; and 35% black, 55% white, and 15% other race. Forty-eight percent of the enrollees had Medicaid (48%), and 52% had private health insurance. Mean age was 12.3 years. Of enrolled patients, 38% were admitted to subspecialty medical services. Enrollee demographics were representative of hospital-level demographics for the study-eligible population; there were no significant differences in age, sex, race, ethnicity, payer type, or hospital service admission type between enrollees and patients who declined to participate (all Ps > 0.05). Table 1 lists demographic and clinical characteristics of the complete study sample and of the groups with FR- or CD-identified psychiatric diagnosis.
Of 119 enrollees, 26 (22%; 95% CI, 15%-30%) had at least 1 FR-identified comorbid psychiatric diagnosis, and 30 (25%; 95% CI, 17%-33%) had at least 1 CD-identified diagnosis. In 13 cases, adolescents (age, 14-17 years) and their parents both completed the structured interview; there were no discrepancies between interview results.
In total, 39 of 119 patients (33%, 95% CI: 24-42%) had either a family-reported or clinician-documented psychiatric diagnosis at the time of hospitalization. For 17 of 119 patients (14%; 95% CI: 9-22%), family-report and clinician-documentation both identified the patient as having a comorbid psychiatric diagnosis. For 9 of 119 patients (8%; 95% CI: 4-14%) families reported a psychiatric diagnosis, but clinicians did not document one. Conversely, for another 13 of 119 patients (11%; 95% CI: 6-18%), a clinician documented a psychiatric diagnosis but the family did not report one. The Figure shows the point prevalence of family-reported psychiatric diagnoses and clinician-documented psychiatric diagnoses for 5 common psychiatric condition categories.
The most common psychiatric conditions reported by families or documented by clinicians were ADHD (n=16, 13%),
Although point prevalence estimates were similar for FR- and CD-identified comorbid psychiatric conditions, FR–CD agreement was modest. It was fair for any psychiatric diagnosis (κ = .49; 95% CI, .30-.67), highest for ADHD (κ = .79; 95% CI, .61-.96), and fair or poor for other psychiatric conditions (κ range, .11-.48). Table 3 lists the FR–CD agreement data for psychiatric diagnoses for hospitalized children and adolescents.
We compared the distribution of FR and CD psychiatric diagnoses with FR use of mental health services. Of the 119 patients, 47 (39%; 95% CI, 31%-49%) had used mental health services within the year before hospitalization. Of these 47 patients, 15 (32%; 95% CI, 19%-47%) had a psychiatric diagnosis identified by both FR and CD, 6 (13%; 95% CI, 5%-26%) had a diagnosis identified only by FR, 8 (17%; 95% CI, 8%-30%) had a diagnosis identified only by CD, and 18 (38%; 95% CI, 25%-54%) had no FR- or CD-identified diagnosis. For 5 (38%; 95% CI, 14%-68%) of the 13 patients with a CD-only diagnosis, the family reported no use of mental health services within the year before hospitalization.
DISCUSSION
At a tertiary-care children’s hospital, we found high point prevalence of comorbid psychiatric conditions and low agreement between FR- and CD-identified psychiatric conditions. Estimates of the prevalence of psychiatric comorbidity among pediatric medical and surgical inpatients were similar for FR- and CD-identified psychiatric conditions, though each method missed about one third of the cases identified by the other method. FR only and CD only each identified about 1 in 4 or 5 hospitalized children and adolescents with a psychiatric comorbidity. When FR and CD were combined, a comorbid psychiatric diagnosis was identified in about 1 in 3 medical and surgical inpatients aged 4 to 21 years. FR–CD agreement was substantial only for ADHD and was fair to slight for most other psychiatric conditions, including autism, depression, anxiety, and disruptive behavior disorders (eg, conduct disorder, oppositional defiant disorder).
Our finding that psychiatric conditions were more commonly reported by families and documented by clinicians for white patients is consistent with a large body of evidence showing that racial or ethnic minority patients experience more stigma related to mental health diagnoses and use mental health services less.29-33 Families were more likely to report use of mental health services than a known mental health diagnosis. This finding may reflect families’ willingness to use services even if they do not understand or experience stigma related to psychiatric diagnoses. Alternatively, use of mental health services without a diagnosis may reflect clinicians’ willingness to refer a child for services when the child is perceived to have an impairment even in the absence of a clear psychiatric diagnosis.
The low FR–CD agreement regarding psychiatric conditions in hospitalized children and adolescents raises 3 issues for pediatric hospital care. First, earlier studies likely underestimated the prevalence of these conditions. A 2014 study of a national sample found that 13% of children hospitalized for a physical health condition had psychiatric comorbidity.25 That study and other large-scale studies showing a high and increasing prevalence of primary psychiatric conditions in hospitalized children and adolescents have relied on administrative data derived from clinician-documented diagnoses.25-27 Our study findings suggest that reliance on administrative data could result in underestimation of the prevalence of psychiatric comorbidity in hospitalized children by as much as 40%. Pediatric hospitals are reporting a shortage of pediatric mental health specialists.34 Augmenting estimates of the prevalence of psychiatric comorbidity in hospitalized children with reports from other sources, including families or outpatient administrative records, may aid health systems in allocating mental health resources for pediatric inpatients.
The second issue is that the present data suggest that families and clinicians do not share the same information about a child’s psychiatric diagnoses when the child is hospitalized for a medical condition or surgical procedure. Low FR–CD agreement regarding psychiatric diagnoses suggests families and clinical teams are not always “on the same page” about psychiatric needs during hospitalization. Implications of this finding are relevant to inpatient and ambulatory care settings. In cases in which a clinician recognizes a psychiatric condition but the family does not, the family may not seek outpatient treatment. In the present study, one third of patients with a psychiatric diagnosis identified by CD but not FR were not engaged in ambulatory treatment for the condition. Conversely, a psychiatric diagnosis identified by FR but not CD suggests clinical teams lack the skills and knowledge needed to elicit information about psychiatric conditions and their potential relevance to inpatient care. As a result, clinicians may miss opportunities to provide interventions that may improve physical or mental health outcomes. For example, clinical teams with information about a patient’s anxiety disorder may be better able to provide brief interventions to prevent medical treatments from triggering anxiety symptoms and to mitigate the risk for traumatic stress symptoms related to the hospitalization.
The third issue is that anxiety disorders were most likely to be the subject of FR–CD disagreement. This finding identifies children with anxiety disorders as a priority population for research into differences between families and clinicians in understanding patients’ psychiatric diagnoses. Our findings suggest families and clinicians have different views of patients’ anxiety symptoms. Anxiety disorders are a risk factor for worse outcomes in children with chronic physical conditions,3,35-37 and acute hospitalization is associated with posthospital anxiety symptoms.38,39 Thus, anxiety disorders are particularly relevant to hospital care and are a priority for research on the differences between families’ and clinicians’ perspectives on children’s psychiatric diagnoses.
Our findings should be interpreted in the context of study limitations. First, because of resource limitations, we did not obtain psychiatric diagnostic evaluations or records to confirm FR- and CD-identified psychiatric diagnoses. Although this lack of clinical confirmation could have resulted in misclassification bias, the risk of bias was no higher than in many other studies that have successfully used hospital records21,25 and family reports to identify psychiatric comorbidity.40 Second, because the study included only English-speaking patients and families, results cannot be generalized to non-English-speaking populations. Third, this was a single-center study, conducted in a free-standing tertiary-care children’s hospital. Sample size was small, particularly for estimating the prevalence of individual psychiatric conditions. Patient characteristics and clinical practice patterns may differ at other types of hospitals. Larger multicenter studies are warranted. Despite these limitations, our results provide important new information that can further our understanding of the epidemiology of psychiatric conditions in hospitalized children. This information should interest clinical teams caring for children with comorbid physical and mental health conditions.
CONCLUSIONS
Low FR–CD agreement regarding hospitalized children’s psychiatric comorbidities suggests that patients and their families and clinicians do not always share the same information about these comorbidities, and that the prevalence of psychiatric comorbidity in hospitalized children is likely underestimated. To allocate adequate resources for these children, health systems may need to obtain information from multiple sources. Furthermore, we need to better our understanding of strategies for communicating about hospitalized children’s psychiatric conditions so that we can develop interventions to improve hospital outcomes for this vulnerable population.
Disclosures
The direct costs of this project were funded by an internal pilot grant from the Center for Pediatric Health Disparities at Children’s Hospital of Philadelphia. Dr. Doupnik was supported by Ruth L. Kirschstein National Research Service Award T32-HP010026-11, funded by the National Institutes of Health. The sponsors had no role in study design; collection, analysis, or interpretation of data; manuscript writing; or deciding to submit this article for publication.
Psychiatric conditions affect 1 in 5 children,1,2 and having a comorbid psychiatric condition is associated with worse outcomes in children hospitalized for medical or surgical indications.3-7 Although little is known about interventions for improving outcomes for hospitalized children with psychiatric conditions,8 several interventions that integrate medical and psychiatric care are known to improve ambulatory patient outcomes.9-14 The success of initiatives that test whether integrated medical and psychiatric care models can improve pediatric hospital outcomes depends on reliable identification of comorbid psychiatric conditions and family and clinician having a shared understanding of a patient’s psychiatric diagnoses.
Mental health care system fragmentation, stigma, and privacy issues15-20 may contribute to clinical teams and families having disparate views of psychiatric comorbidities. Evidence suggests that hospital clinicians caring for pediatric medical and surgical inpatients are often unaware of a psychiatric condition that has been diagnosed or managed in the ambulatory setting,3,6 even in cases in which the patient and family are aware of the diagnosis. Conversely, for other patients, clinicians may be aware of a psychiatric diagnosis, but patient and family may not share that understanding or reliably report a psychiatric diagnosis.21-23 Although hospitalization may not be the ideal setting for identifying a new psychiatric diagnosis, given the short-term relationship between patient and clinical care team, addressing and managing a psychiatric comorbidity that is known to family or clinician are important elements of patient-centered hospital care.
The success of interventions in improving hospital outcomes for hospitalized children with psychiatric comorbidity depends on patients, families, and clinicians having a shared understanding of which patients have psychiatric conditions, and on accurate estimates of the scope of the population in need of psychiatric care during pediatric hospitalization.
We conducted a study to compare estimates of point prevalence of psychiatric comorbidity identified by family report (FR) or clinician documentation (CD) and to determine the degree of FR–CD agreement regarding the presence of psychiatric comorbidity in hospitalized children.
METHODS
We estimated point prevalence and determined FR–CD agreement regarding diagnosed psychiatric comorbidities in a cross-sectional sample of pediatric medical and surgical hospitalizations at Children’s Hospital of Philadelphia (CHOP). CHOP is a free-standing 535-bed children’s hospital that serves as a community hospital for the city of Philadelphia; a regional referral center for eastern Pennsylvania, Delaware, and southern New Jersey; and a national and international quaternary referral center. This study was approved by CHOP’s institutional review board.
Patients eligible for inclusion in the study were 4 to 21 years old and hospitalized for a medical or surgical indication. Patients were ineligible if they were hospitalized for a primary psychiatric indication, were medically unstable (eg, received end-of-life care or escalating interventions for a life-threatening condition), had significant cognitive impairment precluding communication (eg, history of severe hypoxic-ischemic encephalopathy), or did not speak English (pertains to consenting parent, guardian, or patient).
The cross-sectional patient sample was selected using a point prevalence recruitment strategy. All eligible patients on each of CHOP’s 20 inpatient medical, surgical, and critical care units were approached for study participation on 2 dates between July 2015 and March 2016. To avoid enrolling the same patient multiple times for a single hospitalization, we separated recruitment dates on each unit by at least 3 months. A goal sample size of 100 to 150 patients was selected to provide precision sufficient to achieve a confidence interval (CI) of 10% around an estimate of the point prevalence of any mental health condition.
To obtain family report of prior psychiatric diagnoses, we interviewed patients and/or their parents during the hospitalization. For 18- to 21-year-old patients, the adolescent patient completed the interview. For patients under 18 years old, parents completed the interview, and for 14- to 17-year-old adolescents,either the parent, the patient, or both could complete the interview. Adolescents were asked to complete the interview confidentially without a parent present. The structured interview included questions derived from the National Survey of Children’s Health24 and the Services Assessment for Children and Adolescents22 to report the patient’s active psychiatric conditions. Interviewees reported whether the patient had ever been diagnosed with any psychiatric disorder, whether the condition was ongoing in the year prior to hospitalization, and whether the patient received any mental health services in clinical settings or school in the 12 months prior to hospitalization.
For CD, we identified a psychiatric diagnosis associated with the index hospitalization if a psychiatric diagnosis was noted in the patient’s admission note, discharge summary, or hospital problem list, or if an International Classification of Diseases (ICD) code for a psychiatric diagnosis was submitted for billing for the index hospitalization. The Healthcare Cost and Utilization Project condition classification system was used to sort psychiatric condition codes25-27 into 5 categories: attention-deficit/hyperactivity disorder (ADHD), anxiety disorders, depression, disruptive behavior disorders, and autism spectrum disorders. A residual category of other, less common psychiatric conditions included eating disorders, attachment disorders, and bipolar disorder.
For each condition category, we determined the point prevalence of having a psychiatric diagnosis identified by FR and having a diagnosis identified by CD. We used McNemar tests to compare point prevalence estimates, the Clopper-Pearson method to calculate CIs around the estimates,28 and Cohen κ statistics to estimate FR–CD agreement regarding psychiatric diagnoses, grouping patients by type of psychiatric diagnosis and by clinical and demographic characteristics. All statistical tests were 2-sided, and P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Version 13.1 (StataCorp, College Station, Texas).
RESULTS
Of 640 patients hospitalized on study recruitment dates, 411 were ineligible for the study (282 were <4 or >21 years old, 42 were not English speakers, 37 had cognitive impairment, 30 were not medically stable, and 20 were admitted for a primary psychiatric diagnosis). Of the 229 eligible patients, 119 (52%) enrolled. Included patients were 57% female; 9% Hispanic; and 35% black, 55% white, and 15% other race. Forty-eight percent of the enrollees had Medicaid (48%), and 52% had private health insurance. Mean age was 12.3 years. Of enrolled patients, 38% were admitted to subspecialty medical services. Enrollee demographics were representative of hospital-level demographics for the study-eligible population; there were no significant differences in age, sex, race, ethnicity, payer type, or hospital service admission type between enrollees and patients who declined to participate (all Ps > 0.05). Table 1 lists demographic and clinical characteristics of the complete study sample and of the groups with FR- or CD-identified psychiatric diagnosis.
Of 119 enrollees, 26 (22%; 95% CI, 15%-30%) had at least 1 FR-identified comorbid psychiatric diagnosis, and 30 (25%; 95% CI, 17%-33%) had at least 1 CD-identified diagnosis. In 13 cases, adolescents (age, 14-17 years) and their parents both completed the structured interview; there were no discrepancies between interview results.
In total, 39 of 119 patients (33%, 95% CI: 24-42%) had either a family-reported or clinician-documented psychiatric diagnosis at the time of hospitalization. For 17 of 119 patients (14%; 95% CI: 9-22%), family-report and clinician-documentation both identified the patient as having a comorbid psychiatric diagnosis. For 9 of 119 patients (8%; 95% CI: 4-14%) families reported a psychiatric diagnosis, but clinicians did not document one. Conversely, for another 13 of 119 patients (11%; 95% CI: 6-18%), a clinician documented a psychiatric diagnosis but the family did not report one. The Figure shows the point prevalence of family-reported psychiatric diagnoses and clinician-documented psychiatric diagnoses for 5 common psychiatric condition categories.
The most common psychiatric conditions reported by families or documented by clinicians were ADHD (n=16, 13%),
Although point prevalence estimates were similar for FR- and CD-identified comorbid psychiatric conditions, FR–CD agreement was modest. It was fair for any psychiatric diagnosis (κ = .49; 95% CI, .30-.67), highest for ADHD (κ = .79; 95% CI, .61-.96), and fair or poor for other psychiatric conditions (κ range, .11-.48). Table 3 lists the FR–CD agreement data for psychiatric diagnoses for hospitalized children and adolescents.
We compared the distribution of FR and CD psychiatric diagnoses with FR use of mental health services. Of the 119 patients, 47 (39%; 95% CI, 31%-49%) had used mental health services within the year before hospitalization. Of these 47 patients, 15 (32%; 95% CI, 19%-47%) had a psychiatric diagnosis identified by both FR and CD, 6 (13%; 95% CI, 5%-26%) had a diagnosis identified only by FR, 8 (17%; 95% CI, 8%-30%) had a diagnosis identified only by CD, and 18 (38%; 95% CI, 25%-54%) had no FR- or CD-identified diagnosis. For 5 (38%; 95% CI, 14%-68%) of the 13 patients with a CD-only diagnosis, the family reported no use of mental health services within the year before hospitalization.
DISCUSSION
At a tertiary-care children’s hospital, we found high point prevalence of comorbid psychiatric conditions and low agreement between FR- and CD-identified psychiatric conditions. Estimates of the prevalence of psychiatric comorbidity among pediatric medical and surgical inpatients were similar for FR- and CD-identified psychiatric conditions, though each method missed about one third of the cases identified by the other method. FR only and CD only each identified about 1 in 4 or 5 hospitalized children and adolescents with a psychiatric comorbidity. When FR and CD were combined, a comorbid psychiatric diagnosis was identified in about 1 in 3 medical and surgical inpatients aged 4 to 21 years. FR–CD agreement was substantial only for ADHD and was fair to slight for most other psychiatric conditions, including autism, depression, anxiety, and disruptive behavior disorders (eg, conduct disorder, oppositional defiant disorder).
Our finding that psychiatric conditions were more commonly reported by families and documented by clinicians for white patients is consistent with a large body of evidence showing that racial or ethnic minority patients experience more stigma related to mental health diagnoses and use mental health services less.29-33 Families were more likely to report use of mental health services than a known mental health diagnosis. This finding may reflect families’ willingness to use services even if they do not understand or experience stigma related to psychiatric diagnoses. Alternatively, use of mental health services without a diagnosis may reflect clinicians’ willingness to refer a child for services when the child is perceived to have an impairment even in the absence of a clear psychiatric diagnosis.
The low FR–CD agreement regarding psychiatric conditions in hospitalized children and adolescents raises 3 issues for pediatric hospital care. First, earlier studies likely underestimated the prevalence of these conditions. A 2014 study of a national sample found that 13% of children hospitalized for a physical health condition had psychiatric comorbidity.25 That study and other large-scale studies showing a high and increasing prevalence of primary psychiatric conditions in hospitalized children and adolescents have relied on administrative data derived from clinician-documented diagnoses.25-27 Our study findings suggest that reliance on administrative data could result in underestimation of the prevalence of psychiatric comorbidity in hospitalized children by as much as 40%. Pediatric hospitals are reporting a shortage of pediatric mental health specialists.34 Augmenting estimates of the prevalence of psychiatric comorbidity in hospitalized children with reports from other sources, including families or outpatient administrative records, may aid health systems in allocating mental health resources for pediatric inpatients.
The second issue is that the present data suggest that families and clinicians do not share the same information about a child’s psychiatric diagnoses when the child is hospitalized for a medical condition or surgical procedure. Low FR–CD agreement regarding psychiatric diagnoses suggests families and clinical teams are not always “on the same page” about psychiatric needs during hospitalization. Implications of this finding are relevant to inpatient and ambulatory care settings. In cases in which a clinician recognizes a psychiatric condition but the family does not, the family may not seek outpatient treatment. In the present study, one third of patients with a psychiatric diagnosis identified by CD but not FR were not engaged in ambulatory treatment for the condition. Conversely, a psychiatric diagnosis identified by FR but not CD suggests clinical teams lack the skills and knowledge needed to elicit information about psychiatric conditions and their potential relevance to inpatient care. As a result, clinicians may miss opportunities to provide interventions that may improve physical or mental health outcomes. For example, clinical teams with information about a patient’s anxiety disorder may be better able to provide brief interventions to prevent medical treatments from triggering anxiety symptoms and to mitigate the risk for traumatic stress symptoms related to the hospitalization.
The third issue is that anxiety disorders were most likely to be the subject of FR–CD disagreement. This finding identifies children with anxiety disorders as a priority population for research into differences between families and clinicians in understanding patients’ psychiatric diagnoses. Our findings suggest families and clinicians have different views of patients’ anxiety symptoms. Anxiety disorders are a risk factor for worse outcomes in children with chronic physical conditions,3,35-37 and acute hospitalization is associated with posthospital anxiety symptoms.38,39 Thus, anxiety disorders are particularly relevant to hospital care and are a priority for research on the differences between families’ and clinicians’ perspectives on children’s psychiatric diagnoses.
Our findings should be interpreted in the context of study limitations. First, because of resource limitations, we did not obtain psychiatric diagnostic evaluations or records to confirm FR- and CD-identified psychiatric diagnoses. Although this lack of clinical confirmation could have resulted in misclassification bias, the risk of bias was no higher than in many other studies that have successfully used hospital records21,25 and family reports to identify psychiatric comorbidity.40 Second, because the study included only English-speaking patients and families, results cannot be generalized to non-English-speaking populations. Third, this was a single-center study, conducted in a free-standing tertiary-care children’s hospital. Sample size was small, particularly for estimating the prevalence of individual psychiatric conditions. Patient characteristics and clinical practice patterns may differ at other types of hospitals. Larger multicenter studies are warranted. Despite these limitations, our results provide important new information that can further our understanding of the epidemiology of psychiatric conditions in hospitalized children. This information should interest clinical teams caring for children with comorbid physical and mental health conditions.
CONCLUSIONS
Low FR–CD agreement regarding hospitalized children’s psychiatric comorbidities suggests that patients and their families and clinicians do not always share the same information about these comorbidities, and that the prevalence of psychiatric comorbidity in hospitalized children is likely underestimated. To allocate adequate resources for these children, health systems may need to obtain information from multiple sources. Furthermore, we need to better our understanding of strategies for communicating about hospitalized children’s psychiatric conditions so that we can develop interventions to improve hospital outcomes for this vulnerable population.
Disclosures
The direct costs of this project were funded by an internal pilot grant from the Center for Pediatric Health Disparities at Children’s Hospital of Philadelphia. Dr. Doupnik was supported by Ruth L. Kirschstein National Research Service Award T32-HP010026-11, funded by the National Institutes of Health. The sponsors had no role in study design; collection, analysis, or interpretation of data; manuscript writing; or deciding to submit this article for publication.
1. Perou R, Bitsko RH, Blumberg SJ, et al; Centers for Disease Control and Prevention (CDC). Mental health surveillance among children—United States, 2005-2011. MMWR Suppl. 2013;62(2):1-35. PubMed
2. Merikangas KR, Nakamura EF, Kessler RC. Epidemiology of mental disorders in children and adolescents. Dialogues Clin Neurosci. 2009;11(1):7-20. PubMed
3. Doupnik SK, Mitra N, Feudtner C, Marcus SC. The influence of comorbid mood and anxiety disorders on outcomes of pediatric patients hospitalized for pneumonia. Hosp Pediatr. 2016;6(3):135-142. PubMed
4. Snell C, Fernandes S, Bujoreanu IS, Garcia G. Depression, illness severity, and healthcare utilization in cystic fibrosis. Pediatr Pulmonol. 2014;49(12):1177-1181. PubMed
5. Garrison MM, Katon WJ, Richardson LP. The impact of psychiatric comorbidities on readmissions for diabetes in youth. Diabetes Care. 2005;28(9):2150-2154. PubMed
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7. Myrvik MP, Burks LM, Hoffman RG, Dasgupta M, Panepinto JA. Mental health disorders influence admission rates for pain in children with sickle cell disease. Pediatr Blood Cancer. 2013;60(7):1211-1214. PubMed
8. Bujoreanu S, White MT, Gerber B, Ibeziako P. Effect of timing of psychiatry consultation on length of pediatric hospitalization and hospital charges. Hosp Pediatr. 2015;5(5):269-275. PubMed
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14. Huffman JC, Mastromauro CA, Beach SR, et al. Collaborative care for depression and anxiety disorders in patients with recent cardiac events: the Management of Sadness and Anxiety in Cardiology (MOSAIC) randomized clinical trial. JAMA Intern Med. 2014;174(6):927-935. PubMed
15. Henderson C, Noblett J, Parke H, et al. Mental health-related stigma in health care and mental health-care settings. Lancet Psychiatry. 2014;1(6):467-482. PubMed
16. Pescosolido BA, Perry BL, Martin JK, McLeod JD, Jensen PS. Stigmatizing attitudes and beliefs about treatment and psychiatric medications for children with mental illness. Psychiatr Serv. 2007;58(5):613-618. PubMed
17. Pescosolido BA. Culture, children, and mental health treatment: special section on the National Stigma Study–Children. Psychiatr Serv. 2007;58(5):611-612. PubMed
18. Britt TW, Greene-Shortridge TM, Brink S, et al. Perceived stigma and barriers to care for psychological treatment: implications for reactions to stressors in different contexts. J Soc Clin Psychol. 2008;27(4):317-335.
19. Thornicroft G, Mehta N, Clement S, et al. Evidence for effective interventions to reduce mental-health-related stigma and discrimination. Lancet. 2016;387(10023):1123-1132. PubMed
20. Health Insurance Portability and Accountability Act of 1996. Washington, DC: US Government Publishing Office; 1996. https://www.gpo.gov/fdsys/pkg/CRPT-104hrpt736/pdf/CRPT-104hrpt736.pdf. Accessed January 21, 2017.
21. Frayne SM, Miller DR, Sharkansky EJ, et al. Using administrative data to identify mental illness: what approach is best? Am J Med Qual. 2010;25(1):42-50. PubMed
22. Horwitz SM, Hoagwood K, Stiffman AR, et al. Reliability of the Services Assessment for Children and Adolescents. Psychiatr Serv. 2001;52(8):1088-1094. PubMed
23. Ascher BH, Farmer EM, Burns BJ, Angold A. The Child and Adolescent Services Assessment (CASA): description and psychometrics. J Emot Behav Disord. 1996;4(1):12-20.
24. Data Resource Center for Child & Adolescent Health, Child and Adolescent Health Measurement Initiative, Oregon Health & Science University. National Survey of Children’s Health (NSCH), 2011/12. http://childhealthdata.org/docs/drc/2011-12-guide-to-topics-questions-draft.pdf?sfvrsn=4. Accessed January 21, 2017.
25. Bardach NS, Coker TR, Zima BT, et al. Common and costly hospitalizations for pediatric mental health disorders. Pediatrics. 2014;133(4):602-609. PubMed
26. Torio CM, Encinosa W, Berdahl T, McCormick MC, Simpson LA. Annual report on health care for children and youth in the United States: national estimates of cost, utilization and expenditures for children with mental health conditions. Acad Pediatr. 2015;15(1):19-35. PubMed
27. Zima BT, Rodean J, Hall M, Bardach NS, Coker TR, Berry JG. Ten year national trends in pediatric hospitalizations by psychiatric complexity. Abstract presented at: 62nd Annual Meeting of the American Academy of Child and Adolescent Psychiatry; October 2015; San Antonio, TX.
28. Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404-413.
29. Wu CH, Erickson SR, Piette JD, Balkrishnan R. Mental health resource utilization and health care costs associated with race and comorbid anxiety among Medicaid enrollees with major depressive disorder. J Nat Med Assoc. 2012;104(1-2):78-88. PubMed
30. Mapelli E, Black T, Doan Q. Trends in pediatric emergency department utilization for mental health-related visits. J Pediatr. 2015;167(4):905-910. PubMed
31. Berdahl T, Owens PL, Dougherty D, McCormick MC, Pylypchuk Y, Simpson LA. Annual report on health care for children and youth in the United States: racial/ethnic and socioeconomic disparities in children’s health care quality. Acad Pediatr. 2010;10(2):95-118. PubMed
32. Flores G; Committee on Pediatric Research. Technical report—racial and ethnic disparities in the health and health care of children. Pediatrics. 2010;125(4):e979-e1020. PubMed
33. Turner EA, Jensen-Doss A, Heffer RW. Ethnicity as a moderator of how parents’ attitudes and perceived stigma influence intentions to seek child mental health services. Cultur Divers Ethnic Minor Psychol. 2015;21(4):613-618. PubMed
34. Shaw RJ, Wamboldt M, Bursch B, Stuber M. Practice patterns in pediatric consultation-liaison psychiatry: a national survey. Psychosomatics. 2006;47(1):43-49. PubMed
35. Benton TD, Ifeagwu JA, Smith-Whitley K. Anxiety and depression in children and adolescents with sickle cell disease. Curr Psychiatry Rep. 2007;9(2):114-121. PubMed
36. Chavira DA, Garland AF, Daley S, Hough R. The impact of medical comorbidity on mental health and functional health outcomes among children with anxiety disorders. J Dev Behav Pediatr. 2008;29(5):394-402. PubMed
37. Knight A, Weiss P, Morales K, et al. Depression and anxiety and their association with healthcare utilization in pediatric lupus and mixed connective tissue disease patients: a cross-sectional study. Pediatr Rheumatol Online J. 2014;12:42. PubMed
38. Marsac ML, Kassam-Adams N, Delahanty DL, F. Widaman K, Barakat LP. Posttraumatic stress following acute medical trauma in children: a proposed model of bio-psycho-social processes during the peri-trauma period. Clin Child Fam Psychol Rev. 2014;17(4):399-411. PubMed
39. Marsac ML, Hildenbrand AK, Kohser KL, Winston FK, Li Y, Kassam-Adams N. Preventing posttraumatic stress following pediatric injury: a randomized controlled trial of a web-based psycho-educational intervention for parents. J Pediatr Psychol. 2013;38(10):1101-1111. PubMed
40. Petersen MC, Kube DA, Whitaker TM, Graff JC, Palmer FB. Prevalence of developmental and behavioral disorders in a pediatric hospital. Pediatrics. 2009;123(3):e490-e495. PubMed
1. Perou R, Bitsko RH, Blumberg SJ, et al; Centers for Disease Control and Prevention (CDC). Mental health surveillance among children—United States, 2005-2011. MMWR Suppl. 2013;62(2):1-35. PubMed
2. Merikangas KR, Nakamura EF, Kessler RC. Epidemiology of mental disorders in children and adolescents. Dialogues Clin Neurosci. 2009;11(1):7-20. PubMed
3. Doupnik SK, Mitra N, Feudtner C, Marcus SC. The influence of comorbid mood and anxiety disorders on outcomes of pediatric patients hospitalized for pneumonia. Hosp Pediatr. 2016;6(3):135-142. PubMed
4. Snell C, Fernandes S, Bujoreanu IS, Garcia G. Depression, illness severity, and healthcare utilization in cystic fibrosis. Pediatr Pulmonol. 2014;49(12):1177-1181. PubMed
5. Garrison MM, Katon WJ, Richardson LP. The impact of psychiatric comorbidities on readmissions for diabetes in youth. Diabetes Care. 2005;28(9):2150-2154. PubMed
6. Myrvik MP, Campbell AD, Davis MM, Butcher JL. Impact of psychiatric diagnoses on hospital length of stay in children with sickle cell anemia. Pediatr Blood Cancer. 2012;58(2):239-243. PubMed
7. Myrvik MP, Burks LM, Hoffman RG, Dasgupta M, Panepinto JA. Mental health disorders influence admission rates for pain in children with sickle cell disease. Pediatr Blood Cancer. 2013;60(7):1211-1214. PubMed
8. Bujoreanu S, White MT, Gerber B, Ibeziako P. Effect of timing of psychiatry consultation on length of pediatric hospitalization and hospital charges. Hosp Pediatr. 2015;5(5):269-275. PubMed
9. Archer J, Bower P, Gilbody S, et al. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:CD006525. PubMed
10. Aupont O, Doerfler L, Connor DF, Stille C, Tisminetzky M, McLaughlin TJ. A collaborative care model to improve access to pediatric mental health services. Adm Policy Ment Health. 2013;40(4):264-273. PubMed
11. Kolko DJ, Campo J, Kilbourne AM, Hart J, Sakolsky D, Wisniewski S. Collaborative care outcomes for pediatric behavioral health problems: a cluster randomized trial. Pediatrics. 2014;133(4):e981-e992. PubMed
12. Richardson L, McCauley E, Katon W. Collaborative care for adolescent depression: a pilot study. Gen Hosp Psychiatry. 2009;31(1):36-45. PubMed
13. Richardson LP, Ludman E, McCauley E, et al. Collaborative care for adolescents with depression in primary care: a randomized clinical trial. JAMA. 2014;312(8):809-816. PubMed
14. Huffman JC, Mastromauro CA, Beach SR, et al. Collaborative care for depression and anxiety disorders in patients with recent cardiac events: the Management of Sadness and Anxiety in Cardiology (MOSAIC) randomized clinical trial. JAMA Intern Med. 2014;174(6):927-935. PubMed
15. Henderson C, Noblett J, Parke H, et al. Mental health-related stigma in health care and mental health-care settings. Lancet Psychiatry. 2014;1(6):467-482. PubMed
16. Pescosolido BA, Perry BL, Martin JK, McLeod JD, Jensen PS. Stigmatizing attitudes and beliefs about treatment and psychiatric medications for children with mental illness. Psychiatr Serv. 2007;58(5):613-618. PubMed
17. Pescosolido BA. Culture, children, and mental health treatment: special section on the National Stigma Study–Children. Psychiatr Serv. 2007;58(5):611-612. PubMed
18. Britt TW, Greene-Shortridge TM, Brink S, et al. Perceived stigma and barriers to care for psychological treatment: implications for reactions to stressors in different contexts. J Soc Clin Psychol. 2008;27(4):317-335.
19. Thornicroft G, Mehta N, Clement S, et al. Evidence for effective interventions to reduce mental-health-related stigma and discrimination. Lancet. 2016;387(10023):1123-1132. PubMed
20. Health Insurance Portability and Accountability Act of 1996. Washington, DC: US Government Publishing Office; 1996. https://www.gpo.gov/fdsys/pkg/CRPT-104hrpt736/pdf/CRPT-104hrpt736.pdf. Accessed January 21, 2017.
21. Frayne SM, Miller DR, Sharkansky EJ, et al. Using administrative data to identify mental illness: what approach is best? Am J Med Qual. 2010;25(1):42-50. PubMed
22. Horwitz SM, Hoagwood K, Stiffman AR, et al. Reliability of the Services Assessment for Children and Adolescents. Psychiatr Serv. 2001;52(8):1088-1094. PubMed
23. Ascher BH, Farmer EM, Burns BJ, Angold A. The Child and Adolescent Services Assessment (CASA): description and psychometrics. J Emot Behav Disord. 1996;4(1):12-20.
24. Data Resource Center for Child & Adolescent Health, Child and Adolescent Health Measurement Initiative, Oregon Health & Science University. National Survey of Children’s Health (NSCH), 2011/12. http://childhealthdata.org/docs/drc/2011-12-guide-to-topics-questions-draft.pdf?sfvrsn=4. Accessed January 21, 2017.
25. Bardach NS, Coker TR, Zima BT, et al. Common and costly hospitalizations for pediatric mental health disorders. Pediatrics. 2014;133(4):602-609. PubMed
26. Torio CM, Encinosa W, Berdahl T, McCormick MC, Simpson LA. Annual report on health care for children and youth in the United States: national estimates of cost, utilization and expenditures for children with mental health conditions. Acad Pediatr. 2015;15(1):19-35. PubMed
27. Zima BT, Rodean J, Hall M, Bardach NS, Coker TR, Berry JG. Ten year national trends in pediatric hospitalizations by psychiatric complexity. Abstract presented at: 62nd Annual Meeting of the American Academy of Child and Adolescent Psychiatry; October 2015; San Antonio, TX.
28. Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404-413.
29. Wu CH, Erickson SR, Piette JD, Balkrishnan R. Mental health resource utilization and health care costs associated with race and comorbid anxiety among Medicaid enrollees with major depressive disorder. J Nat Med Assoc. 2012;104(1-2):78-88. PubMed
30. Mapelli E, Black T, Doan Q. Trends in pediatric emergency department utilization for mental health-related visits. J Pediatr. 2015;167(4):905-910. PubMed
31. Berdahl T, Owens PL, Dougherty D, McCormick MC, Pylypchuk Y, Simpson LA. Annual report on health care for children and youth in the United States: racial/ethnic and socioeconomic disparities in children’s health care quality. Acad Pediatr. 2010;10(2):95-118. PubMed
32. Flores G; Committee on Pediatric Research. Technical report—racial and ethnic disparities in the health and health care of children. Pediatrics. 2010;125(4):e979-e1020. PubMed
33. Turner EA, Jensen-Doss A, Heffer RW. Ethnicity as a moderator of how parents’ attitudes and perceived stigma influence intentions to seek child mental health services. Cultur Divers Ethnic Minor Psychol. 2015;21(4):613-618. PubMed
34. Shaw RJ, Wamboldt M, Bursch B, Stuber M. Practice patterns in pediatric consultation-liaison psychiatry: a national survey. Psychosomatics. 2006;47(1):43-49. PubMed
35. Benton TD, Ifeagwu JA, Smith-Whitley K. Anxiety and depression in children and adolescents with sickle cell disease. Curr Psychiatry Rep. 2007;9(2):114-121. PubMed
36. Chavira DA, Garland AF, Daley S, Hough R. The impact of medical comorbidity on mental health and functional health outcomes among children with anxiety disorders. J Dev Behav Pediatr. 2008;29(5):394-402. PubMed
37. Knight A, Weiss P, Morales K, et al. Depression and anxiety and their association with healthcare utilization in pediatric lupus and mixed connective tissue disease patients: a cross-sectional study. Pediatr Rheumatol Online J. 2014;12:42. PubMed
38. Marsac ML, Kassam-Adams N, Delahanty DL, F. Widaman K, Barakat LP. Posttraumatic stress following acute medical trauma in children: a proposed model of bio-psycho-social processes during the peri-trauma period. Clin Child Fam Psychol Rev. 2014;17(4):399-411. PubMed
39. Marsac ML, Hildenbrand AK, Kohser KL, Winston FK, Li Y, Kassam-Adams N. Preventing posttraumatic stress following pediatric injury: a randomized controlled trial of a web-based psycho-educational intervention for parents. J Pediatr Psychol. 2013;38(10):1101-1111. PubMed
40. Petersen MC, Kube DA, Whitaker TM, Graff JC, Palmer FB. Prevalence of developmental and behavioral disorders in a pediatric hospital. Pediatrics. 2009;123(3):e490-e495. PubMed
© 2017 Society of Hospital Medicine
Perceived safety and value of inpatient “very important person” services
Recent publications in the medical literature and lay press have stirred controversy regarding the use of inpatient ‘very important person’ (VIP) services.1-3 The term “VIP services” often refers to select conveniences offered in addition to the assumed basic level of care and services provided by a hospital. Examples include additional space, enhanced facilities, specific comforts, or personal support. In some instances, these amenities may only be provided to patients who have close financial, social, or professional relationships with the hospital.
How VIP patients interact with their health system to obtain VIP services has raised unique concerns. Some have speculated that the presence of a VIP patient may be disruptive to the care of non-VIP patients, while others have cautioned physicians about potential dangers to the VIP patients themselves.4-6 Despite much being written on the topics of VIP patients and services in both the lay and academic press, our literature review identified only 1 study on the topic, which cataloged the preferential treatment of VIP patients in the emergency department.6 We are unaware of any investigations of VIP-service use in the inpatient setting. Through a multisite survey of hospital medicine physicians, we assessed physician viewpoints and behavior regarding VIP services.
METHODS
The Hospital Medicine Reengineering Network (HOMERuN) is a nation-wide learning organization focused on measuring and improving the outcomes of hospitalized patients.7 We surveyed hospitalists from 8 HOMERuN hospitals (Appendix 1). The survey instrument contained 4 sections: nonidentifying respondent demographics, local use of VIP services, reported physician perceptions of VIP services, and case-based assessments (Appendix 2). Survey questions and individual cases were developed by study authors and based on real scenarios and concerns provided by front-line clinical providers. Content, length, and reliability of physician understanding were assessed by a 5-person focus group consisting of physicians not included in the survey population.
Subjects were identified via administrative rosters from each HOMERuN site. Surveys were administered via SurveyMonkey, and results were analyzed descriptively. Populations were compared via the Fisher exact test. “VIP services” were defined as conveniences provided in addition to the assumed basic level of care and services (eg, private or luxury-style rooms, access to a special menu, better views, dedicated personal care attendants, hospital liaisons). VIP patients were defined as those patients receiving VIP services. A hospital was identified as providing VIP services if 50% or more of respondents from that site reported the presence of VIP services.
RESULTS
Of 366 hospitalists contacted, 160 completed the survey (44%). Respondent characteristics and reported prevalence of VIP services are demonstrated in Table 1. In total, 78 respondents (45%) reported the presence of VIP services at their hospital. Of the 8 sites surveyed, a majority of physicians at 4 sites (50%) reported presence of VIP services.
Of respondents reporting the presence of VIP services at their hospital, a majority felt that, from a patient safety perspective, the care received by VIP patients was the same as care received by non-VIP patients (Table 2). A majority reported they had felt pressured by a VIP patient or a family member to order additional tests or treatments that the physician believed were medically unnecessary and that they would be more likely to comply with VIP patient’s requests for tests or treatments they felt were unnecessary. More than one-third (36%) felt pressured by other hospital employees or representatives to comply with VIP services patient’s requests for additional tests or treatments that the physicians believed were medically unnecessary.
When presented the case of a VIP patient with community-acquired pneumonia who is clinically stable for discharge but expressing concerns about leaving the hospital, 61 (38%) respondents reported they would not discharge this patient home: 39 of 70 (55.7%) who reported the presence of VIP services at their hospital, and 22 of 91 (24.2%) who reported the absence of VIP services (P < 0.001). Of those who reported they would not discharge this patient home, 37 (61%) reported the reason for this related to the patient’s connection to the Board of Trustees; 48 (79%) reported the reason for this related to the patient’s concerns; 9 (15%) reported the reason for this related to their own concerns regarding medical details of the patient’s case (respondents could select more than 1 reason).
When presented the case of a VIP patient with acute pulmonary embolism who is medically ready for discharge with primary care physician-approved anticoagulation and discharge plans but for whom their family requests additional consultations and inpatient hypercoagulable workup, 33 (21%) respondents reported they would order additional testing and specialist consultation: 17 of 69 (24.6%) who reported the presence of VIP services their hospital, and 16 of 91 (17.6%) who reported the absence of VIP services (P = 0.33). Of those who reported they would order additional testing and specialist consultation, 14 (42%) reported the reason for this related to the family’s financial connections to the hospital; 30 (91%) reported the reason for this related to the family’s concerns; 3 (9%) reported the reason for this related to their own concerns about the medical details of the patient’s case (respondents could select more than 1 reason).
DISCUSSION
In our study, a majority of physicians who reported the presence of VIP services at their hospital felt pressured by VIP patients or their family members to perform unnecessary testing or treatment. While this study was not designed to quantify the burden of unnecessary care for VIP patients, our results have implications for individual patients and public health, including potential effects on resource availability, the identification of clinically irrelevant incidental findings, and short- and long-term medical complications of procedures, testing and radiation exposure.
Prior publications have advocated that physicians and hospitals should not allow VIP status to influence management decisions.3,5 We found that more than one-third of physicians who reported the presence of VIP services at their hospital also reported receiving pressure from hospital representatives to provide care to VIP patients that was not medically indicated. These findings highlight an example of the tension faced by physicians who are caught between patient requests and the delivery of value-based care. This potential conflict may be amplified particularly for those patients with close financial, social, or professional ties to the hospitals (and physicians) providing their care. These results suggest the need for physicians, administrators, and patients to work together to address the potential blurring of ethical boundaries created by VIP relationships. Prevention of harm and avoidance of placing physicians in morally distressing situations are common goals for all involved parties.
Efforts to reduce unnecessary care have predominantly focused on structural and knowledge-based drivers.4,8,9 Our results highlight the presence of additional forces. A majority of physician respondents who reported the presence of VIP services at their hospital also reported that they would be more likely to comply with requests for unnecessary care for a VIP patient as compared to a non-VIP patient. Furthermore, in case-based questions about the requests of a VIP patient and their family for additional unnecessary care, a significant portion of physicians who reported they would comply with these requests listed the VIP status of the patient or family as a factor underlying this decision. Only a minority of physicians reported their decision to provide additional care was the result of their own medically-based concerns. Because these cases were hypothetical and we did not include comparator cases involving non-VIP patients, it remains uncertain whether the observed perceptions accurately reflect real-world differences in the care of VIP and non-VIP patients. Nonetheless, our findings emphasize the importance of better understanding the social drivers of overuse and physician communication strategies related to medically inappropriate tests.10,11
Demand for unnecessary testing may be driven by the mentality that “more is better.”12 Contrary to this belief, provision of unnecessary care can increase the risk of patient harm.13 Despite physician respondents reporting that VIP patients requested and/or received additional unnecessary care, a majority of respondents felt that patient safety for VIP patients was equivalent to that for non-VIP patients. As we assessed only physician perceptions of safety, which may not necessarily correlate with actual safety, further research in this area is needed.
Our study was limited by several factors. While our study population included hospitalists from 8 geographically broad hospitals, including university, safety net, and community hospitals, study responses may not be reflective of nationwide trends. Our response rate may limit our ability to generalize conclusions beyond respondents. Second, our study captured physician perceptions of behavior and safety rather than actually measuring practice and outcomes. Studies comparing physician practice patterns and outcomes between VIP and non-VIP patients would be informative. Additionally, despite our inclusive survey design process, our survey was not validated, and it is possible that our questions were not interpreted as intended. Lastly, despite the anonymous nature of our survey, physicians may have felt compelled to respond in a particular way due to conflicting professional, financial, or social factors.
Our findings provide initial insight into how care for the VIP patient may present unique challenges for physicians, hospitals, and society by systematizing care inequities, as well as potentially incentivizing low-value care practices. Whether these imbalances produce clinical harms or benefits remains worthy of future studies.
Disclosure
Nothing to report.
1. Bernstein N. Chefs, butlers, marble baths: Hospitals vie for the affluent. New York Times. January 21, 2012. http://www.nytimes.com/2012/01/22/nyregion/chefs-butlers-and-marble-baths-not-your-average-hospital-room.html. Accessed February 1, 2017.
2. Kennedy DW, Kagan SH, Abramson KB, Boberick C, Kaiser LR. Academic medicine amenities unit: developing a model to integrate academic medical care with luxury hotel services. Acad Med. 2009;84(2):185-191. PubMed
3. Alfandre D, Clever S, Farber NJ, Hughes MT, Redstone P, Lehmann LS. Caring for ‘very important patients’--ethical dilemmas and suggestions for practical management. Am J Med. 2016;129(2):143-147. PubMed
4. Bulger J, Nickel W, Messler J, et al. Choosing wisely in adult hospital medicine: five opportunities for improved healthcare value. J Hosp Med. 2013;8(9):486-492. PubMed
5. Martin A, Bostic JQ, Pruett K. The V.I.P.: hazard and promise in treating “special” patients. J Am Acad Child Adolesc Psychiatry. 2004;43(3):366-369. PubMed
6. Smally AJ, Carroll B, Carius M, Tilden F, Werdmann M. Treatment of VIPs. Ann Emerg Med. 2011;58(4):397-398. PubMed
7. Auerbach AD, Patel MS, Metlay JP, et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
8. Caverly TJ, Combs BP, Moriates C, Shah N, Grady D. Too much medicine happens too often: the teachable moment and a call for manuscripts from clinical trainees. JAMA Intern Med. 2014;174(1):8-9. PubMed
9. Schwartz AL, Chernew ME, Landon BE, McWilliams JM. Changes in low-value services in year 1 of the Medicare pioneer accountable care organization program. JAMA Intern Med. 2015;175(11):1815-1825. PubMed
10. Paterniti DA, Fancher TL, Cipri CS, Timmermans S, Heritage J, Kravitz RL. Getting to “no”: strategies primary care physicians use to deny patient requests. Arch Intern Med. 2010;170(4):381-388. PubMed
11. Veroff D, Marr A, Wennberg DE. Enhanced support for shared decision making reduced costs of care for patients with preference-sensitive conditions. Health Aff (Millwood). 2013;32(2):285-293. PubMed
12. Korenstein D. Patient perception of benefits and harms: the Achilles heel of high-value care. JAMA Intern Med. 2015;175(2):287-288. PubMed
13. Moynihan R, Doust J, Henry D. Preventing overdiagnosis: how to stop harming the healthy. BMJ. 2012;344:e3502. PubMed
Recent publications in the medical literature and lay press have stirred controversy regarding the use of inpatient ‘very important person’ (VIP) services.1-3 The term “VIP services” often refers to select conveniences offered in addition to the assumed basic level of care and services provided by a hospital. Examples include additional space, enhanced facilities, specific comforts, or personal support. In some instances, these amenities may only be provided to patients who have close financial, social, or professional relationships with the hospital.
How VIP patients interact with their health system to obtain VIP services has raised unique concerns. Some have speculated that the presence of a VIP patient may be disruptive to the care of non-VIP patients, while others have cautioned physicians about potential dangers to the VIP patients themselves.4-6 Despite much being written on the topics of VIP patients and services in both the lay and academic press, our literature review identified only 1 study on the topic, which cataloged the preferential treatment of VIP patients in the emergency department.6 We are unaware of any investigations of VIP-service use in the inpatient setting. Through a multisite survey of hospital medicine physicians, we assessed physician viewpoints and behavior regarding VIP services.
METHODS
The Hospital Medicine Reengineering Network (HOMERuN) is a nation-wide learning organization focused on measuring and improving the outcomes of hospitalized patients.7 We surveyed hospitalists from 8 HOMERuN hospitals (Appendix 1). The survey instrument contained 4 sections: nonidentifying respondent demographics, local use of VIP services, reported physician perceptions of VIP services, and case-based assessments (Appendix 2). Survey questions and individual cases were developed by study authors and based on real scenarios and concerns provided by front-line clinical providers. Content, length, and reliability of physician understanding were assessed by a 5-person focus group consisting of physicians not included in the survey population.
Subjects were identified via administrative rosters from each HOMERuN site. Surveys were administered via SurveyMonkey, and results were analyzed descriptively. Populations were compared via the Fisher exact test. “VIP services” were defined as conveniences provided in addition to the assumed basic level of care and services (eg, private or luxury-style rooms, access to a special menu, better views, dedicated personal care attendants, hospital liaisons). VIP patients were defined as those patients receiving VIP services. A hospital was identified as providing VIP services if 50% or more of respondents from that site reported the presence of VIP services.
RESULTS
Of 366 hospitalists contacted, 160 completed the survey (44%). Respondent characteristics and reported prevalence of VIP services are demonstrated in Table 1. In total, 78 respondents (45%) reported the presence of VIP services at their hospital. Of the 8 sites surveyed, a majority of physicians at 4 sites (50%) reported presence of VIP services.
Of respondents reporting the presence of VIP services at their hospital, a majority felt that, from a patient safety perspective, the care received by VIP patients was the same as care received by non-VIP patients (Table 2). A majority reported they had felt pressured by a VIP patient or a family member to order additional tests or treatments that the physician believed were medically unnecessary and that they would be more likely to comply with VIP patient’s requests for tests or treatments they felt were unnecessary. More than one-third (36%) felt pressured by other hospital employees or representatives to comply with VIP services patient’s requests for additional tests or treatments that the physicians believed were medically unnecessary.
When presented the case of a VIP patient with community-acquired pneumonia who is clinically stable for discharge but expressing concerns about leaving the hospital, 61 (38%) respondents reported they would not discharge this patient home: 39 of 70 (55.7%) who reported the presence of VIP services at their hospital, and 22 of 91 (24.2%) who reported the absence of VIP services (P < 0.001). Of those who reported they would not discharge this patient home, 37 (61%) reported the reason for this related to the patient’s connection to the Board of Trustees; 48 (79%) reported the reason for this related to the patient’s concerns; 9 (15%) reported the reason for this related to their own concerns regarding medical details of the patient’s case (respondents could select more than 1 reason).
When presented the case of a VIP patient with acute pulmonary embolism who is medically ready for discharge with primary care physician-approved anticoagulation and discharge plans but for whom their family requests additional consultations and inpatient hypercoagulable workup, 33 (21%) respondents reported they would order additional testing and specialist consultation: 17 of 69 (24.6%) who reported the presence of VIP services their hospital, and 16 of 91 (17.6%) who reported the absence of VIP services (P = 0.33). Of those who reported they would order additional testing and specialist consultation, 14 (42%) reported the reason for this related to the family’s financial connections to the hospital; 30 (91%) reported the reason for this related to the family’s concerns; 3 (9%) reported the reason for this related to their own concerns about the medical details of the patient’s case (respondents could select more than 1 reason).
DISCUSSION
In our study, a majority of physicians who reported the presence of VIP services at their hospital felt pressured by VIP patients or their family members to perform unnecessary testing or treatment. While this study was not designed to quantify the burden of unnecessary care for VIP patients, our results have implications for individual patients and public health, including potential effects on resource availability, the identification of clinically irrelevant incidental findings, and short- and long-term medical complications of procedures, testing and radiation exposure.
Prior publications have advocated that physicians and hospitals should not allow VIP status to influence management decisions.3,5 We found that more than one-third of physicians who reported the presence of VIP services at their hospital also reported receiving pressure from hospital representatives to provide care to VIP patients that was not medically indicated. These findings highlight an example of the tension faced by physicians who are caught between patient requests and the delivery of value-based care. This potential conflict may be amplified particularly for those patients with close financial, social, or professional ties to the hospitals (and physicians) providing their care. These results suggest the need for physicians, administrators, and patients to work together to address the potential blurring of ethical boundaries created by VIP relationships. Prevention of harm and avoidance of placing physicians in morally distressing situations are common goals for all involved parties.
Efforts to reduce unnecessary care have predominantly focused on structural and knowledge-based drivers.4,8,9 Our results highlight the presence of additional forces. A majority of physician respondents who reported the presence of VIP services at their hospital also reported that they would be more likely to comply with requests for unnecessary care for a VIP patient as compared to a non-VIP patient. Furthermore, in case-based questions about the requests of a VIP patient and their family for additional unnecessary care, a significant portion of physicians who reported they would comply with these requests listed the VIP status of the patient or family as a factor underlying this decision. Only a minority of physicians reported their decision to provide additional care was the result of their own medically-based concerns. Because these cases were hypothetical and we did not include comparator cases involving non-VIP patients, it remains uncertain whether the observed perceptions accurately reflect real-world differences in the care of VIP and non-VIP patients. Nonetheless, our findings emphasize the importance of better understanding the social drivers of overuse and physician communication strategies related to medically inappropriate tests.10,11
Demand for unnecessary testing may be driven by the mentality that “more is better.”12 Contrary to this belief, provision of unnecessary care can increase the risk of patient harm.13 Despite physician respondents reporting that VIP patients requested and/or received additional unnecessary care, a majority of respondents felt that patient safety for VIP patients was equivalent to that for non-VIP patients. As we assessed only physician perceptions of safety, which may not necessarily correlate with actual safety, further research in this area is needed.
Our study was limited by several factors. While our study population included hospitalists from 8 geographically broad hospitals, including university, safety net, and community hospitals, study responses may not be reflective of nationwide trends. Our response rate may limit our ability to generalize conclusions beyond respondents. Second, our study captured physician perceptions of behavior and safety rather than actually measuring practice and outcomes. Studies comparing physician practice patterns and outcomes between VIP and non-VIP patients would be informative. Additionally, despite our inclusive survey design process, our survey was not validated, and it is possible that our questions were not interpreted as intended. Lastly, despite the anonymous nature of our survey, physicians may have felt compelled to respond in a particular way due to conflicting professional, financial, or social factors.
Our findings provide initial insight into how care for the VIP patient may present unique challenges for physicians, hospitals, and society by systematizing care inequities, as well as potentially incentivizing low-value care practices. Whether these imbalances produce clinical harms or benefits remains worthy of future studies.
Disclosure
Nothing to report.
Recent publications in the medical literature and lay press have stirred controversy regarding the use of inpatient ‘very important person’ (VIP) services.1-3 The term “VIP services” often refers to select conveniences offered in addition to the assumed basic level of care and services provided by a hospital. Examples include additional space, enhanced facilities, specific comforts, or personal support. In some instances, these amenities may only be provided to patients who have close financial, social, or professional relationships with the hospital.
How VIP patients interact with their health system to obtain VIP services has raised unique concerns. Some have speculated that the presence of a VIP patient may be disruptive to the care of non-VIP patients, while others have cautioned physicians about potential dangers to the VIP patients themselves.4-6 Despite much being written on the topics of VIP patients and services in both the lay and academic press, our literature review identified only 1 study on the topic, which cataloged the preferential treatment of VIP patients in the emergency department.6 We are unaware of any investigations of VIP-service use in the inpatient setting. Through a multisite survey of hospital medicine physicians, we assessed physician viewpoints and behavior regarding VIP services.
METHODS
The Hospital Medicine Reengineering Network (HOMERuN) is a nation-wide learning organization focused on measuring and improving the outcomes of hospitalized patients.7 We surveyed hospitalists from 8 HOMERuN hospitals (Appendix 1). The survey instrument contained 4 sections: nonidentifying respondent demographics, local use of VIP services, reported physician perceptions of VIP services, and case-based assessments (Appendix 2). Survey questions and individual cases were developed by study authors and based on real scenarios and concerns provided by front-line clinical providers. Content, length, and reliability of physician understanding were assessed by a 5-person focus group consisting of physicians not included in the survey population.
Subjects were identified via administrative rosters from each HOMERuN site. Surveys were administered via SurveyMonkey, and results were analyzed descriptively. Populations were compared via the Fisher exact test. “VIP services” were defined as conveniences provided in addition to the assumed basic level of care and services (eg, private or luxury-style rooms, access to a special menu, better views, dedicated personal care attendants, hospital liaisons). VIP patients were defined as those patients receiving VIP services. A hospital was identified as providing VIP services if 50% or more of respondents from that site reported the presence of VIP services.
RESULTS
Of 366 hospitalists contacted, 160 completed the survey (44%). Respondent characteristics and reported prevalence of VIP services are demonstrated in Table 1. In total, 78 respondents (45%) reported the presence of VIP services at their hospital. Of the 8 sites surveyed, a majority of physicians at 4 sites (50%) reported presence of VIP services.
Of respondents reporting the presence of VIP services at their hospital, a majority felt that, from a patient safety perspective, the care received by VIP patients was the same as care received by non-VIP patients (Table 2). A majority reported they had felt pressured by a VIP patient or a family member to order additional tests or treatments that the physician believed were medically unnecessary and that they would be more likely to comply with VIP patient’s requests for tests or treatments they felt were unnecessary. More than one-third (36%) felt pressured by other hospital employees or representatives to comply with VIP services patient’s requests for additional tests or treatments that the physicians believed were medically unnecessary.
When presented the case of a VIP patient with community-acquired pneumonia who is clinically stable for discharge but expressing concerns about leaving the hospital, 61 (38%) respondents reported they would not discharge this patient home: 39 of 70 (55.7%) who reported the presence of VIP services at their hospital, and 22 of 91 (24.2%) who reported the absence of VIP services (P < 0.001). Of those who reported they would not discharge this patient home, 37 (61%) reported the reason for this related to the patient’s connection to the Board of Trustees; 48 (79%) reported the reason for this related to the patient’s concerns; 9 (15%) reported the reason for this related to their own concerns regarding medical details of the patient’s case (respondents could select more than 1 reason).
When presented the case of a VIP patient with acute pulmonary embolism who is medically ready for discharge with primary care physician-approved anticoagulation and discharge plans but for whom their family requests additional consultations and inpatient hypercoagulable workup, 33 (21%) respondents reported they would order additional testing and specialist consultation: 17 of 69 (24.6%) who reported the presence of VIP services their hospital, and 16 of 91 (17.6%) who reported the absence of VIP services (P = 0.33). Of those who reported they would order additional testing and specialist consultation, 14 (42%) reported the reason for this related to the family’s financial connections to the hospital; 30 (91%) reported the reason for this related to the family’s concerns; 3 (9%) reported the reason for this related to their own concerns about the medical details of the patient’s case (respondents could select more than 1 reason).
DISCUSSION
In our study, a majority of physicians who reported the presence of VIP services at their hospital felt pressured by VIP patients or their family members to perform unnecessary testing or treatment. While this study was not designed to quantify the burden of unnecessary care for VIP patients, our results have implications for individual patients and public health, including potential effects on resource availability, the identification of clinically irrelevant incidental findings, and short- and long-term medical complications of procedures, testing and radiation exposure.
Prior publications have advocated that physicians and hospitals should not allow VIP status to influence management decisions.3,5 We found that more than one-third of physicians who reported the presence of VIP services at their hospital also reported receiving pressure from hospital representatives to provide care to VIP patients that was not medically indicated. These findings highlight an example of the tension faced by physicians who are caught between patient requests and the delivery of value-based care. This potential conflict may be amplified particularly for those patients with close financial, social, or professional ties to the hospitals (and physicians) providing their care. These results suggest the need for physicians, administrators, and patients to work together to address the potential blurring of ethical boundaries created by VIP relationships. Prevention of harm and avoidance of placing physicians in morally distressing situations are common goals for all involved parties.
Efforts to reduce unnecessary care have predominantly focused on structural and knowledge-based drivers.4,8,9 Our results highlight the presence of additional forces. A majority of physician respondents who reported the presence of VIP services at their hospital also reported that they would be more likely to comply with requests for unnecessary care for a VIP patient as compared to a non-VIP patient. Furthermore, in case-based questions about the requests of a VIP patient and their family for additional unnecessary care, a significant portion of physicians who reported they would comply with these requests listed the VIP status of the patient or family as a factor underlying this decision. Only a minority of physicians reported their decision to provide additional care was the result of their own medically-based concerns. Because these cases were hypothetical and we did not include comparator cases involving non-VIP patients, it remains uncertain whether the observed perceptions accurately reflect real-world differences in the care of VIP and non-VIP patients. Nonetheless, our findings emphasize the importance of better understanding the social drivers of overuse and physician communication strategies related to medically inappropriate tests.10,11
Demand for unnecessary testing may be driven by the mentality that “more is better.”12 Contrary to this belief, provision of unnecessary care can increase the risk of patient harm.13 Despite physician respondents reporting that VIP patients requested and/or received additional unnecessary care, a majority of respondents felt that patient safety for VIP patients was equivalent to that for non-VIP patients. As we assessed only physician perceptions of safety, which may not necessarily correlate with actual safety, further research in this area is needed.
Our study was limited by several factors. While our study population included hospitalists from 8 geographically broad hospitals, including university, safety net, and community hospitals, study responses may not be reflective of nationwide trends. Our response rate may limit our ability to generalize conclusions beyond respondents. Second, our study captured physician perceptions of behavior and safety rather than actually measuring practice and outcomes. Studies comparing physician practice patterns and outcomes between VIP and non-VIP patients would be informative. Additionally, despite our inclusive survey design process, our survey was not validated, and it is possible that our questions were not interpreted as intended. Lastly, despite the anonymous nature of our survey, physicians may have felt compelled to respond in a particular way due to conflicting professional, financial, or social factors.
Our findings provide initial insight into how care for the VIP patient may present unique challenges for physicians, hospitals, and society by systematizing care inequities, as well as potentially incentivizing low-value care practices. Whether these imbalances produce clinical harms or benefits remains worthy of future studies.
Disclosure
Nothing to report.
1. Bernstein N. Chefs, butlers, marble baths: Hospitals vie for the affluent. New York Times. January 21, 2012. http://www.nytimes.com/2012/01/22/nyregion/chefs-butlers-and-marble-baths-not-your-average-hospital-room.html. Accessed February 1, 2017.
2. Kennedy DW, Kagan SH, Abramson KB, Boberick C, Kaiser LR. Academic medicine amenities unit: developing a model to integrate academic medical care with luxury hotel services. Acad Med. 2009;84(2):185-191. PubMed
3. Alfandre D, Clever S, Farber NJ, Hughes MT, Redstone P, Lehmann LS. Caring for ‘very important patients’--ethical dilemmas and suggestions for practical management. Am J Med. 2016;129(2):143-147. PubMed
4. Bulger J, Nickel W, Messler J, et al. Choosing wisely in adult hospital medicine: five opportunities for improved healthcare value. J Hosp Med. 2013;8(9):486-492. PubMed
5. Martin A, Bostic JQ, Pruett K. The V.I.P.: hazard and promise in treating “special” patients. J Am Acad Child Adolesc Psychiatry. 2004;43(3):366-369. PubMed
6. Smally AJ, Carroll B, Carius M, Tilden F, Werdmann M. Treatment of VIPs. Ann Emerg Med. 2011;58(4):397-398. PubMed
7. Auerbach AD, Patel MS, Metlay JP, et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
8. Caverly TJ, Combs BP, Moriates C, Shah N, Grady D. Too much medicine happens too often: the teachable moment and a call for manuscripts from clinical trainees. JAMA Intern Med. 2014;174(1):8-9. PubMed
9. Schwartz AL, Chernew ME, Landon BE, McWilliams JM. Changes in low-value services in year 1 of the Medicare pioneer accountable care organization program. JAMA Intern Med. 2015;175(11):1815-1825. PubMed
10. Paterniti DA, Fancher TL, Cipri CS, Timmermans S, Heritage J, Kravitz RL. Getting to “no”: strategies primary care physicians use to deny patient requests. Arch Intern Med. 2010;170(4):381-388. PubMed
11. Veroff D, Marr A, Wennberg DE. Enhanced support for shared decision making reduced costs of care for patients with preference-sensitive conditions. Health Aff (Millwood). 2013;32(2):285-293. PubMed
12. Korenstein D. Patient perception of benefits and harms: the Achilles heel of high-value care. JAMA Intern Med. 2015;175(2):287-288. PubMed
13. Moynihan R, Doust J, Henry D. Preventing overdiagnosis: how to stop harming the healthy. BMJ. 2012;344:e3502. PubMed
1. Bernstein N. Chefs, butlers, marble baths: Hospitals vie for the affluent. New York Times. January 21, 2012. http://www.nytimes.com/2012/01/22/nyregion/chefs-butlers-and-marble-baths-not-your-average-hospital-room.html. Accessed February 1, 2017.
2. Kennedy DW, Kagan SH, Abramson KB, Boberick C, Kaiser LR. Academic medicine amenities unit: developing a model to integrate academic medical care with luxury hotel services. Acad Med. 2009;84(2):185-191. PubMed
3. Alfandre D, Clever S, Farber NJ, Hughes MT, Redstone P, Lehmann LS. Caring for ‘very important patients’--ethical dilemmas and suggestions for practical management. Am J Med. 2016;129(2):143-147. PubMed
4. Bulger J, Nickel W, Messler J, et al. Choosing wisely in adult hospital medicine: five opportunities for improved healthcare value. J Hosp Med. 2013;8(9):486-492. PubMed
5. Martin A, Bostic JQ, Pruett K. The V.I.P.: hazard and promise in treating “special” patients. J Am Acad Child Adolesc Psychiatry. 2004;43(3):366-369. PubMed
6. Smally AJ, Carroll B, Carius M, Tilden F, Werdmann M. Treatment of VIPs. Ann Emerg Med. 2011;58(4):397-398. PubMed
7. Auerbach AD, Patel MS, Metlay JP, et al. The hospital medicine reengineering network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
8. Caverly TJ, Combs BP, Moriates C, Shah N, Grady D. Too much medicine happens too often: the teachable moment and a call for manuscripts from clinical trainees. JAMA Intern Med. 2014;174(1):8-9. PubMed
9. Schwartz AL, Chernew ME, Landon BE, McWilliams JM. Changes in low-value services in year 1 of the Medicare pioneer accountable care organization program. JAMA Intern Med. 2015;175(11):1815-1825. PubMed
10. Paterniti DA, Fancher TL, Cipri CS, Timmermans S, Heritage J, Kravitz RL. Getting to “no”: strategies primary care physicians use to deny patient requests. Arch Intern Med. 2010;170(4):381-388. PubMed
11. Veroff D, Marr A, Wennberg DE. Enhanced support for shared decision making reduced costs of care for patients with preference-sensitive conditions. Health Aff (Millwood). 2013;32(2):285-293. PubMed
12. Korenstein D. Patient perception of benefits and harms: the Achilles heel of high-value care. JAMA Intern Med. 2015;175(2):287-288. PubMed
13. Moynihan R, Doust J, Henry D. Preventing overdiagnosis: how to stop harming the healthy. BMJ. 2012;344:e3502. PubMed
© 2017 Society of Hospital Medicine
A time and motion study of pharmacists and pharmacy technicians obtaining admission medication histories
Using pharmacists to obtain admission medication histories (AMHs) reduces medication errors by 70% to 83% and resultant adverse drug events (ADEs) by 15%.1-3 Dissemination of this practice has been limited by several factors, including clinician practice models, staff availability, confusion in provider roles and accountability, and absence of standardized best practices.4-5 This paper assesses one of these barriers: the high cost of utilizing pharmacists. Third-person observer time and motion analysis shows that pharmacists require 46 and 92 minutes to obtain AMHs from medical and geriatric patients,6 respectively, resulting in pharmacist costs of $44 to $88 per patient, based on 2015 US Bureau of Labor Statistics (BLS) hourly wage data for pharmacists ($57.34).7
Ph
METHODS
This study originated as part of a randomized, controlled trial conducted during January-February 2014 at Cedars-Sinai Medical Center (CSMC), an 896-bed, university-affiliated, not-for-profit hospital.9 Pharmacy staff included pharmacists, PGY-1 pharmacy residents, and pharmacy technicians, each of whom received standardized didactic and experiential training (Appendix 1).
The pharmacists’ AMH and general pharmacy experience ranged from <1 to 3 years and <1 to 5 years, respectively. For PSPTs, AMH and general pharmacy experience ranged from <1 to 2 years and 1 to 17 years, respectively. Three additional pharmacists were involved in supervising PSPTs, and their experience fell within the aforementioned ranges, except for one pharmacist with general pharmacy experience of 16 years. The CSMC Institutional Review Board approved this study with oral consent from pharmacy staff.
For the trial, pharmacists and PSPTs obtained AMHs from 185 patients identified as high-risk for ADEs in the CSMC Emergency Department (ED). Patients were randomized into each arm using RANDI2 software11 if they met one of the trial inclusion criteria, accessed via electronic health record (EHR) (Appendix 2). For several days during this trial, a trained research nurse shadowed pharmacists and PSPTs to record tasks performed, as well as the actual time, including start and end times, dedicated to each task.
After excluding AMHs with incomplete data, we calculated mean AMH times and component task times (Table). We compared mean times for pharmacists and PSPTs using two sample t tests (Table). We calculated mean times of tasks across only AMHs that required the task, mean times of tasks across all AMHs studied, regardless of whether the AMH required the task or not (assigning 0 minutes for the task if it was not required), and percent mean time of task per patient for providers combined (Table).
We calculated Pearson product-moment correlation estimates between AMH time and these continuous variables: patient age; total number of EHR medications; number of chronic EHR medications; years of provider AMH experience; and years of provider general pharmacy experience. Using two sample t tests, we also checked for associations between AMH time and the following categorical variables: sex; presence of a patient-provided medication list; caregiver availability; and altered mental status, as determined by review of the ED physician’s note. Caregiver availability was defined as the availability of a family member, caregiver, or medication administration record (MAR) for patients residing at a skilled nursing facility (SNF). The rationale for combining these variables is that SNF nurses are the primary caregivers responsible for administering medications, and the MAR is reflective of their actions.
After reviewing our initial data, we decided to increase our sample size from 20 to 30 complete AMHs. Because the trial had concluded, we selected 10 additional patients who met trial criteria and who would already have an AMH obtained by pharmacy staff for operational reasons. The only difference with the second set of patients (n = 10) is that we did not randomize patients into each arm, but chose to focus on AMHs obtained by PSPTs, as there is a greater need in the literature to study PSPTs. After finalizing data collection, the aforementioned analyses were conducted on the complete data set.
Lastly, we estimated the mean labor cost for pharmacists and PSPTs to obtain an AMH by using 2015 US BLS hourly wage data for pharmacists ($57.34) and pharmacy technicians ($15.23).7 The cost for a pharmacist-obtained AMH was calculated by multiplying the measured mean time a pharmacist needed to obtain an AMH by $57.34 per hour. The cost for a PSPT-obtained AMH was the sum of the PSPT’s measured mean time to obtain an AMH multiplied by $15.23 per hour and the measured mean pharmacist supervisory time multiplied by $57.34 per hour.
RESULTS
Of the 37 observed AMHs, 30 had complete data. Seven AMHs were excluded because not all task times were recorded, due to the schedule restraints of the research nurse. Pharmacists and PSPTs obtained 12 and 18 AMHs, respectively. Mean patient ages were 83.3 (95% confidence interval [CI], 77.3-89.2) and 79.8 (95% CI, 71.5-88.0), for pharmacists and PSPTs, respectively (P = 0.55). Patient’s EHRs contained a mean of 14.3 (95% CI, 11.2-17.5) and 16.3 (95% CI, 13.2-19.5) medications, prior to pharmacists and PSPTs obtaining an AMH, respectively (P = 0.41).
The mean time pharmacists and PSPTs needed to obtain an AMH was 58.5 (95% CI, 46.9-70.1) and 79.4 (95% CI, 59.1-99.8) minutes, respectively (P = 0.14). Summary time data per provider is reported in the Figure. The mean time for pharmacist supervision of technicians was 26 (95% CI, 14.9-37.1) minutes. Mean times of tasks and comparisons of these means times between providers are reported in the Table. The percent mean time for each task per patient for providers combined is also reported in the Table, in which utilizing the EHR was associated with the greatest percentage of time spent at 42.8% (95% CI, 37.4-48.2).
In the 18 cases for which a caregiver (or SNF medication list) was available, providers needed only 58.1 (95% CI, 44.1-72.1) minutes to obtain an AMH, as compared with 90.5 (95% CI, 67.9-113.1) minutes for the 12 cases lacking these resources (P = 0.02). We also found that among PSPTs, years of AMH experience were positively correlated with AMH time (coefficient of correlation 0.49, P = 0.04). No other studied variables were correlated with or associated with differential AMH times.
We estimated mean labor costs for pharmacists and PSPTs to obtain AMHs as $55.91 (95% CI, 44.9-67.0) and $45.00 (95% CI, 29.7-60.4) per patient, respectively (P = 0.32). In the latter case, $24.85 (95% CI, 14.3-35.4) of the $45.00 would be needed for pharmacist supervisory time. The labor cost for a PSPT-obtained AMH ($45.00) was the sum of the PSPT’s mean time (79.4 minutes) multiplied by technician wage data ($15.23/hour) and supervising pharmacist’s mean time (26.0 minutes) multiplied by pharmacist wage data ($57.34/hour).
DISCUSSION
Although limited by sample size, we observed no difference in time or costs of obtaining AMHs between pharmacists and PSPTs. Several prior studies reported that pharmacists and technicians needed less time to obtain AMHs (20-40 minutes), as compared with our findings.12-14 However, most prior studies used younger, healthier patients. Additionally, they used clinician self-reporting instead of third-person observer time and motion methodology. Indeed, the pharmacist times we observed in this study were consistent with prior findings6 that used accepted third-person observer time and motion methodology.10
We observed more variation in time to obtain AMHs among PSPTs than among pharmacists. While variation may be at least in part to the greater number of technicians studied, variation also points to the need for training and oversight of PSPTs. Selection of PSPTs with prior experience interacting with patients and functioning with higher levels of autonomy, standardized training of PSPTs, and consistent dedication of trained PSPTs to AMH functions to maintain their skills, may help to minimize such variation.
Limitations include the use of a single center and a small sample size. As such, the study may be underpowered to demonstrate statistically significant differences between providers. Furthermore, 7 AMHs (19%) had to be excluded because complete task times were missing. This was exclusively because the workday of the research nurse ended before the AMH had been completed. Another limitation was that the tasks observed could have been dissected further to identify even more specific factors that could be targeted to decrease AMH times. We recommend that future studies be larger, investigate in more depth various factors associated with time needed to obtain AMHs, consider which patients would most likely benefit from PSPTs, and use a measure of value (eg, number of history errors prevented/dollar spent).
In summary, we found that PSPTs can obtain AMHs for similar cost to pharmacists. It will be especially important to know whether PSPTs maintain the accuracy documented in prior studies.8-9 If that continues to be the case, we expect our findings to allow many hospitals to implement programs using PSPTs to obtain accurate AMHs.
Acknowledgment
The authors thank Katherine M. Abdel-Razek for her role in data collection.
Disclosure
This research was supported by NIH/National Center for Advancing Translational Science UCLA CTSI Grant Number KL2TR000122 and National Institute on Aging Grant Number K23 AG049181-01 (Pevnick). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The investigators retained full independence in the conduct of this research.
1. Mergenhagen KA, Blum SS, Kugler A, et al. Pharmacist- versus physician-initiated admission medication reconciliation: impact on adverse drug events. Am J Geriatr Pharmacother. 2012;10(4):242-250. PubMed
2. Mills PR, McGuffie AC. Formal medication reconciliation within the emergency department reduces the medication error rates for emergency admissions. Emerg Med J. 2010;27(12):911-915. PubMed
3. Boockvar KS, LaCorte HC, Giambanco V, Fridman B, Siu A. Medication reconciliation for reducing drug-discrepancy adverse events. Am J Geriatr Pharmacother. 2006;4(3):236-243. PubMed
4. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
5. Lee KP, Hartridge C, Corbett K, Vittinghoff E, Auerbach AD. “Whose job is it, really?” Physicians’, nurses’, and pharmacists’ perspectives on completing inpatient medication reconciliation. J Hosp Med. 2015;10(3):184-186. PubMed
6. Meguerditchian AN, Krotneva S, Reidel K, Huang A, Tamblyn R. Medication reconciliation at admission and discharge: a time and motion study. BMC Health Serv Res. 2013;13:485. PubMed
7. Bureau of Labor Statistics, US Department of Labor, Occupational Employment Statistics, May 2015. Pharmacists and Pharmacy Technicians. http://www.bls.gov/oes/. Accessed July 15, 2016.
8. Johnston R, Saulnier L, Gould O. Best possible medication history in the emergency department: comparing pharmacy technicians and pharmacists. Can J Hosp Pharm. 2010;63(5):359-365. PubMed
9. Pevnick JM, Nguyen CB, Jackevicius CA, et al. Minimizing medication histories errors for patients admitted to the hospital through the emergency department: a three-arm pragmatic randomized controlled trial of adding admission medication history interviews by pharmacists or pharmacist-supervised pharmacy technicians to usual care. J Patient Cent Res Rev. 2015;2:93.
10. Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc. 2011;18(5):704-710. PubMed
11. Schrimpf D, Plotnicki L, Pilz LR. Web-based open source application for the randomization process in clinical trials: RANDI2. Int J Clin Pharmacol Ther. 2010;48(7):465-467. PubMed
12. American Society of Health-System Pharmacists and the American Pharmacists Association. ASHP-APhA medication management in care transitions best practices. http://media.pharmacist.com/practice/ASHP_APhA_MedicationManagementinCareTransitionsBestPracticesReport2_2013.pdf. Accessed January 15, 2016.
13. Kent AJ, Harrington L, Skinner J. Medication reconciliation by a pharmacist in the emergency department: a pilot project. Can J Hosp Pharm. 2009;62(3):238-242. PubMed
14. Sen S, Siemianowski L, Murphy M, McAllister SC. Implementation of a pharmacy technician-centered medication reconciliation program at an urban teaching medical center. Am J Health Syst Pharm. 2014;71(1):51-56. PubMed
Using pharmacists to obtain admission medication histories (AMHs) reduces medication errors by 70% to 83% and resultant adverse drug events (ADEs) by 15%.1-3 Dissemination of this practice has been limited by several factors, including clinician practice models, staff availability, confusion in provider roles and accountability, and absence of standardized best practices.4-5 This paper assesses one of these barriers: the high cost of utilizing pharmacists. Third-person observer time and motion analysis shows that pharmacists require 46 and 92 minutes to obtain AMHs from medical and geriatric patients,6 respectively, resulting in pharmacist costs of $44 to $88 per patient, based on 2015 US Bureau of Labor Statistics (BLS) hourly wage data for pharmacists ($57.34).7
Ph
METHODS
This study originated as part of a randomized, controlled trial conducted during January-February 2014 at Cedars-Sinai Medical Center (CSMC), an 896-bed, university-affiliated, not-for-profit hospital.9 Pharmacy staff included pharmacists, PGY-1 pharmacy residents, and pharmacy technicians, each of whom received standardized didactic and experiential training (Appendix 1).
The pharmacists’ AMH and general pharmacy experience ranged from <1 to 3 years and <1 to 5 years, respectively. For PSPTs, AMH and general pharmacy experience ranged from <1 to 2 years and 1 to 17 years, respectively. Three additional pharmacists were involved in supervising PSPTs, and their experience fell within the aforementioned ranges, except for one pharmacist with general pharmacy experience of 16 years. The CSMC Institutional Review Board approved this study with oral consent from pharmacy staff.
For the trial, pharmacists and PSPTs obtained AMHs from 185 patients identified as high-risk for ADEs in the CSMC Emergency Department (ED). Patients were randomized into each arm using RANDI2 software11 if they met one of the trial inclusion criteria, accessed via electronic health record (EHR) (Appendix 2). For several days during this trial, a trained research nurse shadowed pharmacists and PSPTs to record tasks performed, as well as the actual time, including start and end times, dedicated to each task.
After excluding AMHs with incomplete data, we calculated mean AMH times and component task times (Table). We compared mean times for pharmacists and PSPTs using two sample t tests (Table). We calculated mean times of tasks across only AMHs that required the task, mean times of tasks across all AMHs studied, regardless of whether the AMH required the task or not (assigning 0 minutes for the task if it was not required), and percent mean time of task per patient for providers combined (Table).
We calculated Pearson product-moment correlation estimates between AMH time and these continuous variables: patient age; total number of EHR medications; number of chronic EHR medications; years of provider AMH experience; and years of provider general pharmacy experience. Using two sample t tests, we also checked for associations between AMH time and the following categorical variables: sex; presence of a patient-provided medication list; caregiver availability; and altered mental status, as determined by review of the ED physician’s note. Caregiver availability was defined as the availability of a family member, caregiver, or medication administration record (MAR) for patients residing at a skilled nursing facility (SNF). The rationale for combining these variables is that SNF nurses are the primary caregivers responsible for administering medications, and the MAR is reflective of their actions.
After reviewing our initial data, we decided to increase our sample size from 20 to 30 complete AMHs. Because the trial had concluded, we selected 10 additional patients who met trial criteria and who would already have an AMH obtained by pharmacy staff for operational reasons. The only difference with the second set of patients (n = 10) is that we did not randomize patients into each arm, but chose to focus on AMHs obtained by PSPTs, as there is a greater need in the literature to study PSPTs. After finalizing data collection, the aforementioned analyses were conducted on the complete data set.
Lastly, we estimated the mean labor cost for pharmacists and PSPTs to obtain an AMH by using 2015 US BLS hourly wage data for pharmacists ($57.34) and pharmacy technicians ($15.23).7 The cost for a pharmacist-obtained AMH was calculated by multiplying the measured mean time a pharmacist needed to obtain an AMH by $57.34 per hour. The cost for a PSPT-obtained AMH was the sum of the PSPT’s measured mean time to obtain an AMH multiplied by $15.23 per hour and the measured mean pharmacist supervisory time multiplied by $57.34 per hour.
RESULTS
Of the 37 observed AMHs, 30 had complete data. Seven AMHs were excluded because not all task times were recorded, due to the schedule restraints of the research nurse. Pharmacists and PSPTs obtained 12 and 18 AMHs, respectively. Mean patient ages were 83.3 (95% confidence interval [CI], 77.3-89.2) and 79.8 (95% CI, 71.5-88.0), for pharmacists and PSPTs, respectively (P = 0.55). Patient’s EHRs contained a mean of 14.3 (95% CI, 11.2-17.5) and 16.3 (95% CI, 13.2-19.5) medications, prior to pharmacists and PSPTs obtaining an AMH, respectively (P = 0.41).
The mean time pharmacists and PSPTs needed to obtain an AMH was 58.5 (95% CI, 46.9-70.1) and 79.4 (95% CI, 59.1-99.8) minutes, respectively (P = 0.14). Summary time data per provider is reported in the Figure. The mean time for pharmacist supervision of technicians was 26 (95% CI, 14.9-37.1) minutes. Mean times of tasks and comparisons of these means times between providers are reported in the Table. The percent mean time for each task per patient for providers combined is also reported in the Table, in which utilizing the EHR was associated with the greatest percentage of time spent at 42.8% (95% CI, 37.4-48.2).
In the 18 cases for which a caregiver (or SNF medication list) was available, providers needed only 58.1 (95% CI, 44.1-72.1) minutes to obtain an AMH, as compared with 90.5 (95% CI, 67.9-113.1) minutes for the 12 cases lacking these resources (P = 0.02). We also found that among PSPTs, years of AMH experience were positively correlated with AMH time (coefficient of correlation 0.49, P = 0.04). No other studied variables were correlated with or associated with differential AMH times.
We estimated mean labor costs for pharmacists and PSPTs to obtain AMHs as $55.91 (95% CI, 44.9-67.0) and $45.00 (95% CI, 29.7-60.4) per patient, respectively (P = 0.32). In the latter case, $24.85 (95% CI, 14.3-35.4) of the $45.00 would be needed for pharmacist supervisory time. The labor cost for a PSPT-obtained AMH ($45.00) was the sum of the PSPT’s mean time (79.4 minutes) multiplied by technician wage data ($15.23/hour) and supervising pharmacist’s mean time (26.0 minutes) multiplied by pharmacist wage data ($57.34/hour).
DISCUSSION
Although limited by sample size, we observed no difference in time or costs of obtaining AMHs between pharmacists and PSPTs. Several prior studies reported that pharmacists and technicians needed less time to obtain AMHs (20-40 minutes), as compared with our findings.12-14 However, most prior studies used younger, healthier patients. Additionally, they used clinician self-reporting instead of third-person observer time and motion methodology. Indeed, the pharmacist times we observed in this study were consistent with prior findings6 that used accepted third-person observer time and motion methodology.10
We observed more variation in time to obtain AMHs among PSPTs than among pharmacists. While variation may be at least in part to the greater number of technicians studied, variation also points to the need for training and oversight of PSPTs. Selection of PSPTs with prior experience interacting with patients and functioning with higher levels of autonomy, standardized training of PSPTs, and consistent dedication of trained PSPTs to AMH functions to maintain their skills, may help to minimize such variation.
Limitations include the use of a single center and a small sample size. As such, the study may be underpowered to demonstrate statistically significant differences between providers. Furthermore, 7 AMHs (19%) had to be excluded because complete task times were missing. This was exclusively because the workday of the research nurse ended before the AMH had been completed. Another limitation was that the tasks observed could have been dissected further to identify even more specific factors that could be targeted to decrease AMH times. We recommend that future studies be larger, investigate in more depth various factors associated with time needed to obtain AMHs, consider which patients would most likely benefit from PSPTs, and use a measure of value (eg, number of history errors prevented/dollar spent).
In summary, we found that PSPTs can obtain AMHs for similar cost to pharmacists. It will be especially important to know whether PSPTs maintain the accuracy documented in prior studies.8-9 If that continues to be the case, we expect our findings to allow many hospitals to implement programs using PSPTs to obtain accurate AMHs.
Acknowledgment
The authors thank Katherine M. Abdel-Razek for her role in data collection.
Disclosure
This research was supported by NIH/National Center for Advancing Translational Science UCLA CTSI Grant Number KL2TR000122 and National Institute on Aging Grant Number K23 AG049181-01 (Pevnick). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The investigators retained full independence in the conduct of this research.
Using pharmacists to obtain admission medication histories (AMHs) reduces medication errors by 70% to 83% and resultant adverse drug events (ADEs) by 15%.1-3 Dissemination of this practice has been limited by several factors, including clinician practice models, staff availability, confusion in provider roles and accountability, and absence of standardized best practices.4-5 This paper assesses one of these barriers: the high cost of utilizing pharmacists. Third-person observer time and motion analysis shows that pharmacists require 46 and 92 minutes to obtain AMHs from medical and geriatric patients,6 respectively, resulting in pharmacist costs of $44 to $88 per patient, based on 2015 US Bureau of Labor Statistics (BLS) hourly wage data for pharmacists ($57.34).7
Ph
METHODS
This study originated as part of a randomized, controlled trial conducted during January-February 2014 at Cedars-Sinai Medical Center (CSMC), an 896-bed, university-affiliated, not-for-profit hospital.9 Pharmacy staff included pharmacists, PGY-1 pharmacy residents, and pharmacy technicians, each of whom received standardized didactic and experiential training (Appendix 1).
The pharmacists’ AMH and general pharmacy experience ranged from <1 to 3 years and <1 to 5 years, respectively. For PSPTs, AMH and general pharmacy experience ranged from <1 to 2 years and 1 to 17 years, respectively. Three additional pharmacists were involved in supervising PSPTs, and their experience fell within the aforementioned ranges, except for one pharmacist with general pharmacy experience of 16 years. The CSMC Institutional Review Board approved this study with oral consent from pharmacy staff.
For the trial, pharmacists and PSPTs obtained AMHs from 185 patients identified as high-risk for ADEs in the CSMC Emergency Department (ED). Patients were randomized into each arm using RANDI2 software11 if they met one of the trial inclusion criteria, accessed via electronic health record (EHR) (Appendix 2). For several days during this trial, a trained research nurse shadowed pharmacists and PSPTs to record tasks performed, as well as the actual time, including start and end times, dedicated to each task.
After excluding AMHs with incomplete data, we calculated mean AMH times and component task times (Table). We compared mean times for pharmacists and PSPTs using two sample t tests (Table). We calculated mean times of tasks across only AMHs that required the task, mean times of tasks across all AMHs studied, regardless of whether the AMH required the task or not (assigning 0 minutes for the task if it was not required), and percent mean time of task per patient for providers combined (Table).
We calculated Pearson product-moment correlation estimates between AMH time and these continuous variables: patient age; total number of EHR medications; number of chronic EHR medications; years of provider AMH experience; and years of provider general pharmacy experience. Using two sample t tests, we also checked for associations between AMH time and the following categorical variables: sex; presence of a patient-provided medication list; caregiver availability; and altered mental status, as determined by review of the ED physician’s note. Caregiver availability was defined as the availability of a family member, caregiver, or medication administration record (MAR) for patients residing at a skilled nursing facility (SNF). The rationale for combining these variables is that SNF nurses are the primary caregivers responsible for administering medications, and the MAR is reflective of their actions.
After reviewing our initial data, we decided to increase our sample size from 20 to 30 complete AMHs. Because the trial had concluded, we selected 10 additional patients who met trial criteria and who would already have an AMH obtained by pharmacy staff for operational reasons. The only difference with the second set of patients (n = 10) is that we did not randomize patients into each arm, but chose to focus on AMHs obtained by PSPTs, as there is a greater need in the literature to study PSPTs. After finalizing data collection, the aforementioned analyses were conducted on the complete data set.
Lastly, we estimated the mean labor cost for pharmacists and PSPTs to obtain an AMH by using 2015 US BLS hourly wage data for pharmacists ($57.34) and pharmacy technicians ($15.23).7 The cost for a pharmacist-obtained AMH was calculated by multiplying the measured mean time a pharmacist needed to obtain an AMH by $57.34 per hour. The cost for a PSPT-obtained AMH was the sum of the PSPT’s measured mean time to obtain an AMH multiplied by $15.23 per hour and the measured mean pharmacist supervisory time multiplied by $57.34 per hour.
RESULTS
Of the 37 observed AMHs, 30 had complete data. Seven AMHs were excluded because not all task times were recorded, due to the schedule restraints of the research nurse. Pharmacists and PSPTs obtained 12 and 18 AMHs, respectively. Mean patient ages were 83.3 (95% confidence interval [CI], 77.3-89.2) and 79.8 (95% CI, 71.5-88.0), for pharmacists and PSPTs, respectively (P = 0.55). Patient’s EHRs contained a mean of 14.3 (95% CI, 11.2-17.5) and 16.3 (95% CI, 13.2-19.5) medications, prior to pharmacists and PSPTs obtaining an AMH, respectively (P = 0.41).
The mean time pharmacists and PSPTs needed to obtain an AMH was 58.5 (95% CI, 46.9-70.1) and 79.4 (95% CI, 59.1-99.8) minutes, respectively (P = 0.14). Summary time data per provider is reported in the Figure. The mean time for pharmacist supervision of technicians was 26 (95% CI, 14.9-37.1) minutes. Mean times of tasks and comparisons of these means times between providers are reported in the Table. The percent mean time for each task per patient for providers combined is also reported in the Table, in which utilizing the EHR was associated with the greatest percentage of time spent at 42.8% (95% CI, 37.4-48.2).
In the 18 cases for which a caregiver (or SNF medication list) was available, providers needed only 58.1 (95% CI, 44.1-72.1) minutes to obtain an AMH, as compared with 90.5 (95% CI, 67.9-113.1) minutes for the 12 cases lacking these resources (P = 0.02). We also found that among PSPTs, years of AMH experience were positively correlated with AMH time (coefficient of correlation 0.49, P = 0.04). No other studied variables were correlated with or associated with differential AMH times.
We estimated mean labor costs for pharmacists and PSPTs to obtain AMHs as $55.91 (95% CI, 44.9-67.0) and $45.00 (95% CI, 29.7-60.4) per patient, respectively (P = 0.32). In the latter case, $24.85 (95% CI, 14.3-35.4) of the $45.00 would be needed for pharmacist supervisory time. The labor cost for a PSPT-obtained AMH ($45.00) was the sum of the PSPT’s mean time (79.4 minutes) multiplied by technician wage data ($15.23/hour) and supervising pharmacist’s mean time (26.0 minutes) multiplied by pharmacist wage data ($57.34/hour).
DISCUSSION
Although limited by sample size, we observed no difference in time or costs of obtaining AMHs between pharmacists and PSPTs. Several prior studies reported that pharmacists and technicians needed less time to obtain AMHs (20-40 minutes), as compared with our findings.12-14 However, most prior studies used younger, healthier patients. Additionally, they used clinician self-reporting instead of third-person observer time and motion methodology. Indeed, the pharmacist times we observed in this study were consistent with prior findings6 that used accepted third-person observer time and motion methodology.10
We observed more variation in time to obtain AMHs among PSPTs than among pharmacists. While variation may be at least in part to the greater number of technicians studied, variation also points to the need for training and oversight of PSPTs. Selection of PSPTs with prior experience interacting with patients and functioning with higher levels of autonomy, standardized training of PSPTs, and consistent dedication of trained PSPTs to AMH functions to maintain their skills, may help to minimize such variation.
Limitations include the use of a single center and a small sample size. As such, the study may be underpowered to demonstrate statistically significant differences between providers. Furthermore, 7 AMHs (19%) had to be excluded because complete task times were missing. This was exclusively because the workday of the research nurse ended before the AMH had been completed. Another limitation was that the tasks observed could have been dissected further to identify even more specific factors that could be targeted to decrease AMH times. We recommend that future studies be larger, investigate in more depth various factors associated with time needed to obtain AMHs, consider which patients would most likely benefit from PSPTs, and use a measure of value (eg, number of history errors prevented/dollar spent).
In summary, we found that PSPTs can obtain AMHs for similar cost to pharmacists. It will be especially important to know whether PSPTs maintain the accuracy documented in prior studies.8-9 If that continues to be the case, we expect our findings to allow many hospitals to implement programs using PSPTs to obtain accurate AMHs.
Acknowledgment
The authors thank Katherine M. Abdel-Razek for her role in data collection.
Disclosure
This research was supported by NIH/National Center for Advancing Translational Science UCLA CTSI Grant Number KL2TR000122 and National Institute on Aging Grant Number K23 AG049181-01 (Pevnick). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The investigators retained full independence in the conduct of this research.
1. Mergenhagen KA, Blum SS, Kugler A, et al. Pharmacist- versus physician-initiated admission medication reconciliation: impact on adverse drug events. Am J Geriatr Pharmacother. 2012;10(4):242-250. PubMed
2. Mills PR, McGuffie AC. Formal medication reconciliation within the emergency department reduces the medication error rates for emergency admissions. Emerg Med J. 2010;27(12):911-915. PubMed
3. Boockvar KS, LaCorte HC, Giambanco V, Fridman B, Siu A. Medication reconciliation for reducing drug-discrepancy adverse events. Am J Geriatr Pharmacother. 2006;4(3):236-243. PubMed
4. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
5. Lee KP, Hartridge C, Corbett K, Vittinghoff E, Auerbach AD. “Whose job is it, really?” Physicians’, nurses’, and pharmacists’ perspectives on completing inpatient medication reconciliation. J Hosp Med. 2015;10(3):184-186. PubMed
6. Meguerditchian AN, Krotneva S, Reidel K, Huang A, Tamblyn R. Medication reconciliation at admission and discharge: a time and motion study. BMC Health Serv Res. 2013;13:485. PubMed
7. Bureau of Labor Statistics, US Department of Labor, Occupational Employment Statistics, May 2015. Pharmacists and Pharmacy Technicians. http://www.bls.gov/oes/. Accessed July 15, 2016.
8. Johnston R, Saulnier L, Gould O. Best possible medication history in the emergency department: comparing pharmacy technicians and pharmacists. Can J Hosp Pharm. 2010;63(5):359-365. PubMed
9. Pevnick JM, Nguyen CB, Jackevicius CA, et al. Minimizing medication histories errors for patients admitted to the hospital through the emergency department: a three-arm pragmatic randomized controlled trial of adding admission medication history interviews by pharmacists or pharmacist-supervised pharmacy technicians to usual care. J Patient Cent Res Rev. 2015;2:93.
10. Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc. 2011;18(5):704-710. PubMed
11. Schrimpf D, Plotnicki L, Pilz LR. Web-based open source application for the randomization process in clinical trials: RANDI2. Int J Clin Pharmacol Ther. 2010;48(7):465-467. PubMed
12. American Society of Health-System Pharmacists and the American Pharmacists Association. ASHP-APhA medication management in care transitions best practices. http://media.pharmacist.com/practice/ASHP_APhA_MedicationManagementinCareTransitionsBestPracticesReport2_2013.pdf. Accessed January 15, 2016.
13. Kent AJ, Harrington L, Skinner J. Medication reconciliation by a pharmacist in the emergency department: a pilot project. Can J Hosp Pharm. 2009;62(3):238-242. PubMed
14. Sen S, Siemianowski L, Murphy M, McAllister SC. Implementation of a pharmacy technician-centered medication reconciliation program at an urban teaching medical center. Am J Health Syst Pharm. 2014;71(1):51-56. PubMed
1. Mergenhagen KA, Blum SS, Kugler A, et al. Pharmacist- versus physician-initiated admission medication reconciliation: impact on adverse drug events. Am J Geriatr Pharmacother. 2012;10(4):242-250. PubMed
2. Mills PR, McGuffie AC. Formal medication reconciliation within the emergency department reduces the medication error rates for emergency admissions. Emerg Med J. 2010;27(12):911-915. PubMed
3. Boockvar KS, LaCorte HC, Giambanco V, Fridman B, Siu A. Medication reconciliation for reducing drug-discrepancy adverse events. Am J Geriatr Pharmacother. 2006;4(3):236-243. PubMed
4. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
5. Lee KP, Hartridge C, Corbett K, Vittinghoff E, Auerbach AD. “Whose job is it, really?” Physicians’, nurses’, and pharmacists’ perspectives on completing inpatient medication reconciliation. J Hosp Med. 2015;10(3):184-186. PubMed
6. Meguerditchian AN, Krotneva S, Reidel K, Huang A, Tamblyn R. Medication reconciliation at admission and discharge: a time and motion study. BMC Health Serv Res. 2013;13:485. PubMed
7. Bureau of Labor Statistics, US Department of Labor, Occupational Employment Statistics, May 2015. Pharmacists and Pharmacy Technicians. http://www.bls.gov/oes/. Accessed July 15, 2016.
8. Johnston R, Saulnier L, Gould O. Best possible medication history in the emergency department: comparing pharmacy technicians and pharmacists. Can J Hosp Pharm. 2010;63(5):359-365. PubMed
9. Pevnick JM, Nguyen CB, Jackevicius CA, et al. Minimizing medication histories errors for patients admitted to the hospital through the emergency department: a three-arm pragmatic randomized controlled trial of adding admission medication history interviews by pharmacists or pharmacist-supervised pharmacy technicians to usual care. J Patient Cent Res Rev. 2015;2:93.
10. Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc. 2011;18(5):704-710. PubMed
11. Schrimpf D, Plotnicki L, Pilz LR. Web-based open source application for the randomization process in clinical trials: RANDI2. Int J Clin Pharmacol Ther. 2010;48(7):465-467. PubMed
12. American Society of Health-System Pharmacists and the American Pharmacists Association. ASHP-APhA medication management in care transitions best practices. http://media.pharmacist.com/practice/ASHP_APhA_MedicationManagementinCareTransitionsBestPracticesReport2_2013.pdf. Accessed January 15, 2016.
13. Kent AJ, Harrington L, Skinner J. Medication reconciliation by a pharmacist in the emergency department: a pilot project. Can J Hosp Pharm. 2009;62(3):238-242. PubMed
14. Sen S, Siemianowski L, Murphy M, McAllister SC. Implementation of a pharmacy technician-centered medication reconciliation program at an urban teaching medical center. Am J Health Syst Pharm. 2014;71(1):51-56. PubMed
© 2017 Society of Hospital Medicine
Nondirected testing for inpatients with severe liver injury
The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/
CASE REPORT
A 68-year-old woman with ischemic cardiomyopathy was admitted with abdominal cramping, diarrhea, and nausea, which had left her unable to keep food and liquids down for 2 days. She had been taking diuretics and had a remote history of intravenous drug use. On admission, she was afebrile and had blood pressure of 100/60 mm Hg and a heart rate of 100 bpm. Her extremities were cool and clammy. Blood test results showed an alanine aminotransferase (ALT) level of 1510 IU/L and an aspartate aminotransferase (AST) level of 1643 IU/L. The patient’s clinician did not know her baseline ALT and AST levels and thought the best approach was to identify the cause of the transaminase elevation.
Severe acute liver injury (liver enzymes, >10 × upper limit of normal [ULN], usually 40 IU/L) is a common presentation among hospitalized patients. Between 1997 and 2015, 1.5% of patients admitted to our hospital had severe liver injury. In another large cohort of hospitalized patients,1 0.6% had an ALT level higher than 1000 IU/L (~20 × ULN). A precise diagnosis is often needed to direct appropriate therapy, and serologic tests are available for many conditions, both common and rare (Table). Given the relative ease of bundled blood testing, nondirected testing has emerged as a popular, if reflexive, strategy.2-5 In this approach, clinicians evaluate each patient for the set of testable diseases all at once—in contrast to taking a directed, stepwise testing approach guided by the patient’s history.
Use of nondirected testing is common in patients with severe acute liver injury. Of the 5795 such patients treated at our hospital between 2000 and 2015, within the same day of service 53% were tested for hepatitis C virus antibody, 38% for hemochromatosis (ferritin test), 28% for autoimmune hepatitis (antinuclear antibody test), and 15% for primary biliary cholangitis (antimitochondrial antibody test) by our clinical laboratory. Of the 5023 patients who had send-out tests performed for Wilson disease (ceruloplasmin), 81% were queried for hepatitis B virus infection, 76% for hepatitis C virus infection, 75% for autoimmune hepatitis, and 73.1% for hemochromatosis.2 Similar trends were found for patients with severe liver injury tested for α1-antitrypsin (AAT) deficiency.3 In sum, these data showed that each patient with severe liver injury was tested out of concern about diseases with markedly different epidemiology and clinical presentations (Table).
WHY YOU MIGHT THINK NONDIRECTED TESTING IS HELPFUL
Use of nondirected testing may reflect perceived urgency, convenience, and thoroughness.2-6 Alternatively, it may simply involve following a consultant’s recommendations.4 As severe acute liver injury is often associated with tremendous morbidity, clinicians seeking answers may perceive directed, stepwise testing as inappropriately slow given the urgency of the presentation; they may think that nondirected testing can reduce hospital length of stay.
WHY NONDIRECTED TESTING IS NOT HELPFUL
Nondirected testing is a problem for at least 4 reasons: limited benefit of reflexive testing for rare diseases, no meaningful impact on outcomes, false positives, and financial cost.
First, immediately testing for rare causes of liver disease is unlikely to benefit patients with severe liver injury. The underlying etiologies of severe liver injury are relatively well circumscribed (Table). Overall, 42% of patients with severe liver injury and 57% of those with an ALT level higher than 1000 IU/L have ischemic hepatitis.7 Accounting for a significant percentage of severe liver injury cases are acute biliary obstruction (24%), drug-induced injury (10%-13%), and viral hepatitis (4%-7%).1,8 Of the small subset of patients with severe liver injury that progresses to acute liver failure (ALF; encephalopathy, coagulopathy), 0.5% have autoimmune hepatitis and 0.1% have Wilson disease.9 Furthermore, many patients are tested for AAT deficiency, hemochromatosis, and primary biliary cholangitis, but these are never causes of severe acute liver injury (Table).
Second, diagnosing a rarer cause of acute liver injury modestly earlier has no meaningful impact on outcome. Work-up for more common etiologies can usually be completed within 2 or 3 days. This is true even for patients with ALF. Specific therapies generally are lacking for ALF, save for use of N-acetylcysteine for acetaminophen overdose and antiviral therapy for hepatitis B virus infection.9,10 Furthermore, although effective therapies are available for both autoimmune hepatitis and Wilson disease, the potential benefit stems from altering the longer term course of disease. Initial management, even for these rare conditions, is no different from that for other etiologies. Conversely, acute liver injury caused by ischemic hepatitis, biliary disease, or drug-induced liver injury requires swift corrective action. Even if normotensive, patients with ischemic hepatitis are often in cardiogenic shock and benefit from careful monitoring and critical care.7 Patients with acute biliary obstruction may need therapeutic endoscopy. Last, patients with drug-induced liver injury benefit from immediate discontinuation of the offending drug.
Third, in the testing of patients with low pretest probabilities, false positives are common. For example, at our institution and at an institution in Austria, severe liver injury patients with a low ceruloplasmin level have a 95.1% to 98.1% chance of a false-positive result (they have a low ceruloplasmin level but do not have Wilson disease).3,4 Furthermore, 91% of positive tests are never confirmed,3 indicating either that clinicians never valued the initial test or that other diagnoses were much more likely. Even worse, as was the case in 65% of patients with low AAT levels,2,3 genetic diagnoses were based on unconfirmed, potentially false-positive serologic tests.
Fourth, although the financial cost for each individual test is small, at the population level the cost of nondirected testing is significant. For example, although each reflects testing for conditions that do not cause acute liver injury, the costs of ferritin, AAT, and antimitochondrial antibody tests are $13, $16, and $37, respectively (Medicare/Medicaid reimbursements in 2016 $US).11 About 1.5% of admitted patients are found to have severe liver injury. If this proportion holds true for the roughly 40 million discharges from US hospitals each year, then there would be an annual cost of about $40 million if all 3 tests were performed for each patient with severe liver injury. In addition, although nondirected testing may seem clinically expedient, there are no data suggesting it reduces length of stay. In fact, ceruloplasmin, AAT, and many other tests are sent to external laboratories and are unlikely to be returned before discharge. If clinicians delay discharge for results, then nondirected testing would increase rather than decrease length of stay.
WHAT YOU SHOULD DO INSTEAD
In this era of increasing cost-consciousness, nondirected testing has escaped relatively unscathed. Indeed, nondirected testing is prevalent, yet has pitfalls similar to those of serologic testing (eg, vasculitis or arthritis,6 acute renal injury, infectious disease12). The alternative is deliberate, empirical, patient-centered testing that is attentive to the patient’s presentation and the harms of false positives. The idea is to select tests for each patient with acute liver injury according to presentation and the most likely corresponding diagnoses (Table, Figure).
The “one-stop shopping” in providers’ electronic order entry systems makes it too easy to over-order tests. Fortunately, these systems’ simple and effective decision supports can force pauses in the ordering process, create barriers to waste, and provide education about test characteristics and costs.4,5,13 Our medical center’s volume of ceruloplasmin orders decreased by 80% after a change was made to its ordering system; the ordering of a ceruloplasmin test is now interrupted by a pop-up screen that displays test characteristics and an option to continue or cancel the order.4,5 Hospitals should consider implementing clinical decision supports in this area. Successful interventions provide electronic rather than paper-based support as part of the clinical workflow, during the ordering process, and recommendations rather than assessments.13
RECOMMENDATIONS
- For each patient with severe acute liver injury, select tests on the basis of the presentation (Figure). Testing for rare diseases should be performed only after common diseases have been excluded.
- Avoid testing for hemochromatosis (iron indices, genetic tests), AAT deficiency (AAT levels or phenotypes), and primary biliary cholangitis (antimitochondrial antibodies) in patients with severe acute liver injury.
- Consider implementing decision supports that can curb nondirected testing in areas in which it is common.
CONCLUSION
Nondirected testing is associated with false positives and increased costs in the evaluation and management of severe acute liver injury. The alternative is deliberate, epidemiologically and clinically driven directed testing. Electronic ordering system decision supports can be useful in curtailing nondirected testing.
Disclosure
Nothing to report.
Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.
1. Johnson RD, O’Connor ML, Kerr RM. Extreme serum elevations of aspartate aminotransferase. Am J Gastroenterol. 1995;90(8):1244-1245. PubMed
2. Tapper EB, Patwardhan VR, Curry M. Low yield and utilization of confirmatory testing in a cohort of patients with liver disease assessed for alpha-1 antitrypsin deficiency. Dig Dis Sci. 2015;60(6):1589-1594. PubMed
3. Tapper EB, Rahni DO, Arnaout R, Lai M. The overuse of serum ceruloplasmin measurement. Am J Med. 2013;126(10):926.e1-e5. PubMed
4. Tapper EB, Sengupta N, Lai M, Horowitz G. Understanding and reducing ceruloplasmin overuse with a decision support intervention for liver disease evaluation. Am J Med. 2016;129(1):115.e17-e22. PubMed
5. Tapper EB, Sengupta N, Lai M, Horowitz G. A decision support tool to reduce overtesting for ceruloplasmin and improve adherence with clinical guidelines. JAMA Intern Med. 2015;175(9):1561-1562. PubMed
6. Lichtenstein MJ, Pincus T. How useful are combinations of blood tests in “rheumatic panels” in diagnosis of rheumatic diseases? J Gen Intern Med. 1988;3(5):435-442. PubMed
7. Tapper EB, Sengupta N, Bonder A. The incidence and outcomes of ischemic hepatitis: a systematic review with meta-analysis. Am J Med. 2015;128(12):1314-1321. PubMed
8. Whitehead MW, Hawkes ND, Hainsworth I, Kingham JG. A prospective study of the causes of notably raised aspartate aminotransferase of liver origin. Gut. 1999;45(1):129-133. PubMed
9. Fontana RJ. Acute liver failure including acetaminophen overdose. Med Clin North Am. 2008;92(4):761-794. PubMed
10. Lee WM, Larson AM, Stravitz RT. AASLD Position Paper: The Management of Acute Liver Failure: Update 2011. American Association for the Study of Liver Diseases website. https://www.aasld.org/sites/default/files/guideline_documents/alfenhanced.pdf. Published 2011. Accessed January 26, 2017.
11. Green RM, Flamm S. AGA technical review on the evaluation of liver chemistry tests. Gastroenterology. 2002;123(4):1367-1384. PubMed
12. Aesif SW, Parenti DM, Lesky L, Keiser JF. A cost-effective interdisciplinary approach to microbiologic send-out test use. Arch Pathol Lab Med. 2015;139(2):194-198. PubMed
13. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765. PubMed
14. Boberg KM. Prevalence and epidemiology of autoimmune hepatitis. Clin Liver Dis. 2002;6(3):635-647. PubMed
15. Bacon BR, Adams PC, Kowdley KV, Powell LW, Tavill AS; American Association for the Study of Liver Diseases. Diagnosis and management of hemochromatosis: 2011 practice guideline by the American Association for the Study of Liver Diseases. Hepatology. 2011;54(1):328-343. PubMed
16. Boonstra K, Beuers U, Ponsioen CY. Epidemiology of primary sclerosing cholangitis and primary biliary cirrhosis: a systematic review. J Hepatol. 2012;56(5):1181-1188. PubMed
The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/
CASE REPORT
A 68-year-old woman with ischemic cardiomyopathy was admitted with abdominal cramping, diarrhea, and nausea, which had left her unable to keep food and liquids down for 2 days. She had been taking diuretics and had a remote history of intravenous drug use. On admission, she was afebrile and had blood pressure of 100/60 mm Hg and a heart rate of 100 bpm. Her extremities were cool and clammy. Blood test results showed an alanine aminotransferase (ALT) level of 1510 IU/L and an aspartate aminotransferase (AST) level of 1643 IU/L. The patient’s clinician did not know her baseline ALT and AST levels and thought the best approach was to identify the cause of the transaminase elevation.
Severe acute liver injury (liver enzymes, >10 × upper limit of normal [ULN], usually 40 IU/L) is a common presentation among hospitalized patients. Between 1997 and 2015, 1.5% of patients admitted to our hospital had severe liver injury. In another large cohort of hospitalized patients,1 0.6% had an ALT level higher than 1000 IU/L (~20 × ULN). A precise diagnosis is often needed to direct appropriate therapy, and serologic tests are available for many conditions, both common and rare (Table). Given the relative ease of bundled blood testing, nondirected testing has emerged as a popular, if reflexive, strategy.2-5 In this approach, clinicians evaluate each patient for the set of testable diseases all at once—in contrast to taking a directed, stepwise testing approach guided by the patient’s history.
Use of nondirected testing is common in patients with severe acute liver injury. Of the 5795 such patients treated at our hospital between 2000 and 2015, within the same day of service 53% were tested for hepatitis C virus antibody, 38% for hemochromatosis (ferritin test), 28% for autoimmune hepatitis (antinuclear antibody test), and 15% for primary biliary cholangitis (antimitochondrial antibody test) by our clinical laboratory. Of the 5023 patients who had send-out tests performed for Wilson disease (ceruloplasmin), 81% were queried for hepatitis B virus infection, 76% for hepatitis C virus infection, 75% for autoimmune hepatitis, and 73.1% for hemochromatosis.2 Similar trends were found for patients with severe liver injury tested for α1-antitrypsin (AAT) deficiency.3 In sum, these data showed that each patient with severe liver injury was tested out of concern about diseases with markedly different epidemiology and clinical presentations (Table).
WHY YOU MIGHT THINK NONDIRECTED TESTING IS HELPFUL
Use of nondirected testing may reflect perceived urgency, convenience, and thoroughness.2-6 Alternatively, it may simply involve following a consultant’s recommendations.4 As severe acute liver injury is often associated with tremendous morbidity, clinicians seeking answers may perceive directed, stepwise testing as inappropriately slow given the urgency of the presentation; they may think that nondirected testing can reduce hospital length of stay.
WHY NONDIRECTED TESTING IS NOT HELPFUL
Nondirected testing is a problem for at least 4 reasons: limited benefit of reflexive testing for rare diseases, no meaningful impact on outcomes, false positives, and financial cost.
First, immediately testing for rare causes of liver disease is unlikely to benefit patients with severe liver injury. The underlying etiologies of severe liver injury are relatively well circumscribed (Table). Overall, 42% of patients with severe liver injury and 57% of those with an ALT level higher than 1000 IU/L have ischemic hepatitis.7 Accounting for a significant percentage of severe liver injury cases are acute biliary obstruction (24%), drug-induced injury (10%-13%), and viral hepatitis (4%-7%).1,8 Of the small subset of patients with severe liver injury that progresses to acute liver failure (ALF; encephalopathy, coagulopathy), 0.5% have autoimmune hepatitis and 0.1% have Wilson disease.9 Furthermore, many patients are tested for AAT deficiency, hemochromatosis, and primary biliary cholangitis, but these are never causes of severe acute liver injury (Table).
Second, diagnosing a rarer cause of acute liver injury modestly earlier has no meaningful impact on outcome. Work-up for more common etiologies can usually be completed within 2 or 3 days. This is true even for patients with ALF. Specific therapies generally are lacking for ALF, save for use of N-acetylcysteine for acetaminophen overdose and antiviral therapy for hepatitis B virus infection.9,10 Furthermore, although effective therapies are available for both autoimmune hepatitis and Wilson disease, the potential benefit stems from altering the longer term course of disease. Initial management, even for these rare conditions, is no different from that for other etiologies. Conversely, acute liver injury caused by ischemic hepatitis, biliary disease, or drug-induced liver injury requires swift corrective action. Even if normotensive, patients with ischemic hepatitis are often in cardiogenic shock and benefit from careful monitoring and critical care.7 Patients with acute biliary obstruction may need therapeutic endoscopy. Last, patients with drug-induced liver injury benefit from immediate discontinuation of the offending drug.
Third, in the testing of patients with low pretest probabilities, false positives are common. For example, at our institution and at an institution in Austria, severe liver injury patients with a low ceruloplasmin level have a 95.1% to 98.1% chance of a false-positive result (they have a low ceruloplasmin level but do not have Wilson disease).3,4 Furthermore, 91% of positive tests are never confirmed,3 indicating either that clinicians never valued the initial test or that other diagnoses were much more likely. Even worse, as was the case in 65% of patients with low AAT levels,2,3 genetic diagnoses were based on unconfirmed, potentially false-positive serologic tests.
Fourth, although the financial cost for each individual test is small, at the population level the cost of nondirected testing is significant. For example, although each reflects testing for conditions that do not cause acute liver injury, the costs of ferritin, AAT, and antimitochondrial antibody tests are $13, $16, and $37, respectively (Medicare/Medicaid reimbursements in 2016 $US).11 About 1.5% of admitted patients are found to have severe liver injury. If this proportion holds true for the roughly 40 million discharges from US hospitals each year, then there would be an annual cost of about $40 million if all 3 tests were performed for each patient with severe liver injury. In addition, although nondirected testing may seem clinically expedient, there are no data suggesting it reduces length of stay. In fact, ceruloplasmin, AAT, and many other tests are sent to external laboratories and are unlikely to be returned before discharge. If clinicians delay discharge for results, then nondirected testing would increase rather than decrease length of stay.
WHAT YOU SHOULD DO INSTEAD
In this era of increasing cost-consciousness, nondirected testing has escaped relatively unscathed. Indeed, nondirected testing is prevalent, yet has pitfalls similar to those of serologic testing (eg, vasculitis or arthritis,6 acute renal injury, infectious disease12). The alternative is deliberate, empirical, patient-centered testing that is attentive to the patient’s presentation and the harms of false positives. The idea is to select tests for each patient with acute liver injury according to presentation and the most likely corresponding diagnoses (Table, Figure).
The “one-stop shopping” in providers’ electronic order entry systems makes it too easy to over-order tests. Fortunately, these systems’ simple and effective decision supports can force pauses in the ordering process, create barriers to waste, and provide education about test characteristics and costs.4,5,13 Our medical center’s volume of ceruloplasmin orders decreased by 80% after a change was made to its ordering system; the ordering of a ceruloplasmin test is now interrupted by a pop-up screen that displays test characteristics and an option to continue or cancel the order.4,5 Hospitals should consider implementing clinical decision supports in this area. Successful interventions provide electronic rather than paper-based support as part of the clinical workflow, during the ordering process, and recommendations rather than assessments.13
RECOMMENDATIONS
- For each patient with severe acute liver injury, select tests on the basis of the presentation (Figure). Testing for rare diseases should be performed only after common diseases have been excluded.
- Avoid testing for hemochromatosis (iron indices, genetic tests), AAT deficiency (AAT levels or phenotypes), and primary biliary cholangitis (antimitochondrial antibodies) in patients with severe acute liver injury.
- Consider implementing decision supports that can curb nondirected testing in areas in which it is common.
CONCLUSION
Nondirected testing is associated with false positives and increased costs in the evaluation and management of severe acute liver injury. The alternative is deliberate, epidemiologically and clinically driven directed testing. Electronic ordering system decision supports can be useful in curtailing nondirected testing.
Disclosure
Nothing to report.
Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.
The “Things We Do for No Reason” series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/
CASE REPORT
A 68-year-old woman with ischemic cardiomyopathy was admitted with abdominal cramping, diarrhea, and nausea, which had left her unable to keep food and liquids down for 2 days. She had been taking diuretics and had a remote history of intravenous drug use. On admission, she was afebrile and had blood pressure of 100/60 mm Hg and a heart rate of 100 bpm. Her extremities were cool and clammy. Blood test results showed an alanine aminotransferase (ALT) level of 1510 IU/L and an aspartate aminotransferase (AST) level of 1643 IU/L. The patient’s clinician did not know her baseline ALT and AST levels and thought the best approach was to identify the cause of the transaminase elevation.
Severe acute liver injury (liver enzymes, >10 × upper limit of normal [ULN], usually 40 IU/L) is a common presentation among hospitalized patients. Between 1997 and 2015, 1.5% of patients admitted to our hospital had severe liver injury. In another large cohort of hospitalized patients,1 0.6% had an ALT level higher than 1000 IU/L (~20 × ULN). A precise diagnosis is often needed to direct appropriate therapy, and serologic tests are available for many conditions, both common and rare (Table). Given the relative ease of bundled blood testing, nondirected testing has emerged as a popular, if reflexive, strategy.2-5 In this approach, clinicians evaluate each patient for the set of testable diseases all at once—in contrast to taking a directed, stepwise testing approach guided by the patient’s history.
Use of nondirected testing is common in patients with severe acute liver injury. Of the 5795 such patients treated at our hospital between 2000 and 2015, within the same day of service 53% were tested for hepatitis C virus antibody, 38% for hemochromatosis (ferritin test), 28% for autoimmune hepatitis (antinuclear antibody test), and 15% for primary biliary cholangitis (antimitochondrial antibody test) by our clinical laboratory. Of the 5023 patients who had send-out tests performed for Wilson disease (ceruloplasmin), 81% were queried for hepatitis B virus infection, 76% for hepatitis C virus infection, 75% for autoimmune hepatitis, and 73.1% for hemochromatosis.2 Similar trends were found for patients with severe liver injury tested for α1-antitrypsin (AAT) deficiency.3 In sum, these data showed that each patient with severe liver injury was tested out of concern about diseases with markedly different epidemiology and clinical presentations (Table).
WHY YOU MIGHT THINK NONDIRECTED TESTING IS HELPFUL
Use of nondirected testing may reflect perceived urgency, convenience, and thoroughness.2-6 Alternatively, it may simply involve following a consultant’s recommendations.4 As severe acute liver injury is often associated with tremendous morbidity, clinicians seeking answers may perceive directed, stepwise testing as inappropriately slow given the urgency of the presentation; they may think that nondirected testing can reduce hospital length of stay.
WHY NONDIRECTED TESTING IS NOT HELPFUL
Nondirected testing is a problem for at least 4 reasons: limited benefit of reflexive testing for rare diseases, no meaningful impact on outcomes, false positives, and financial cost.
First, immediately testing for rare causes of liver disease is unlikely to benefit patients with severe liver injury. The underlying etiologies of severe liver injury are relatively well circumscribed (Table). Overall, 42% of patients with severe liver injury and 57% of those with an ALT level higher than 1000 IU/L have ischemic hepatitis.7 Accounting for a significant percentage of severe liver injury cases are acute biliary obstruction (24%), drug-induced injury (10%-13%), and viral hepatitis (4%-7%).1,8 Of the small subset of patients with severe liver injury that progresses to acute liver failure (ALF; encephalopathy, coagulopathy), 0.5% have autoimmune hepatitis and 0.1% have Wilson disease.9 Furthermore, many patients are tested for AAT deficiency, hemochromatosis, and primary biliary cholangitis, but these are never causes of severe acute liver injury (Table).
Second, diagnosing a rarer cause of acute liver injury modestly earlier has no meaningful impact on outcome. Work-up for more common etiologies can usually be completed within 2 or 3 days. This is true even for patients with ALF. Specific therapies generally are lacking for ALF, save for use of N-acetylcysteine for acetaminophen overdose and antiviral therapy for hepatitis B virus infection.9,10 Furthermore, although effective therapies are available for both autoimmune hepatitis and Wilson disease, the potential benefit stems from altering the longer term course of disease. Initial management, even for these rare conditions, is no different from that for other etiologies. Conversely, acute liver injury caused by ischemic hepatitis, biliary disease, or drug-induced liver injury requires swift corrective action. Even if normotensive, patients with ischemic hepatitis are often in cardiogenic shock and benefit from careful monitoring and critical care.7 Patients with acute biliary obstruction may need therapeutic endoscopy. Last, patients with drug-induced liver injury benefit from immediate discontinuation of the offending drug.
Third, in the testing of patients with low pretest probabilities, false positives are common. For example, at our institution and at an institution in Austria, severe liver injury patients with a low ceruloplasmin level have a 95.1% to 98.1% chance of a false-positive result (they have a low ceruloplasmin level but do not have Wilson disease).3,4 Furthermore, 91% of positive tests are never confirmed,3 indicating either that clinicians never valued the initial test or that other diagnoses were much more likely. Even worse, as was the case in 65% of patients with low AAT levels,2,3 genetic diagnoses were based on unconfirmed, potentially false-positive serologic tests.
Fourth, although the financial cost for each individual test is small, at the population level the cost of nondirected testing is significant. For example, although each reflects testing for conditions that do not cause acute liver injury, the costs of ferritin, AAT, and antimitochondrial antibody tests are $13, $16, and $37, respectively (Medicare/Medicaid reimbursements in 2016 $US).11 About 1.5% of admitted patients are found to have severe liver injury. If this proportion holds true for the roughly 40 million discharges from US hospitals each year, then there would be an annual cost of about $40 million if all 3 tests were performed for each patient with severe liver injury. In addition, although nondirected testing may seem clinically expedient, there are no data suggesting it reduces length of stay. In fact, ceruloplasmin, AAT, and many other tests are sent to external laboratories and are unlikely to be returned before discharge. If clinicians delay discharge for results, then nondirected testing would increase rather than decrease length of stay.
WHAT YOU SHOULD DO INSTEAD
In this era of increasing cost-consciousness, nondirected testing has escaped relatively unscathed. Indeed, nondirected testing is prevalent, yet has pitfalls similar to those of serologic testing (eg, vasculitis or arthritis,6 acute renal injury, infectious disease12). The alternative is deliberate, empirical, patient-centered testing that is attentive to the patient’s presentation and the harms of false positives. The idea is to select tests for each patient with acute liver injury according to presentation and the most likely corresponding diagnoses (Table, Figure).
The “one-stop shopping” in providers’ electronic order entry systems makes it too easy to over-order tests. Fortunately, these systems’ simple and effective decision supports can force pauses in the ordering process, create barriers to waste, and provide education about test characteristics and costs.4,5,13 Our medical center’s volume of ceruloplasmin orders decreased by 80% after a change was made to its ordering system; the ordering of a ceruloplasmin test is now interrupted by a pop-up screen that displays test characteristics and an option to continue or cancel the order.4,5 Hospitals should consider implementing clinical decision supports in this area. Successful interventions provide electronic rather than paper-based support as part of the clinical workflow, during the ordering process, and recommendations rather than assessments.13
RECOMMENDATIONS
- For each patient with severe acute liver injury, select tests on the basis of the presentation (Figure). Testing for rare diseases should be performed only after common diseases have been excluded.
- Avoid testing for hemochromatosis (iron indices, genetic tests), AAT deficiency (AAT levels or phenotypes), and primary biliary cholangitis (antimitochondrial antibodies) in patients with severe acute liver injury.
- Consider implementing decision supports that can curb nondirected testing in areas in which it is common.
CONCLUSION
Nondirected testing is associated with false positives and increased costs in the evaluation and management of severe acute liver injury. The alternative is deliberate, epidemiologically and clinically driven directed testing. Electronic ordering system decision supports can be useful in curtailing nondirected testing.
Disclosure
Nothing to report.
Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.
1. Johnson RD, O’Connor ML, Kerr RM. Extreme serum elevations of aspartate aminotransferase. Am J Gastroenterol. 1995;90(8):1244-1245. PubMed
2. Tapper EB, Patwardhan VR, Curry M. Low yield and utilization of confirmatory testing in a cohort of patients with liver disease assessed for alpha-1 antitrypsin deficiency. Dig Dis Sci. 2015;60(6):1589-1594. PubMed
3. Tapper EB, Rahni DO, Arnaout R, Lai M. The overuse of serum ceruloplasmin measurement. Am J Med. 2013;126(10):926.e1-e5. PubMed
4. Tapper EB, Sengupta N, Lai M, Horowitz G. Understanding and reducing ceruloplasmin overuse with a decision support intervention for liver disease evaluation. Am J Med. 2016;129(1):115.e17-e22. PubMed
5. Tapper EB, Sengupta N, Lai M, Horowitz G. A decision support tool to reduce overtesting for ceruloplasmin and improve adherence with clinical guidelines. JAMA Intern Med. 2015;175(9):1561-1562. PubMed
6. Lichtenstein MJ, Pincus T. How useful are combinations of blood tests in “rheumatic panels” in diagnosis of rheumatic diseases? J Gen Intern Med. 1988;3(5):435-442. PubMed
7. Tapper EB, Sengupta N, Bonder A. The incidence and outcomes of ischemic hepatitis: a systematic review with meta-analysis. Am J Med. 2015;128(12):1314-1321. PubMed
8. Whitehead MW, Hawkes ND, Hainsworth I, Kingham JG. A prospective study of the causes of notably raised aspartate aminotransferase of liver origin. Gut. 1999;45(1):129-133. PubMed
9. Fontana RJ. Acute liver failure including acetaminophen overdose. Med Clin North Am. 2008;92(4):761-794. PubMed
10. Lee WM, Larson AM, Stravitz RT. AASLD Position Paper: The Management of Acute Liver Failure: Update 2011. American Association for the Study of Liver Diseases website. https://www.aasld.org/sites/default/files/guideline_documents/alfenhanced.pdf. Published 2011. Accessed January 26, 2017.
11. Green RM, Flamm S. AGA technical review on the evaluation of liver chemistry tests. Gastroenterology. 2002;123(4):1367-1384. PubMed
12. Aesif SW, Parenti DM, Lesky L, Keiser JF. A cost-effective interdisciplinary approach to microbiologic send-out test use. Arch Pathol Lab Med. 2015;139(2):194-198. PubMed
13. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765. PubMed
14. Boberg KM. Prevalence and epidemiology of autoimmune hepatitis. Clin Liver Dis. 2002;6(3):635-647. PubMed
15. Bacon BR, Adams PC, Kowdley KV, Powell LW, Tavill AS; American Association for the Study of Liver Diseases. Diagnosis and management of hemochromatosis: 2011 practice guideline by the American Association for the Study of Liver Diseases. Hepatology. 2011;54(1):328-343. PubMed
16. Boonstra K, Beuers U, Ponsioen CY. Epidemiology of primary sclerosing cholangitis and primary biliary cirrhosis: a systematic review. J Hepatol. 2012;56(5):1181-1188. PubMed
1. Johnson RD, O’Connor ML, Kerr RM. Extreme serum elevations of aspartate aminotransferase. Am J Gastroenterol. 1995;90(8):1244-1245. PubMed
2. Tapper EB, Patwardhan VR, Curry M. Low yield and utilization of confirmatory testing in a cohort of patients with liver disease assessed for alpha-1 antitrypsin deficiency. Dig Dis Sci. 2015;60(6):1589-1594. PubMed
3. Tapper EB, Rahni DO, Arnaout R, Lai M. The overuse of serum ceruloplasmin measurement. Am J Med. 2013;126(10):926.e1-e5. PubMed
4. Tapper EB, Sengupta N, Lai M, Horowitz G. Understanding and reducing ceruloplasmin overuse with a decision support intervention for liver disease evaluation. Am J Med. 2016;129(1):115.e17-e22. PubMed
5. Tapper EB, Sengupta N, Lai M, Horowitz G. A decision support tool to reduce overtesting for ceruloplasmin and improve adherence with clinical guidelines. JAMA Intern Med. 2015;175(9):1561-1562. PubMed
6. Lichtenstein MJ, Pincus T. How useful are combinations of blood tests in “rheumatic panels” in diagnosis of rheumatic diseases? J Gen Intern Med. 1988;3(5):435-442. PubMed
7. Tapper EB, Sengupta N, Bonder A. The incidence and outcomes of ischemic hepatitis: a systematic review with meta-analysis. Am J Med. 2015;128(12):1314-1321. PubMed
8. Whitehead MW, Hawkes ND, Hainsworth I, Kingham JG. A prospective study of the causes of notably raised aspartate aminotransferase of liver origin. Gut. 1999;45(1):129-133. PubMed
9. Fontana RJ. Acute liver failure including acetaminophen overdose. Med Clin North Am. 2008;92(4):761-794. PubMed
10. Lee WM, Larson AM, Stravitz RT. AASLD Position Paper: The Management of Acute Liver Failure: Update 2011. American Association for the Study of Liver Diseases website. https://www.aasld.org/sites/default/files/guideline_documents/alfenhanced.pdf. Published 2011. Accessed January 26, 2017.
11. Green RM, Flamm S. AGA technical review on the evaluation of liver chemistry tests. Gastroenterology. 2002;123(4):1367-1384. PubMed
12. Aesif SW, Parenti DM, Lesky L, Keiser JF. A cost-effective interdisciplinary approach to microbiologic send-out test use. Arch Pathol Lab Med. 2015;139(2):194-198. PubMed
13. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765. PubMed
14. Boberg KM. Prevalence and epidemiology of autoimmune hepatitis. Clin Liver Dis. 2002;6(3):635-647. PubMed
15. Bacon BR, Adams PC, Kowdley KV, Powell LW, Tavill AS; American Association for the Study of Liver Diseases. Diagnosis and management of hemochromatosis: 2011 practice guideline by the American Association for the Study of Liver Diseases. Hepatology. 2011;54(1):328-343. PubMed
16. Boonstra K, Beuers U, Ponsioen CY. Epidemiology of primary sclerosing cholangitis and primary biliary cirrhosis: a systematic review. J Hepatol. 2012;56(5):1181-1188. PubMed
© 2017 Society of Hospital Medicine
Judge blocks Texas attempt to defund Planned Parenthood
A federal judge has blocked Texas from withholding Medicaid funds from Planned Parenthood centers in the state, ruling that state officials had no cause to terminate funding for the providers.
In his Feb. 21 decision, Judge Sam Sparks of the U.S. District Court for the Western District of Texas temporarily barred Texas from taking funds from Planned Parenthood while the case proceeds. Restricting the money would deprive Medicaid patients of their right to obtain health care from their chosen providers and would potentially disrupt care for 12,500 Medicaid patients, Judge Sparks wrote.
Texas Attorney General Ken Paxton (R) pledged to appeal the ruling.
“[The] decision is disappointing and flies in the face of basic human decency,” Mr. Paxton said in a statement. “The raw, unedited footage from undercover videos exposed a brazen willingness by Planned Parenthood officials to traffic in fetal body parts, as well as manipulate the timing and method of an abortion ... No taxpayer in Texas should have to subsidize this repugnant and illegal conduct. We should never lose sight of the fact that, as long as abortion is legal in the United States, the potential for these types of horrors will continue.”
Planned Parenthood representatives did not return messages seeking comment on the ruling.
The Texas Health and Human Services Commission (HHSC) terminated funding for its Planned Parenthood providers in the state following a 2015 video by two anti-abortion advocates that purported to show representatives of Planned Parenthood Gulf Coast (Houston) contracting to sell aborted human fetal tissue and body parts. State authorities investigated the facility and found no wrongdoing, but a grand jury indicted the two anti-abortion activists for using fake names and identities.
Separate investigations by the Texas Attorney General’s Office, the Texas Department of State Health Services, and HHSC also found no wrongdoing, according to court documents. In December 2016 however, HHSC sent termination letters to five Texas Planned Parenthood centers, stating that the facilities were not qualified to provide medical services in a “competent, safe, legal and ethical manner” under state and federal law, according to court records. The providers and several patients sued.
In his ruling, Judge Sparks said he found no evidence indicating that an actual program violation occurred warranting termination of funding for the providers.
“After reviewing the evidence currently in the record, the court finds the Inspector General, and thus HHSC, likely acted to disenroll qualified health care providers from Medicaid without cause,” Judge Sparks wrote. “The individual plaintiffs have met their burden to establish a likelihood of success on the merits. The Inspector General did not have prima facie of evidence, or even a scintilla of evidence, to conclude the basis of termination set forth in the final notice merited finding [the plaintiffs] were not qualified.”
Similar efforts were overturned in Virgina after Governor Terry McAuliffe (D) vetoed a bill that sought to restrict state and federal funding from Planned Parenthood providers in the state. In a statement, Gov. McAuliffe said the bill would have harmed Virginians who rely on health care services and programs provided by Planned Parenthood health centers by denying them access to affordable care.
Similar legislation to defund Planned Parenthood providers was introduced by Michigan lawmakers in February.
In addition, efforts are underway at the federal level. On Feb. 16, the House passed H.J.Res.43, which would allow states to withhold Title X family planning funds from providers that offer abortion services, overturning a rule put in place at the end of the Obama administration. The resolution passed 230-188, largely along party lines. It would strike down the Obama-era rule via the 1996 Congressional Review Act, which allows Congress to overturn new regulations within 60 days of their passage. H.J.Res.43 is currently before the Senate.
The American Congress of Obstetricians and Gynecologists expressed disappointment with the House resolution.
“The resolution allows states to discriminate against women’s health care providers for reasons unrelated to qualifications or best practices,” according to an ACOG statement. “Under this resolution, states could disqualify health centers, including Planned Parenthood, from providing Title X contraceptive and preventive care to over 4 million individuals. The Title X program is the only federal grant program exclusively dedicated to providing low-income patients with access to effective family planning and related preventive health services, including contraceptive care. Contraceptive access is essential to helping women achieve greater educational, financial, and professional success and stability. It’s critical to the economic success of this population.”
House Speaker Paul Ryan (R-Wis.) has promised that the bill to repeal the Affordable Care Act will include a measure stripping funds from Planned Parenthood.
[email protected]
On Twitter @legal_med
A federal judge has blocked Texas from withholding Medicaid funds from Planned Parenthood centers in the state, ruling that state officials had no cause to terminate funding for the providers.
In his Feb. 21 decision, Judge Sam Sparks of the U.S. District Court for the Western District of Texas temporarily barred Texas from taking funds from Planned Parenthood while the case proceeds. Restricting the money would deprive Medicaid patients of their right to obtain health care from their chosen providers and would potentially disrupt care for 12,500 Medicaid patients, Judge Sparks wrote.
Texas Attorney General Ken Paxton (R) pledged to appeal the ruling.
“[The] decision is disappointing and flies in the face of basic human decency,” Mr. Paxton said in a statement. “The raw, unedited footage from undercover videos exposed a brazen willingness by Planned Parenthood officials to traffic in fetal body parts, as well as manipulate the timing and method of an abortion ... No taxpayer in Texas should have to subsidize this repugnant and illegal conduct. We should never lose sight of the fact that, as long as abortion is legal in the United States, the potential for these types of horrors will continue.”
Planned Parenthood representatives did not return messages seeking comment on the ruling.
The Texas Health and Human Services Commission (HHSC) terminated funding for its Planned Parenthood providers in the state following a 2015 video by two anti-abortion advocates that purported to show representatives of Planned Parenthood Gulf Coast (Houston) contracting to sell aborted human fetal tissue and body parts. State authorities investigated the facility and found no wrongdoing, but a grand jury indicted the two anti-abortion activists for using fake names and identities.
Separate investigations by the Texas Attorney General’s Office, the Texas Department of State Health Services, and HHSC also found no wrongdoing, according to court documents. In December 2016 however, HHSC sent termination letters to five Texas Planned Parenthood centers, stating that the facilities were not qualified to provide medical services in a “competent, safe, legal and ethical manner” under state and federal law, according to court records. The providers and several patients sued.
In his ruling, Judge Sparks said he found no evidence indicating that an actual program violation occurred warranting termination of funding for the providers.
“After reviewing the evidence currently in the record, the court finds the Inspector General, and thus HHSC, likely acted to disenroll qualified health care providers from Medicaid without cause,” Judge Sparks wrote. “The individual plaintiffs have met their burden to establish a likelihood of success on the merits. The Inspector General did not have prima facie of evidence, or even a scintilla of evidence, to conclude the basis of termination set forth in the final notice merited finding [the plaintiffs] were not qualified.”
Similar efforts were overturned in Virgina after Governor Terry McAuliffe (D) vetoed a bill that sought to restrict state and federal funding from Planned Parenthood providers in the state. In a statement, Gov. McAuliffe said the bill would have harmed Virginians who rely on health care services and programs provided by Planned Parenthood health centers by denying them access to affordable care.
Similar legislation to defund Planned Parenthood providers was introduced by Michigan lawmakers in February.
In addition, efforts are underway at the federal level. On Feb. 16, the House passed H.J.Res.43, which would allow states to withhold Title X family planning funds from providers that offer abortion services, overturning a rule put in place at the end of the Obama administration. The resolution passed 230-188, largely along party lines. It would strike down the Obama-era rule via the 1996 Congressional Review Act, which allows Congress to overturn new regulations within 60 days of their passage. H.J.Res.43 is currently before the Senate.
The American Congress of Obstetricians and Gynecologists expressed disappointment with the House resolution.
“The resolution allows states to discriminate against women’s health care providers for reasons unrelated to qualifications or best practices,” according to an ACOG statement. “Under this resolution, states could disqualify health centers, including Planned Parenthood, from providing Title X contraceptive and preventive care to over 4 million individuals. The Title X program is the only federal grant program exclusively dedicated to providing low-income patients with access to effective family planning and related preventive health services, including contraceptive care. Contraceptive access is essential to helping women achieve greater educational, financial, and professional success and stability. It’s critical to the economic success of this population.”
House Speaker Paul Ryan (R-Wis.) has promised that the bill to repeal the Affordable Care Act will include a measure stripping funds from Planned Parenthood.
[email protected]
On Twitter @legal_med
A federal judge has blocked Texas from withholding Medicaid funds from Planned Parenthood centers in the state, ruling that state officials had no cause to terminate funding for the providers.
In his Feb. 21 decision, Judge Sam Sparks of the U.S. District Court for the Western District of Texas temporarily barred Texas from taking funds from Planned Parenthood while the case proceeds. Restricting the money would deprive Medicaid patients of their right to obtain health care from their chosen providers and would potentially disrupt care for 12,500 Medicaid patients, Judge Sparks wrote.
Texas Attorney General Ken Paxton (R) pledged to appeal the ruling.
“[The] decision is disappointing and flies in the face of basic human decency,” Mr. Paxton said in a statement. “The raw, unedited footage from undercover videos exposed a brazen willingness by Planned Parenthood officials to traffic in fetal body parts, as well as manipulate the timing and method of an abortion ... No taxpayer in Texas should have to subsidize this repugnant and illegal conduct. We should never lose sight of the fact that, as long as abortion is legal in the United States, the potential for these types of horrors will continue.”
Planned Parenthood representatives did not return messages seeking comment on the ruling.
The Texas Health and Human Services Commission (HHSC) terminated funding for its Planned Parenthood providers in the state following a 2015 video by two anti-abortion advocates that purported to show representatives of Planned Parenthood Gulf Coast (Houston) contracting to sell aborted human fetal tissue and body parts. State authorities investigated the facility and found no wrongdoing, but a grand jury indicted the two anti-abortion activists for using fake names and identities.
Separate investigations by the Texas Attorney General’s Office, the Texas Department of State Health Services, and HHSC also found no wrongdoing, according to court documents. In December 2016 however, HHSC sent termination letters to five Texas Planned Parenthood centers, stating that the facilities were not qualified to provide medical services in a “competent, safe, legal and ethical manner” under state and federal law, according to court records. The providers and several patients sued.
In his ruling, Judge Sparks said he found no evidence indicating that an actual program violation occurred warranting termination of funding for the providers.
“After reviewing the evidence currently in the record, the court finds the Inspector General, and thus HHSC, likely acted to disenroll qualified health care providers from Medicaid without cause,” Judge Sparks wrote. “The individual plaintiffs have met their burden to establish a likelihood of success on the merits. The Inspector General did not have prima facie of evidence, or even a scintilla of evidence, to conclude the basis of termination set forth in the final notice merited finding [the plaintiffs] were not qualified.”
Similar efforts were overturned in Virgina after Governor Terry McAuliffe (D) vetoed a bill that sought to restrict state and federal funding from Planned Parenthood providers in the state. In a statement, Gov. McAuliffe said the bill would have harmed Virginians who rely on health care services and programs provided by Planned Parenthood health centers by denying them access to affordable care.
Similar legislation to defund Planned Parenthood providers was introduced by Michigan lawmakers in February.
In addition, efforts are underway at the federal level. On Feb. 16, the House passed H.J.Res.43, which would allow states to withhold Title X family planning funds from providers that offer abortion services, overturning a rule put in place at the end of the Obama administration. The resolution passed 230-188, largely along party lines. It would strike down the Obama-era rule via the 1996 Congressional Review Act, which allows Congress to overturn new regulations within 60 days of their passage. H.J.Res.43 is currently before the Senate.
The American Congress of Obstetricians and Gynecologists expressed disappointment with the House resolution.
“The resolution allows states to discriminate against women’s health care providers for reasons unrelated to qualifications or best practices,” according to an ACOG statement. “Under this resolution, states could disqualify health centers, including Planned Parenthood, from providing Title X contraceptive and preventive care to over 4 million individuals. The Title X program is the only federal grant program exclusively dedicated to providing low-income patients with access to effective family planning and related preventive health services, including contraceptive care. Contraceptive access is essential to helping women achieve greater educational, financial, and professional success and stability. It’s critical to the economic success of this population.”
House Speaker Paul Ryan (R-Wis.) has promised that the bill to repeal the Affordable Care Act will include a measure stripping funds from Planned Parenthood.
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Forging ahead
The approach to clinical conundrums by an expert clinician is revealed through the presentation of an actual patient’s case in an approach typical of a morning report. Similarly to patient care, sequential pieces of information are provided to the clinician, who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring for the patient and the discussant. The bolded text represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.
A 45-year-old woman presented to the emergency department with 2 days of generalized, progressive weakness. Her ability to walk and perform daily chores was increasingly limited. On the morning of her presentation, she was unable to stand up without falling.
A complaint of weakness must be classified as either functional weakness related to a systemic process or true neurologic weakness from dysfunction of the central nervous system (eg, brain, spinal cord) or peripheral nervous system (eg, anterior horn cell, nerve, neuromuscular junction, or muscle). More information on her clinical course and a detailed neurologic exam will help clarify this key branch point.
She was 2 weeks status-post laparoscopic Roux-en-Y gastric bypass and gastric band removal performed in Europe. Immediately following surgery, she experienced abdominal discomfort and nausea with occasional nonbloody, nonbilious emesis, attributed to expected postoperative anatomical changes. She developed a postoperative pneumonia treated with amoxicillin-clavulanate. She tolerated her flight back to the United States, but her abdominal discomfort persisted and she had minimal oral intake due to her nausea.
Functional weakness may stem from hypovolemia from insufficient oral intake, anemia related to the recent surgery, electrolyte abnormalities, chronic nutritional issues associated with obesity and weight-reduction surgery, and pneumonia. Prolonged air travel, obesity, and recent surgery place her at risk for venous thromboembolism, which may manifest as reduced exercise tolerance. Nausea, vomiting, and abdominal pain persisting for 2 weeks after a Roux-en-Y gastric bypass surgery raises several concerns, including gastric remnant distension (although hiccups are often prominent); stomal stenosis, which typically presents several weeks after surgery; marginal ulceration; or infection at the surgical site or from an anastomotic leak. She may also have a surgery- or medication-related myopathy.
The patient had a history of obesity, hypertension, hyperlipidemia, migraine headaches, and nonalcoholic steatohepatitis. Four years previously, she had undergone gastric banding complicated by band migration and ulceration at the banding site. Her medications were amlodipine, losartan, ranitidine, acetaminophen, and nadroparin for venous thromboembolism prophylaxis during her flight. She denied alcohol, tobacco, or illicit drug use. On further questioning, she reported diaphoresis, mild dyspnea, loose stools, and a sensation of numbness and “heaviness” in her arms. Her abdominal pain was limited to the surgical incision and was controlled with acetaminophen. She denied fevers, cough, chest pain, diplopia, or dysphagia.
Heaviness in both arms could result from an acutely presenting myopathic or neuropathic process, while the coexistence of numbness suggests a sensorimotor polyneuropathy. Obesity and gastric bypass surgery increase her nutritional risk, and thiamine deficiency may present as an acute axonal polyneuropathy (ie, beriberi). Unlike vitamin B12 deficiency, which may take years to develop, thiamine deficiency can present within 4 weeks of gastric bypass surgery. Her dyspnea may be a manifestation of diaphragmatic weakness, although her ostensibly treated pneumonia or as of yet unproven postoperative anemia may be contributing. Chemoprophylaxis mitigates her risk of venous thromboembolism, which is, nonetheless, unlikely to account for the gastrointestinal symptoms and upper extremity weakness. If she is continuing to take amlodipine and losartan but has become volume-depleted, hypotension may be contributing to the generalized weakness.
Physical examination revealed an obese, pale and diaphoretic woman. Her temperature was 36.9°C, heart rate 77 beats per minute, blood pressure 158/90 mm Hg, respiratory rate 28 breaths per minute, and O2 saturation 99% on ambient air. She had no cervical lymphadenopathy and a normal thyroid exam. There were no murmurs on cardiac examination, and jugular venous pressure was estimated at 10 cm of water. Her lung sounds were clear. Her abdomen was soft, nondistended, with localized tenderness and fluctuance around the midline surgical incision with a small amount of purulent drainage. She was alert and oriented to name, date, place, and situation. Cranial nerves II through XII were grossly intact. Strength was 4/5 in bilateral biceps, triceps and distal hand and finger extensors, 3/5 in bilateral deltoids. Strength in hip flexors was 4/5 and it was 5/5 in distal lower extremities. Sensation was intact to pinprick in upper and lower extremities. Biceps reflexes were absent; patellar and ankle reflexes were 1+ and symmetric. The remainder of the physical exam was unremarkable.
The patient has symmetric proximal muscle weakness with upper extremity predominance and preserved strength in her distal lower extremities. A myopathy could explain this pattern of weakness, further substantiated by absent reflexes and reportedly intact sensation. Subacute causes of myopathy include hypokalemia, hyperkalemia, toxic myopathies from medications, or infection-induced rhabdomyolysis. However, she does not report muscle pain, and the loss of reflexes is faster than would be expected with a myopathy. A more thorough sensory examination would inform the assessment of potential neuropathic processes. Guillain-Barré syndrome (GBS) is possible; it most commonly presents as an ascending, distally predominant acute inflammatory demyelinating polyneuropathy (AIDP), although her upper extremity weakness predominates and there are no clear sensory changes. It remains to be determined how her wound infection might relate to her overall presentation.
Her white blood cell count was 12,600/μL (reference range: 3,400-10,000/μL), hemoglobin was 10.2 g/dL, and platelet count was 698,000/μL. Mean corpuscular volume was 86 fL. Serum chemistries were: sodium 138 mEq/L, potassium 3.8 mEq/L, chloride 106 mmol/L, bicarbonate 15 mmol/L, blood urea nitrogen 5 mg/dL, creatinine 0.65 mg/dL, glucose 125 mg/dL, calcium 8.3 mg/dL, magnesium 1.9 mg/dL, phosphorous 2.4 mg/dL, and lactate 1.8 mmol/L (normal: < 2.0 mmol/L). Creatinine kinase (CK), liver function tests, and coagulation panel were normal. Total protein was 6.4 g/dL, and albumin was 2.7 g/dL. Venous blood gas was: pH 7.39 and PCO2 25 mmHg. Urinalysis revealed ketones. Blood and wound cultures were sent for evaluation. A chest x-ray was unremarkable. An electrocardiogram showed normal sinus rhythm. Computed tomography (CT) of the abdomen and pelvis revealed a multiloculated rim-enhancing fluid collection in the anterior abdominal wall (Figure 1).
She does not have any notable electrolyte derangements that would account for her weakness, and the normal creatinine kinase lowers the probability of a myopathy and excludes rhabdomyolysis. Progression of weakness from proximal to distal muscles in a symmetric fashion is consistent with botulism, and she has an intra-abdominal wound infection that could be harboring Clostridium botulinum. Nonetheless, the normal cranial nerve exam and the rarity of botulism occurring with surgical wounds argue against this diagnosis. She should receive intravenous (IV) thiamine for the possibility of beriberi. A lumbar puncture should be performed to assess for albuminocytologic dissociation, which can be seen in patients with GBS.
The patient received high-dose IV thiamine, IV vancomycin, IV piperacillin-tazobactam, and acetaminophen. Over the subsequent 4 hours, her anion gap acidosis worsened. She declined arterial puncture. Repeat venous blood gas was: pH 7.22, PCO2 28 mmHg, and bicarbonate 11 mmol/L. Lactate and glucose were normal. Serum osmolarity was 292 mmol/kg (reference range: 283-301 mmol/kg). She was started on an IV sodium bicarbonate infusion without improvement in her acidemia.
An acute anion gap metabolic acidosis suggests a limited differential diagnosis that includes lactic acidosis, D-lactic acidosis, severe starvation ketoacidosis, acute renal failure, salicylate, or other drug or poison ingestion. Starvation ketoacidosis may be contributing, but a bicarbonate value this low would be unusual. There is no history of alcohol use or other ingestions, and the normal serum osmolality and low osmolal gap (less than 10 mOsm/kg) argue against a poisoning with ethanol, ethylene glycol, or methanol. The initial combined anion gap metabolic acidosis and respiratory alkalosis is consistent with salicylate toxicity, but she does not report aspirin ingestion. Acetaminophen use in the setting of malnutrition or starvation physiology raises the possibility of 5-oxoproline accumulation.
Routine serum lactate does not detect D-lactate, which is produced by colonic bacteria and has been reported in short bowel syndrome and following intestinal bypass surgery. This may occur weeks to months after intestinal procedures, following ingestion of a heavy carbohydrate load, and almost invariably presents with altered mental status and increased anion gap metabolic acidosis, although generalized weakness has been reported.
A surgical consultant drained her wound infection. Fluid Gram stain was negative. D-lactate, salicylate and acetaminophen levels were undetectable. Thiamine pyrophosphate level was 229 nmol/L (reference range: 78-185 nmol/L). Acetaminophen was discontinued and N-acetylcysteine infusion was started for possible 5-oxoprolinemia. Her anion gap acidosis rapidly improved. Twelve hours after admission, she reported sudden onset of blurry vision. Her vital signs were: temperature 37oC, heart rate 110 beats per minute, respiratory rate 40 breaths per minute, blood pressure 168/90, and oxygen saturation 100% on ambient air. Telemetry showed ventricular bigeminy. On examination, she was unable to abduct her right eye; muscle strength was 1/5 in all extremities; biceps, ankle, and patellar reflexes were absent.
Her neurological deficits have progressed over hours to near complete paralysis, asymmetric cranial nerve paresis, and areflexia. Although botulism can cause blurred vision and absent deep tendon reflexes, patients almost always have symmetrical bulbar findings followed by descending paralysis. Should the “numbness” in her arms reported earlier represent undetected sensory deficits, this, too would be inconsistent with botulism.
A diagnosis of GBS ties together several aspects of her presentation and clinical course. Several variants show different patterns of weakness and may involve cranial nerves. Her tachypnea and dyspnea are concerning signs of potential impending respiratory failure. The ventricular bigeminy and mild hypertension could represent autonomic dysfunction that is seen in many cases of GBS.
She was intubated for airway protection. Computed tomography angiography and magnetic resonance imaging of her brain were normal. Cerebral spinal fluid analysis obtained through lumbar puncture showed the following: white blood cell count 3/μL, red blood cell count 11/μL, protein 63 mg/dL (reference range: 15-60mg/dL), and glucose 128 mg/dL (reference range: 40-80mg/dL).
The lumbar puncture is consistent with GBS given the slightly elevated protein and cell count well below 50/μL. Given the severity of her symptoms, treatment with IV immunoglobulin or plasmapheresis should be initiated. Nerve conduction studies (NCS) and electromyography (EMG) are indicated for diagnostic confirmation.
EMG and NCS revealed a severe sensorimotor polyneuropathy with demyelinating features including a conduction block at a noncompressible site, consistent with AIDP. Left sural nerve biopsy confirmed acute demyelinating and mild axonal neuropathy (Figure 2). On hospital day 2, treatment with IV immunoglobulins (IVIG) was initiated; however, she developed anaphylaxis following her second administration and subsequently received plasmapheresis. A tracheostomy was performed for respiratory muscle weakness, and she was discharged to a nursing facility. C. botulinum cultures from the wound eventually returned negative. Following her hospitalization, a serum 5-oxoproline level sent 10 hours after admission returned as elevated, confirming the additional diagnosis of 5-oxoprolinemia. On follow-up, she can sit up and feed herself without assistance, and her gait continues to improve with physical therapy.
DISCUSSION
This patient presented with rapidly progressive weakness that developed in the 2 weeks following bariatric surgery. In the postsurgical setting, patient complaints of weakness are commonly encountered and can pose a diagnostic challenge. Asthenia (ie, general loss of strength or energy) is frequently reported in the immediate postoperative period, and may result from the stress of surgery, pain, deconditioning, or infection. This must be distinguished from true neurologic weakness, which results from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, or muscle. The initial history can help elucidate the inciting events such as preceding surgery, infections or ingestions, and can also categorize the pattern of weakness. The neurologic examination can localize the pathology within the neuraxis. EMG and NCS can distinguish neuropathy from radiculopathy, and categorize the process as axonal, demyelinating, or mixed. In this case, the oculomotor weakness, sensory abnormalities and areflexia signaled a severe sensorimotor polyneuropathy, and EMG/NCS confirmed a demyelinating process consistent with GBS.
Guillain-Barré syndrome is an acute, immune-mediated polyneuropathy. Patients with GBS often present with a preceding respiratory or diarrheal illness; however, the stress of a recent surgery can serve as an inciting event. The syndrome, acute postgastric reduction surgery (APGARS) neuropathy, was introduced in the literature in 2002, describing 3 patients who presented with progressive vomiting, weakness, and hyporeflexia following bariatric surgery.1 The term has been used to describe bariatric surgery patients who developed postoperative quadriparesis, cranial nerve deficits, and respiratory compromise.2 Given the clinical heterogeneity in the literature with relation to APGARS, it is probable that the cases described could result from multiple etiologies. While GBS is purely immune-mediated and can be precipitated by the stress of surgery itself, postbariatric surgery patients are susceptible to many nutritional deficiencies that can lead to similar presentations.3 For example, thiamine (vitamin B1) and cobalamin (vitamin B12) deficiencies cause distinct postbariatric surgery neuropathies.4 Thiamine deficiency may manifest weeks to months after surgery and can rapidly progress, whereas cobalamin deficiency generally develops over 3 to 5 years. Both of these syndromes demonstrate an axonal pattern of nerve injury on EMG/NCS, in contrast to the demyelinating pattern typically seen in GBS. In addition, bariatric surgery patients are at higher risk for copper deficiency, which usually presents as a myeloneuropathy with subacute gait decline and upper motor neuron signs including spasticity.
Although GBS classically presents with symmetric ascending weakness and sensory abnormalities, it may manifest in myriad ways. Factors influencing the presentation include the types of nerve fibers involved (motor, sensory, cranial or autonomic), the predominant mode of injury (axonal vs demyelinating), and the presence or absence of alteration in consciousness.5 The most common form of GBS is AIDP. The classic presentation involves paresthesias in the fingertips and toes followed by lower extremity weakness that ascends over hours to days to involve the arms and potentially the muscles of respiration. A minority of patients with GBS first experience weakness in the upper extremities or facial muscles, and oculomotor involvement is rare.5 Pain is common and often severe.6 Dysautonomia affects most patients with GBS and may manifest as labile blood pressure or arrhythmias.5 Several variant GBS presentation patterns have been described, including acute motor axonal neuropathy, a pure motor form of GBS; ophthalmoplegia, ataxia, and areflexia in Miller Fisher syndrome; and alteration in consciousness, hyperreflexia, ataxia, and ophthalmoparesis in Bickerstaff’s brain stem encephalitis.5
Patients with GBS can progress rapidly to respiratory failure. Serial neurologic exams may signal the diagnosis and inform triage to the appropriate level of care. Measurement of bedside pulmonary function, including mean inspiratory force and functional vital capacity, help to determine if there is weakness of diaphragmatic muscles. Patients with signs or symptoms of diaphragmatic weakness require monitoring in an intensive care unit and potentially early intubation. Treatment with IVIG or plasmapheresis has been found to hasten recovery from GBS, including earlier improvement in muscle strength and a reduced need for mechanical ventilation.7 Treatment selection is based on available resources as both modalities are felt to be equivalent.The majority of patients with GBS make a full recovery over a period of weeks to months, although many have persistent motor weakness. Despite immunotherapy, up to 20% of patients remain severely disabled and approximately 5% die.8 Advanced age, rapid progression of weakness over a period of less than 72 hours, need for mechanical ventilation, and absent compound muscle action potentials on NCS are all associated with prolonged and incomplete recovery.9
This patient developed respiratory failure within 12 hours of hospitalization, prior to being diagnosed with GBS. Even in that short time, the treating clinicians encountered a series of clinical diversions. The initial proximal pattern of muscle weakness suggested a possible myopathic process; the wound infection introduced the possibility of botulism; obesity and recent bariatric surgery triggered concern for thiamine deficiency; and the anion gap acidosis from 5-oxoprolinemia created yet another clinical detour. While the path from presentation to diagnosis is seldom a straight line, when faced with rapidly progressive weakness, it is paramount to forge ahead with an efficient diagnostic evaluation and timely therapeutic intervention.
KEY TEACHING POINTS
- A complaint of general weakness requires distinction between asthenia (ie, general loss of strength or energy) and true neuromuscular weakness from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, and/or muscle.
- Guillain-Barré syndrome may present in a variety of atypical fashions not limited to ascending, distally predominant weakness.
- Acute postgastric reduction surgery neuropathy should be considered in patients presenting with weakness, vomiting, or hyporeflexia after bariatric surgery.
- Acute inflammatory demyelinating polyneuropathy may rapidly progress to respiratory failure, and warrants serial neurologic examinations, monitoring of pulmonary function, and an expedited diagnostic evaluation.
Disclosure
Nothing to report.
1. Akhtar M, Collins MP, Kissel JT. Acute postgastric reduction surgery (APGARS) Neuropathy: A polynutritional, multisystem disorder. Neurology. 2002;58:A68. PubMed
2. Chang CG, Adams-Huet B, Provost DA. Acute post-gastric reduction surgery (APGARS) neuropathy. Obes Surg. 2004;14(2):182-189. PubMed
3. Chang CG, Helling TS, Black WE, Rymer MM. Weakness after gastric bypass. Obes Surg. 2002;12(4):592-597. PubMed
4. Shankar P, Boylan M, Sriram K. Micronutrient deficiencies after bariatric surgery. Nutrition. 2010;26(11-12):1031-1037. PubMed
5. Dimachkie MM, Barohn RJ. Guillain-Barré syndrome and variants. Neurol Clin. 2013;31(2):491-510. PubMed
6. Ruts L, Drenthen J, Jongen JL, et al. Pain in Guillain-Barré syndrome: a long-term follow-up study. Neurology. 2010;75(16):1439-1447. PubMed
7. Hughes RAC, Wijdicks EFM, Barohn R, et al: Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: immunotherapy for Guillain-Barré syndrome: report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2003;61:736-740. PubMed
8. Hughes RA, Swan AV, Raphaël JC, Annane D, van Koningsveld R, van Doorn PA. Immunotherapy for Guillain-Barré syndrome: a systematic review. Brain. 2007;130(Pt 9):2245-2257. PubMed
9. Rajabally YA, Uncini A. Outcome and predictors in Guillain-Barré syndrome. J Neurol Neurosurg Psychiatry. 2012;83(7):711-718. PubMed
The approach to clinical conundrums by an expert clinician is revealed through the presentation of an actual patient’s case in an approach typical of a morning report. Similarly to patient care, sequential pieces of information are provided to the clinician, who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring for the patient and the discussant. The bolded text represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.
A 45-year-old woman presented to the emergency department with 2 days of generalized, progressive weakness. Her ability to walk and perform daily chores was increasingly limited. On the morning of her presentation, she was unable to stand up without falling.
A complaint of weakness must be classified as either functional weakness related to a systemic process or true neurologic weakness from dysfunction of the central nervous system (eg, brain, spinal cord) or peripheral nervous system (eg, anterior horn cell, nerve, neuromuscular junction, or muscle). More information on her clinical course and a detailed neurologic exam will help clarify this key branch point.
She was 2 weeks status-post laparoscopic Roux-en-Y gastric bypass and gastric band removal performed in Europe. Immediately following surgery, she experienced abdominal discomfort and nausea with occasional nonbloody, nonbilious emesis, attributed to expected postoperative anatomical changes. She developed a postoperative pneumonia treated with amoxicillin-clavulanate. She tolerated her flight back to the United States, but her abdominal discomfort persisted and she had minimal oral intake due to her nausea.
Functional weakness may stem from hypovolemia from insufficient oral intake, anemia related to the recent surgery, electrolyte abnormalities, chronic nutritional issues associated with obesity and weight-reduction surgery, and pneumonia. Prolonged air travel, obesity, and recent surgery place her at risk for venous thromboembolism, which may manifest as reduced exercise tolerance. Nausea, vomiting, and abdominal pain persisting for 2 weeks after a Roux-en-Y gastric bypass surgery raises several concerns, including gastric remnant distension (although hiccups are often prominent); stomal stenosis, which typically presents several weeks after surgery; marginal ulceration; or infection at the surgical site or from an anastomotic leak. She may also have a surgery- or medication-related myopathy.
The patient had a history of obesity, hypertension, hyperlipidemia, migraine headaches, and nonalcoholic steatohepatitis. Four years previously, she had undergone gastric banding complicated by band migration and ulceration at the banding site. Her medications were amlodipine, losartan, ranitidine, acetaminophen, and nadroparin for venous thromboembolism prophylaxis during her flight. She denied alcohol, tobacco, or illicit drug use. On further questioning, she reported diaphoresis, mild dyspnea, loose stools, and a sensation of numbness and “heaviness” in her arms. Her abdominal pain was limited to the surgical incision and was controlled with acetaminophen. She denied fevers, cough, chest pain, diplopia, or dysphagia.
Heaviness in both arms could result from an acutely presenting myopathic or neuropathic process, while the coexistence of numbness suggests a sensorimotor polyneuropathy. Obesity and gastric bypass surgery increase her nutritional risk, and thiamine deficiency may present as an acute axonal polyneuropathy (ie, beriberi). Unlike vitamin B12 deficiency, which may take years to develop, thiamine deficiency can present within 4 weeks of gastric bypass surgery. Her dyspnea may be a manifestation of diaphragmatic weakness, although her ostensibly treated pneumonia or as of yet unproven postoperative anemia may be contributing. Chemoprophylaxis mitigates her risk of venous thromboembolism, which is, nonetheless, unlikely to account for the gastrointestinal symptoms and upper extremity weakness. If she is continuing to take amlodipine and losartan but has become volume-depleted, hypotension may be contributing to the generalized weakness.
Physical examination revealed an obese, pale and diaphoretic woman. Her temperature was 36.9°C, heart rate 77 beats per minute, blood pressure 158/90 mm Hg, respiratory rate 28 breaths per minute, and O2 saturation 99% on ambient air. She had no cervical lymphadenopathy and a normal thyroid exam. There were no murmurs on cardiac examination, and jugular venous pressure was estimated at 10 cm of water. Her lung sounds were clear. Her abdomen was soft, nondistended, with localized tenderness and fluctuance around the midline surgical incision with a small amount of purulent drainage. She was alert and oriented to name, date, place, and situation. Cranial nerves II through XII were grossly intact. Strength was 4/5 in bilateral biceps, triceps and distal hand and finger extensors, 3/5 in bilateral deltoids. Strength in hip flexors was 4/5 and it was 5/5 in distal lower extremities. Sensation was intact to pinprick in upper and lower extremities. Biceps reflexes were absent; patellar and ankle reflexes were 1+ and symmetric. The remainder of the physical exam was unremarkable.
The patient has symmetric proximal muscle weakness with upper extremity predominance and preserved strength in her distal lower extremities. A myopathy could explain this pattern of weakness, further substantiated by absent reflexes and reportedly intact sensation. Subacute causes of myopathy include hypokalemia, hyperkalemia, toxic myopathies from medications, or infection-induced rhabdomyolysis. However, she does not report muscle pain, and the loss of reflexes is faster than would be expected with a myopathy. A more thorough sensory examination would inform the assessment of potential neuropathic processes. Guillain-Barré syndrome (GBS) is possible; it most commonly presents as an ascending, distally predominant acute inflammatory demyelinating polyneuropathy (AIDP), although her upper extremity weakness predominates and there are no clear sensory changes. It remains to be determined how her wound infection might relate to her overall presentation.
Her white blood cell count was 12,600/μL (reference range: 3,400-10,000/μL), hemoglobin was 10.2 g/dL, and platelet count was 698,000/μL. Mean corpuscular volume was 86 fL. Serum chemistries were: sodium 138 mEq/L, potassium 3.8 mEq/L, chloride 106 mmol/L, bicarbonate 15 mmol/L, blood urea nitrogen 5 mg/dL, creatinine 0.65 mg/dL, glucose 125 mg/dL, calcium 8.3 mg/dL, magnesium 1.9 mg/dL, phosphorous 2.4 mg/dL, and lactate 1.8 mmol/L (normal: < 2.0 mmol/L). Creatinine kinase (CK), liver function tests, and coagulation panel were normal. Total protein was 6.4 g/dL, and albumin was 2.7 g/dL. Venous blood gas was: pH 7.39 and PCO2 25 mmHg. Urinalysis revealed ketones. Blood and wound cultures were sent for evaluation. A chest x-ray was unremarkable. An electrocardiogram showed normal sinus rhythm. Computed tomography (CT) of the abdomen and pelvis revealed a multiloculated rim-enhancing fluid collection in the anterior abdominal wall (Figure 1).
She does not have any notable electrolyte derangements that would account for her weakness, and the normal creatinine kinase lowers the probability of a myopathy and excludes rhabdomyolysis. Progression of weakness from proximal to distal muscles in a symmetric fashion is consistent with botulism, and she has an intra-abdominal wound infection that could be harboring Clostridium botulinum. Nonetheless, the normal cranial nerve exam and the rarity of botulism occurring with surgical wounds argue against this diagnosis. She should receive intravenous (IV) thiamine for the possibility of beriberi. A lumbar puncture should be performed to assess for albuminocytologic dissociation, which can be seen in patients with GBS.
The patient received high-dose IV thiamine, IV vancomycin, IV piperacillin-tazobactam, and acetaminophen. Over the subsequent 4 hours, her anion gap acidosis worsened. She declined arterial puncture. Repeat venous blood gas was: pH 7.22, PCO2 28 mmHg, and bicarbonate 11 mmol/L. Lactate and glucose were normal. Serum osmolarity was 292 mmol/kg (reference range: 283-301 mmol/kg). She was started on an IV sodium bicarbonate infusion without improvement in her acidemia.
An acute anion gap metabolic acidosis suggests a limited differential diagnosis that includes lactic acidosis, D-lactic acidosis, severe starvation ketoacidosis, acute renal failure, salicylate, or other drug or poison ingestion. Starvation ketoacidosis may be contributing, but a bicarbonate value this low would be unusual. There is no history of alcohol use or other ingestions, and the normal serum osmolality and low osmolal gap (less than 10 mOsm/kg) argue against a poisoning with ethanol, ethylene glycol, or methanol. The initial combined anion gap metabolic acidosis and respiratory alkalosis is consistent with salicylate toxicity, but she does not report aspirin ingestion. Acetaminophen use in the setting of malnutrition or starvation physiology raises the possibility of 5-oxoproline accumulation.
Routine serum lactate does not detect D-lactate, which is produced by colonic bacteria and has been reported in short bowel syndrome and following intestinal bypass surgery. This may occur weeks to months after intestinal procedures, following ingestion of a heavy carbohydrate load, and almost invariably presents with altered mental status and increased anion gap metabolic acidosis, although generalized weakness has been reported.
A surgical consultant drained her wound infection. Fluid Gram stain was negative. D-lactate, salicylate and acetaminophen levels were undetectable. Thiamine pyrophosphate level was 229 nmol/L (reference range: 78-185 nmol/L). Acetaminophen was discontinued and N-acetylcysteine infusion was started for possible 5-oxoprolinemia. Her anion gap acidosis rapidly improved. Twelve hours after admission, she reported sudden onset of blurry vision. Her vital signs were: temperature 37oC, heart rate 110 beats per minute, respiratory rate 40 breaths per minute, blood pressure 168/90, and oxygen saturation 100% on ambient air. Telemetry showed ventricular bigeminy. On examination, she was unable to abduct her right eye; muscle strength was 1/5 in all extremities; biceps, ankle, and patellar reflexes were absent.
Her neurological deficits have progressed over hours to near complete paralysis, asymmetric cranial nerve paresis, and areflexia. Although botulism can cause blurred vision and absent deep tendon reflexes, patients almost always have symmetrical bulbar findings followed by descending paralysis. Should the “numbness” in her arms reported earlier represent undetected sensory deficits, this, too would be inconsistent with botulism.
A diagnosis of GBS ties together several aspects of her presentation and clinical course. Several variants show different patterns of weakness and may involve cranial nerves. Her tachypnea and dyspnea are concerning signs of potential impending respiratory failure. The ventricular bigeminy and mild hypertension could represent autonomic dysfunction that is seen in many cases of GBS.
She was intubated for airway protection. Computed tomography angiography and magnetic resonance imaging of her brain were normal. Cerebral spinal fluid analysis obtained through lumbar puncture showed the following: white blood cell count 3/μL, red blood cell count 11/μL, protein 63 mg/dL (reference range: 15-60mg/dL), and glucose 128 mg/dL (reference range: 40-80mg/dL).
The lumbar puncture is consistent with GBS given the slightly elevated protein and cell count well below 50/μL. Given the severity of her symptoms, treatment with IV immunoglobulin or plasmapheresis should be initiated. Nerve conduction studies (NCS) and electromyography (EMG) are indicated for diagnostic confirmation.
EMG and NCS revealed a severe sensorimotor polyneuropathy with demyelinating features including a conduction block at a noncompressible site, consistent with AIDP. Left sural nerve biopsy confirmed acute demyelinating and mild axonal neuropathy (Figure 2). On hospital day 2, treatment with IV immunoglobulins (IVIG) was initiated; however, she developed anaphylaxis following her second administration and subsequently received plasmapheresis. A tracheostomy was performed for respiratory muscle weakness, and she was discharged to a nursing facility. C. botulinum cultures from the wound eventually returned negative. Following her hospitalization, a serum 5-oxoproline level sent 10 hours after admission returned as elevated, confirming the additional diagnosis of 5-oxoprolinemia. On follow-up, she can sit up and feed herself without assistance, and her gait continues to improve with physical therapy.
DISCUSSION
This patient presented with rapidly progressive weakness that developed in the 2 weeks following bariatric surgery. In the postsurgical setting, patient complaints of weakness are commonly encountered and can pose a diagnostic challenge. Asthenia (ie, general loss of strength or energy) is frequently reported in the immediate postoperative period, and may result from the stress of surgery, pain, deconditioning, or infection. This must be distinguished from true neurologic weakness, which results from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, or muscle. The initial history can help elucidate the inciting events such as preceding surgery, infections or ingestions, and can also categorize the pattern of weakness. The neurologic examination can localize the pathology within the neuraxis. EMG and NCS can distinguish neuropathy from radiculopathy, and categorize the process as axonal, demyelinating, or mixed. In this case, the oculomotor weakness, sensory abnormalities and areflexia signaled a severe sensorimotor polyneuropathy, and EMG/NCS confirmed a demyelinating process consistent with GBS.
Guillain-Barré syndrome is an acute, immune-mediated polyneuropathy. Patients with GBS often present with a preceding respiratory or diarrheal illness; however, the stress of a recent surgery can serve as an inciting event. The syndrome, acute postgastric reduction surgery (APGARS) neuropathy, was introduced in the literature in 2002, describing 3 patients who presented with progressive vomiting, weakness, and hyporeflexia following bariatric surgery.1 The term has been used to describe bariatric surgery patients who developed postoperative quadriparesis, cranial nerve deficits, and respiratory compromise.2 Given the clinical heterogeneity in the literature with relation to APGARS, it is probable that the cases described could result from multiple etiologies. While GBS is purely immune-mediated and can be precipitated by the stress of surgery itself, postbariatric surgery patients are susceptible to many nutritional deficiencies that can lead to similar presentations.3 For example, thiamine (vitamin B1) and cobalamin (vitamin B12) deficiencies cause distinct postbariatric surgery neuropathies.4 Thiamine deficiency may manifest weeks to months after surgery and can rapidly progress, whereas cobalamin deficiency generally develops over 3 to 5 years. Both of these syndromes demonstrate an axonal pattern of nerve injury on EMG/NCS, in contrast to the demyelinating pattern typically seen in GBS. In addition, bariatric surgery patients are at higher risk for copper deficiency, which usually presents as a myeloneuropathy with subacute gait decline and upper motor neuron signs including spasticity.
Although GBS classically presents with symmetric ascending weakness and sensory abnormalities, it may manifest in myriad ways. Factors influencing the presentation include the types of nerve fibers involved (motor, sensory, cranial or autonomic), the predominant mode of injury (axonal vs demyelinating), and the presence or absence of alteration in consciousness.5 The most common form of GBS is AIDP. The classic presentation involves paresthesias in the fingertips and toes followed by lower extremity weakness that ascends over hours to days to involve the arms and potentially the muscles of respiration. A minority of patients with GBS first experience weakness in the upper extremities or facial muscles, and oculomotor involvement is rare.5 Pain is common and often severe.6 Dysautonomia affects most patients with GBS and may manifest as labile blood pressure or arrhythmias.5 Several variant GBS presentation patterns have been described, including acute motor axonal neuropathy, a pure motor form of GBS; ophthalmoplegia, ataxia, and areflexia in Miller Fisher syndrome; and alteration in consciousness, hyperreflexia, ataxia, and ophthalmoparesis in Bickerstaff’s brain stem encephalitis.5
Patients with GBS can progress rapidly to respiratory failure. Serial neurologic exams may signal the diagnosis and inform triage to the appropriate level of care. Measurement of bedside pulmonary function, including mean inspiratory force and functional vital capacity, help to determine if there is weakness of diaphragmatic muscles. Patients with signs or symptoms of diaphragmatic weakness require monitoring in an intensive care unit and potentially early intubation. Treatment with IVIG or plasmapheresis has been found to hasten recovery from GBS, including earlier improvement in muscle strength and a reduced need for mechanical ventilation.7 Treatment selection is based on available resources as both modalities are felt to be equivalent.The majority of patients with GBS make a full recovery over a period of weeks to months, although many have persistent motor weakness. Despite immunotherapy, up to 20% of patients remain severely disabled and approximately 5% die.8 Advanced age, rapid progression of weakness over a period of less than 72 hours, need for mechanical ventilation, and absent compound muscle action potentials on NCS are all associated with prolonged and incomplete recovery.9
This patient developed respiratory failure within 12 hours of hospitalization, prior to being diagnosed with GBS. Even in that short time, the treating clinicians encountered a series of clinical diversions. The initial proximal pattern of muscle weakness suggested a possible myopathic process; the wound infection introduced the possibility of botulism; obesity and recent bariatric surgery triggered concern for thiamine deficiency; and the anion gap acidosis from 5-oxoprolinemia created yet another clinical detour. While the path from presentation to diagnosis is seldom a straight line, when faced with rapidly progressive weakness, it is paramount to forge ahead with an efficient diagnostic evaluation and timely therapeutic intervention.
KEY TEACHING POINTS
- A complaint of general weakness requires distinction between asthenia (ie, general loss of strength or energy) and true neuromuscular weakness from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, and/or muscle.
- Guillain-Barré syndrome may present in a variety of atypical fashions not limited to ascending, distally predominant weakness.
- Acute postgastric reduction surgery neuropathy should be considered in patients presenting with weakness, vomiting, or hyporeflexia after bariatric surgery.
- Acute inflammatory demyelinating polyneuropathy may rapidly progress to respiratory failure, and warrants serial neurologic examinations, monitoring of pulmonary function, and an expedited diagnostic evaluation.
Disclosure
Nothing to report.
The approach to clinical conundrums by an expert clinician is revealed through the presentation of an actual patient’s case in an approach typical of a morning report. Similarly to patient care, sequential pieces of information are provided to the clinician, who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring for the patient and the discussant. The bolded text represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.
A 45-year-old woman presented to the emergency department with 2 days of generalized, progressive weakness. Her ability to walk and perform daily chores was increasingly limited. On the morning of her presentation, she was unable to stand up without falling.
A complaint of weakness must be classified as either functional weakness related to a systemic process or true neurologic weakness from dysfunction of the central nervous system (eg, brain, spinal cord) or peripheral nervous system (eg, anterior horn cell, nerve, neuromuscular junction, or muscle). More information on her clinical course and a detailed neurologic exam will help clarify this key branch point.
She was 2 weeks status-post laparoscopic Roux-en-Y gastric bypass and gastric band removal performed in Europe. Immediately following surgery, she experienced abdominal discomfort and nausea with occasional nonbloody, nonbilious emesis, attributed to expected postoperative anatomical changes. She developed a postoperative pneumonia treated with amoxicillin-clavulanate. She tolerated her flight back to the United States, but her abdominal discomfort persisted and she had minimal oral intake due to her nausea.
Functional weakness may stem from hypovolemia from insufficient oral intake, anemia related to the recent surgery, electrolyte abnormalities, chronic nutritional issues associated with obesity and weight-reduction surgery, and pneumonia. Prolonged air travel, obesity, and recent surgery place her at risk for venous thromboembolism, which may manifest as reduced exercise tolerance. Nausea, vomiting, and abdominal pain persisting for 2 weeks after a Roux-en-Y gastric bypass surgery raises several concerns, including gastric remnant distension (although hiccups are often prominent); stomal stenosis, which typically presents several weeks after surgery; marginal ulceration; or infection at the surgical site or from an anastomotic leak. She may also have a surgery- or medication-related myopathy.
The patient had a history of obesity, hypertension, hyperlipidemia, migraine headaches, and nonalcoholic steatohepatitis. Four years previously, she had undergone gastric banding complicated by band migration and ulceration at the banding site. Her medications were amlodipine, losartan, ranitidine, acetaminophen, and nadroparin for venous thromboembolism prophylaxis during her flight. She denied alcohol, tobacco, or illicit drug use. On further questioning, she reported diaphoresis, mild dyspnea, loose stools, and a sensation of numbness and “heaviness” in her arms. Her abdominal pain was limited to the surgical incision and was controlled with acetaminophen. She denied fevers, cough, chest pain, diplopia, or dysphagia.
Heaviness in both arms could result from an acutely presenting myopathic or neuropathic process, while the coexistence of numbness suggests a sensorimotor polyneuropathy. Obesity and gastric bypass surgery increase her nutritional risk, and thiamine deficiency may present as an acute axonal polyneuropathy (ie, beriberi). Unlike vitamin B12 deficiency, which may take years to develop, thiamine deficiency can present within 4 weeks of gastric bypass surgery. Her dyspnea may be a manifestation of diaphragmatic weakness, although her ostensibly treated pneumonia or as of yet unproven postoperative anemia may be contributing. Chemoprophylaxis mitigates her risk of venous thromboembolism, which is, nonetheless, unlikely to account for the gastrointestinal symptoms and upper extremity weakness. If she is continuing to take amlodipine and losartan but has become volume-depleted, hypotension may be contributing to the generalized weakness.
Physical examination revealed an obese, pale and diaphoretic woman. Her temperature was 36.9°C, heart rate 77 beats per minute, blood pressure 158/90 mm Hg, respiratory rate 28 breaths per minute, and O2 saturation 99% on ambient air. She had no cervical lymphadenopathy and a normal thyroid exam. There were no murmurs on cardiac examination, and jugular venous pressure was estimated at 10 cm of water. Her lung sounds were clear. Her abdomen was soft, nondistended, with localized tenderness and fluctuance around the midline surgical incision with a small amount of purulent drainage. She was alert and oriented to name, date, place, and situation. Cranial nerves II through XII were grossly intact. Strength was 4/5 in bilateral biceps, triceps and distal hand and finger extensors, 3/5 in bilateral deltoids. Strength in hip flexors was 4/5 and it was 5/5 in distal lower extremities. Sensation was intact to pinprick in upper and lower extremities. Biceps reflexes were absent; patellar and ankle reflexes were 1+ and symmetric. The remainder of the physical exam was unremarkable.
The patient has symmetric proximal muscle weakness with upper extremity predominance and preserved strength in her distal lower extremities. A myopathy could explain this pattern of weakness, further substantiated by absent reflexes and reportedly intact sensation. Subacute causes of myopathy include hypokalemia, hyperkalemia, toxic myopathies from medications, or infection-induced rhabdomyolysis. However, she does not report muscle pain, and the loss of reflexes is faster than would be expected with a myopathy. A more thorough sensory examination would inform the assessment of potential neuropathic processes. Guillain-Barré syndrome (GBS) is possible; it most commonly presents as an ascending, distally predominant acute inflammatory demyelinating polyneuropathy (AIDP), although her upper extremity weakness predominates and there are no clear sensory changes. It remains to be determined how her wound infection might relate to her overall presentation.
Her white blood cell count was 12,600/μL (reference range: 3,400-10,000/μL), hemoglobin was 10.2 g/dL, and platelet count was 698,000/μL. Mean corpuscular volume was 86 fL. Serum chemistries were: sodium 138 mEq/L, potassium 3.8 mEq/L, chloride 106 mmol/L, bicarbonate 15 mmol/L, blood urea nitrogen 5 mg/dL, creatinine 0.65 mg/dL, glucose 125 mg/dL, calcium 8.3 mg/dL, magnesium 1.9 mg/dL, phosphorous 2.4 mg/dL, and lactate 1.8 mmol/L (normal: < 2.0 mmol/L). Creatinine kinase (CK), liver function tests, and coagulation panel were normal. Total protein was 6.4 g/dL, and albumin was 2.7 g/dL. Venous blood gas was: pH 7.39 and PCO2 25 mmHg. Urinalysis revealed ketones. Blood and wound cultures were sent for evaluation. A chest x-ray was unremarkable. An electrocardiogram showed normal sinus rhythm. Computed tomography (CT) of the abdomen and pelvis revealed a multiloculated rim-enhancing fluid collection in the anterior abdominal wall (Figure 1).
She does not have any notable electrolyte derangements that would account for her weakness, and the normal creatinine kinase lowers the probability of a myopathy and excludes rhabdomyolysis. Progression of weakness from proximal to distal muscles in a symmetric fashion is consistent with botulism, and she has an intra-abdominal wound infection that could be harboring Clostridium botulinum. Nonetheless, the normal cranial nerve exam and the rarity of botulism occurring with surgical wounds argue against this diagnosis. She should receive intravenous (IV) thiamine for the possibility of beriberi. A lumbar puncture should be performed to assess for albuminocytologic dissociation, which can be seen in patients with GBS.
The patient received high-dose IV thiamine, IV vancomycin, IV piperacillin-tazobactam, and acetaminophen. Over the subsequent 4 hours, her anion gap acidosis worsened. She declined arterial puncture. Repeat venous blood gas was: pH 7.22, PCO2 28 mmHg, and bicarbonate 11 mmol/L. Lactate and glucose were normal. Serum osmolarity was 292 mmol/kg (reference range: 283-301 mmol/kg). She was started on an IV sodium bicarbonate infusion without improvement in her acidemia.
An acute anion gap metabolic acidosis suggests a limited differential diagnosis that includes lactic acidosis, D-lactic acidosis, severe starvation ketoacidosis, acute renal failure, salicylate, or other drug or poison ingestion. Starvation ketoacidosis may be contributing, but a bicarbonate value this low would be unusual. There is no history of alcohol use or other ingestions, and the normal serum osmolality and low osmolal gap (less than 10 mOsm/kg) argue against a poisoning with ethanol, ethylene glycol, or methanol. The initial combined anion gap metabolic acidosis and respiratory alkalosis is consistent with salicylate toxicity, but she does not report aspirin ingestion. Acetaminophen use in the setting of malnutrition or starvation physiology raises the possibility of 5-oxoproline accumulation.
Routine serum lactate does not detect D-lactate, which is produced by colonic bacteria and has been reported in short bowel syndrome and following intestinal bypass surgery. This may occur weeks to months after intestinal procedures, following ingestion of a heavy carbohydrate load, and almost invariably presents with altered mental status and increased anion gap metabolic acidosis, although generalized weakness has been reported.
A surgical consultant drained her wound infection. Fluid Gram stain was negative. D-lactate, salicylate and acetaminophen levels were undetectable. Thiamine pyrophosphate level was 229 nmol/L (reference range: 78-185 nmol/L). Acetaminophen was discontinued and N-acetylcysteine infusion was started for possible 5-oxoprolinemia. Her anion gap acidosis rapidly improved. Twelve hours after admission, she reported sudden onset of blurry vision. Her vital signs were: temperature 37oC, heart rate 110 beats per minute, respiratory rate 40 breaths per minute, blood pressure 168/90, and oxygen saturation 100% on ambient air. Telemetry showed ventricular bigeminy. On examination, she was unable to abduct her right eye; muscle strength was 1/5 in all extremities; biceps, ankle, and patellar reflexes were absent.
Her neurological deficits have progressed over hours to near complete paralysis, asymmetric cranial nerve paresis, and areflexia. Although botulism can cause blurred vision and absent deep tendon reflexes, patients almost always have symmetrical bulbar findings followed by descending paralysis. Should the “numbness” in her arms reported earlier represent undetected sensory deficits, this, too would be inconsistent with botulism.
A diagnosis of GBS ties together several aspects of her presentation and clinical course. Several variants show different patterns of weakness and may involve cranial nerves. Her tachypnea and dyspnea are concerning signs of potential impending respiratory failure. The ventricular bigeminy and mild hypertension could represent autonomic dysfunction that is seen in many cases of GBS.
She was intubated for airway protection. Computed tomography angiography and magnetic resonance imaging of her brain were normal. Cerebral spinal fluid analysis obtained through lumbar puncture showed the following: white blood cell count 3/μL, red blood cell count 11/μL, protein 63 mg/dL (reference range: 15-60mg/dL), and glucose 128 mg/dL (reference range: 40-80mg/dL).
The lumbar puncture is consistent with GBS given the slightly elevated protein and cell count well below 50/μL. Given the severity of her symptoms, treatment with IV immunoglobulin or plasmapheresis should be initiated. Nerve conduction studies (NCS) and electromyography (EMG) are indicated for diagnostic confirmation.
EMG and NCS revealed a severe sensorimotor polyneuropathy with demyelinating features including a conduction block at a noncompressible site, consistent with AIDP. Left sural nerve biopsy confirmed acute demyelinating and mild axonal neuropathy (Figure 2). On hospital day 2, treatment with IV immunoglobulins (IVIG) was initiated; however, she developed anaphylaxis following her second administration and subsequently received plasmapheresis. A tracheostomy was performed for respiratory muscle weakness, and she was discharged to a nursing facility. C. botulinum cultures from the wound eventually returned negative. Following her hospitalization, a serum 5-oxoproline level sent 10 hours after admission returned as elevated, confirming the additional diagnosis of 5-oxoprolinemia. On follow-up, she can sit up and feed herself without assistance, and her gait continues to improve with physical therapy.
DISCUSSION
This patient presented with rapidly progressive weakness that developed in the 2 weeks following bariatric surgery. In the postsurgical setting, patient complaints of weakness are commonly encountered and can pose a diagnostic challenge. Asthenia (ie, general loss of strength or energy) is frequently reported in the immediate postoperative period, and may result from the stress of surgery, pain, deconditioning, or infection. This must be distinguished from true neurologic weakness, which results from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, or muscle. The initial history can help elucidate the inciting events such as preceding surgery, infections or ingestions, and can also categorize the pattern of weakness. The neurologic examination can localize the pathology within the neuraxis. EMG and NCS can distinguish neuropathy from radiculopathy, and categorize the process as axonal, demyelinating, or mixed. In this case, the oculomotor weakness, sensory abnormalities and areflexia signaled a severe sensorimotor polyneuropathy, and EMG/NCS confirmed a demyelinating process consistent with GBS.
Guillain-Barré syndrome is an acute, immune-mediated polyneuropathy. Patients with GBS often present with a preceding respiratory or diarrheal illness; however, the stress of a recent surgery can serve as an inciting event. The syndrome, acute postgastric reduction surgery (APGARS) neuropathy, was introduced in the literature in 2002, describing 3 patients who presented with progressive vomiting, weakness, and hyporeflexia following bariatric surgery.1 The term has been used to describe bariatric surgery patients who developed postoperative quadriparesis, cranial nerve deficits, and respiratory compromise.2 Given the clinical heterogeneity in the literature with relation to APGARS, it is probable that the cases described could result from multiple etiologies. While GBS is purely immune-mediated and can be precipitated by the stress of surgery itself, postbariatric surgery patients are susceptible to many nutritional deficiencies that can lead to similar presentations.3 For example, thiamine (vitamin B1) and cobalamin (vitamin B12) deficiencies cause distinct postbariatric surgery neuropathies.4 Thiamine deficiency may manifest weeks to months after surgery and can rapidly progress, whereas cobalamin deficiency generally develops over 3 to 5 years. Both of these syndromes demonstrate an axonal pattern of nerve injury on EMG/NCS, in contrast to the demyelinating pattern typically seen in GBS. In addition, bariatric surgery patients are at higher risk for copper deficiency, which usually presents as a myeloneuropathy with subacute gait decline and upper motor neuron signs including spasticity.
Although GBS classically presents with symmetric ascending weakness and sensory abnormalities, it may manifest in myriad ways. Factors influencing the presentation include the types of nerve fibers involved (motor, sensory, cranial or autonomic), the predominant mode of injury (axonal vs demyelinating), and the presence or absence of alteration in consciousness.5 The most common form of GBS is AIDP. The classic presentation involves paresthesias in the fingertips and toes followed by lower extremity weakness that ascends over hours to days to involve the arms and potentially the muscles of respiration. A minority of patients with GBS first experience weakness in the upper extremities or facial muscles, and oculomotor involvement is rare.5 Pain is common and often severe.6 Dysautonomia affects most patients with GBS and may manifest as labile blood pressure or arrhythmias.5 Several variant GBS presentation patterns have been described, including acute motor axonal neuropathy, a pure motor form of GBS; ophthalmoplegia, ataxia, and areflexia in Miller Fisher syndrome; and alteration in consciousness, hyperreflexia, ataxia, and ophthalmoparesis in Bickerstaff’s brain stem encephalitis.5
Patients with GBS can progress rapidly to respiratory failure. Serial neurologic exams may signal the diagnosis and inform triage to the appropriate level of care. Measurement of bedside pulmonary function, including mean inspiratory force and functional vital capacity, help to determine if there is weakness of diaphragmatic muscles. Patients with signs or symptoms of diaphragmatic weakness require monitoring in an intensive care unit and potentially early intubation. Treatment with IVIG or plasmapheresis has been found to hasten recovery from GBS, including earlier improvement in muscle strength and a reduced need for mechanical ventilation.7 Treatment selection is based on available resources as both modalities are felt to be equivalent.The majority of patients with GBS make a full recovery over a period of weeks to months, although many have persistent motor weakness. Despite immunotherapy, up to 20% of patients remain severely disabled and approximately 5% die.8 Advanced age, rapid progression of weakness over a period of less than 72 hours, need for mechanical ventilation, and absent compound muscle action potentials on NCS are all associated with prolonged and incomplete recovery.9
This patient developed respiratory failure within 12 hours of hospitalization, prior to being diagnosed with GBS. Even in that short time, the treating clinicians encountered a series of clinical diversions. The initial proximal pattern of muscle weakness suggested a possible myopathic process; the wound infection introduced the possibility of botulism; obesity and recent bariatric surgery triggered concern for thiamine deficiency; and the anion gap acidosis from 5-oxoprolinemia created yet another clinical detour. While the path from presentation to diagnosis is seldom a straight line, when faced with rapidly progressive weakness, it is paramount to forge ahead with an efficient diagnostic evaluation and timely therapeutic intervention.
KEY TEACHING POINTS
- A complaint of general weakness requires distinction between asthenia (ie, general loss of strength or energy) and true neuromuscular weakness from dysfunction of the brain, spinal cord, nerve, neuromuscular junction, and/or muscle.
- Guillain-Barré syndrome may present in a variety of atypical fashions not limited to ascending, distally predominant weakness.
- Acute postgastric reduction surgery neuropathy should be considered in patients presenting with weakness, vomiting, or hyporeflexia after bariatric surgery.
- Acute inflammatory demyelinating polyneuropathy may rapidly progress to respiratory failure, and warrants serial neurologic examinations, monitoring of pulmonary function, and an expedited diagnostic evaluation.
Disclosure
Nothing to report.
1. Akhtar M, Collins MP, Kissel JT. Acute postgastric reduction surgery (APGARS) Neuropathy: A polynutritional, multisystem disorder. Neurology. 2002;58:A68. PubMed
2. Chang CG, Adams-Huet B, Provost DA. Acute post-gastric reduction surgery (APGARS) neuropathy. Obes Surg. 2004;14(2):182-189. PubMed
3. Chang CG, Helling TS, Black WE, Rymer MM. Weakness after gastric bypass. Obes Surg. 2002;12(4):592-597. PubMed
4. Shankar P, Boylan M, Sriram K. Micronutrient deficiencies after bariatric surgery. Nutrition. 2010;26(11-12):1031-1037. PubMed
5. Dimachkie MM, Barohn RJ. Guillain-Barré syndrome and variants. Neurol Clin. 2013;31(2):491-510. PubMed
6. Ruts L, Drenthen J, Jongen JL, et al. Pain in Guillain-Barré syndrome: a long-term follow-up study. Neurology. 2010;75(16):1439-1447. PubMed
7. Hughes RAC, Wijdicks EFM, Barohn R, et al: Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: immunotherapy for Guillain-Barré syndrome: report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2003;61:736-740. PubMed
8. Hughes RA, Swan AV, Raphaël JC, Annane D, van Koningsveld R, van Doorn PA. Immunotherapy for Guillain-Barré syndrome: a systematic review. Brain. 2007;130(Pt 9):2245-2257. PubMed
9. Rajabally YA, Uncini A. Outcome and predictors in Guillain-Barré syndrome. J Neurol Neurosurg Psychiatry. 2012;83(7):711-718. PubMed
1. Akhtar M, Collins MP, Kissel JT. Acute postgastric reduction surgery (APGARS) Neuropathy: A polynutritional, multisystem disorder. Neurology. 2002;58:A68. PubMed
2. Chang CG, Adams-Huet B, Provost DA. Acute post-gastric reduction surgery (APGARS) neuropathy. Obes Surg. 2004;14(2):182-189. PubMed
3. Chang CG, Helling TS, Black WE, Rymer MM. Weakness after gastric bypass. Obes Surg. 2002;12(4):592-597. PubMed
4. Shankar P, Boylan M, Sriram K. Micronutrient deficiencies after bariatric surgery. Nutrition. 2010;26(11-12):1031-1037. PubMed
5. Dimachkie MM, Barohn RJ. Guillain-Barré syndrome and variants. Neurol Clin. 2013;31(2):491-510. PubMed
6. Ruts L, Drenthen J, Jongen JL, et al. Pain in Guillain-Barré syndrome: a long-term follow-up study. Neurology. 2010;75(16):1439-1447. PubMed
7. Hughes RAC, Wijdicks EFM, Barohn R, et al: Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: immunotherapy for Guillain-Barré syndrome: report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2003;61:736-740. PubMed
8. Hughes RA, Swan AV, Raphaël JC, Annane D, van Koningsveld R, van Doorn PA. Immunotherapy for Guillain-Barré syndrome: a systematic review. Brain. 2007;130(Pt 9):2245-2257. PubMed
9. Rajabally YA, Uncini A. Outcome and predictors in Guillain-Barré syndrome. J Neurol Neurosurg Psychiatry. 2012;83(7):711-718. PubMed
© 2017 Society of Hospital Medicine
Health information exchange in US hospitals: The current landscape and a path to improved information sharing
The US healthcare system is highly fragmented, with patients typically receiving treatment from multiple providers during an episode of care and from many more providers over their lifetime.1,2 As patients move between care delivery settings, whether and how their information follows them is determined by a haphazard and error-prone patchwork of telephone, fax, and electronic communication channels.3 The existence of more robust electronic communication channels is often dictated by factors such as which providers share the same electronic health record (EHR) vendor rather than which providers share the highest volume of patients. As a result, providers often make clinical decisions with incomplete information, increasing the chances of misdiagnosis, unsafe or suboptimal treatment, and duplicative utilization.
Providers across the continuum of care encounter challenges to optimal clinical decision-making as a result of incomplete information. These are particularly problematic among clinicians in hospitals and emergency departments (EDs). Clinical decision-making in EDs often involves urgent and critical conditions in which decisions are made under pressure. Time constraints limit provider ability to find key clinical information to accurately diagnose and safely treat patients.4-6 Even for planned inpatient care, providers are often unfamiliar with patients, and they make safer decisions when they have full access to information from outside providers.7,8
Transitions of care between hospitals and primary care settings are also fraught with gaps in information sharing. Clinical decisions made in primary care can set patients on treatment trajectories that are greatly affected by the quality of information available to the care team at the time of initial diagnosis as well as in their subsequent treatment. Primary care physicians are not universally notified when their patients are hospitalized and may not have access to detailed information about the hospitalization, which can impair their ability to provide high quality care.9-11
Widespread and effective electronic health information exchange (HIE) holds the potential to address these challenges.3 With robust, interconnected electronic systems, key pieces of a patient’s health record can be electronically accessed and reconciled during planned and unplanned care transitions. The concept of HIE is simple—make all relevant patient data available to the clinical care team at the point of care, regardless of where that information was generated. The estimated value of nationwide interoperable EHR adoption suggests large savings from the more efficient, less duplicative, and higher quality care that likely results.12,13
There has been substantial funding and activity at federal, state, and local levels to promote the development of HIE in the US. The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act has the specific goal of accelerating adoption and use of certified EHR technology coupled with the ability to exchange clinical information to support patient care.14 The HITECH programs supported specific types of HIE that were believed to be particularly critical to improving patient care and included them in the federally-defined criteria for Meaningful Use (MU) of EHRs (ie, providers receive financial incentives for achieving specific objectives). The MU criteria evolve, moving from data capture in stage 1 to improved patient outcomes in stage 3.15 The HIE criteria focus on sending and receiving summary-of-care records during care transitions.
Despite the clear benefits of HIE and substantial support stated in policy initiatives, the spread of national HIE has been slow. Today, HIE in the US is highly heterogeneous: as a result of multiple federal-, state-, community-, enterprise- and EHR vendor-level efforts, only some provider organizations are able to engage in HIE with the other provider organizations with which they routinely share patients. In this review, we offer a framework and a corresponding set of definitions to understand the current state of HIE in the US. We describe key challenges to HIE progress and offer insights into the likely path to ensure that clinicians have routine, electronic access to patient information.
FOUR KEY DIMENSIONS OF HEALTH INFORMATION EXCHANGE
While the concept of HIE is simple—electronic access to clinical information across healthcare settings—the operationalization of HIE occurs in many different ways.16 While the terms “health information exchange” and “interoperability” are often used interchangeably, they can have different meanings. In this section, we describe 4 important dimensions that serve as a framework for understanding any given effort to enable HIE (Table).
(1) What Is Exchanged? Types of Information
The term “health information exchange” is ambiguous with respect to the type(s) of information that are accessible. Health information exchange may refer to the process of 2 providers electronically sharing a wide range of data, from a single type of information (eg, lab test results), summary of care records, to complete patient records.17 Part of this ambiguity may stem from uncertainty about the scope of information that should be shared, and how this varies based on the type of clinical encounter. For example, critical types of information in the ED setting may differ from those relevant to a primary care team after a referral. While the ability to access only particular types of information will not address all information gaps, providing access to complete patient records may result in information overload that inhibits the ability to find the subset of information relevant in a given clinical encounter.
(2) Who is Exchanging? Relationship Between Provider Organizations
The types of information accessed electronically are effectively agnostic to the relationship between the provider organizations that are sharing information. Traditionally, HIE has been considered as information that is electronically shared among 2 or more unaffiliated organizations. However, there is increasing recognition that some providers may not have electronic access to all information about their patients that exists within their organization, often after a merger or acquisition between 2 providers with different EHR systems.18,19 In these cases, a primary care team in a large integrated delivery system may have as many information gaps as a primary care team in a small, independent practice. Fulfilling clinical information needs may require both intra- and interorganizational HIE, which complicates the design of HIE processes and how the care team approaches incorporating information from both types of organizations into their decision-making. It is also important to recognize that some provider organizations, particularly small, rural practices, may not have the information technology and connectivity infrastructure required to engage in HIE.
(3) How Is Information Exchanged? Types of Electronic Access: Push vs Pull Exchange
To minimize information gaps, electronic access to information from external settings needs to offer both “push” and “pull” options. Push exchange, which can direct information electronically to a targeted recipient, works in scenarios in which there is a known information gap and known information source. The classic use for push exchange is care coordination, such as primary care physician-specialist referrals or hospital-primary care physician transitions postdischarge. Pull exchange accommodates scenarios in which there is a known information gap but the source(s) of information are unknown; it requires that clinical care teams search for and locate the clinical information that exists about the patient in external settings. Here, the classic use is emergency care in which the care team may encounter a new patient and want to retrieve records.
Widespread use of provider portals that offer view-only access into EHRs and other clinical data repositories maintained by external organizations complicate the picture. Portals are commonly used by hospitals to enable community providers to view information from a hospitalization.21 While this does not fall under the commonly held notion of HIE because no exchange occurs, portals support a pull approach to accessing information electronically among care settings that treat the same patients but use different EHRs.
Regardless of whether information is pushed or pulled, this may happen with varying degrees of human effort. This distinction gives rise to the difference between HIE and interoperability. Health information exchange reflects the ability of EHRs to exchange information, while interoperability additionally requires that EHRs be able to use exchanged information. From an operational perspective, the key distinction between HIE and interoperability is the extent of human involvement. Health information exchange requires that a human read and decide how to enter information from external settings (eg, a chart in PDF format sent between 2 EHRs), while interoperability enables the EHR that receives the information to understand the content and automatically triage or reconcile information, such as a medication list, without any human action.21 Health information exchange, therefore, relies on the diligence of the receiving clinician, while interoperability does not.
(4) What Governance Entity Defines the “Rules” of Exchange?
When more than 1 provider organization shares patient-identified data, a governance entity must specify the framework that governs the exchange. While the specifics of HIE governance vary, there are 3 predominant types of HIE networks, based on the type of organization that governs exchange: enterprise HIE networks, EHR vendor HIE networks or community HIE networks.
Enterprise HIE networks exist when 1 or more provider organizations electronically share clinical information to support patient care with some restriction, beyond geography, that dictates which organizations are involved. Typically, restrictions are driven by strategic, proprietary interests.22,23 Although broad-based information access across settings would be in the best interest of the patient, provider organizations are sensitive to the competitive implications of sharing data and may pursue such sharing in a strategic way.24 A common scenario is when hospitals choose to strategically affiliate with select ambulatory providers and exclusively exchange information with them. This should facilitate better care coordination for patients shared by the hospital and those providers but can also benefit the hospital by increasing the referrals from those providers. While there is little direct evidence quantifying the extent to which this type of strategic sharing takes place, there have been anecdotal reports as well as indirect findings that for-profit hospitals in competitive markets are less likely to share patient data.19,25
EHR vendor HIE networks exist when exchange occurs within a community of provider organizations that use an EHR from the same vendor. A subset of EHR vendors have made this capability available; EPIC’s CareEverywhere solution27 is the best-known example. Providers with an EPIC EHR are able to query for and retrieve summary of care records and other documents from any provider organization with EPIC that has activated this functionality. There are also multivendor efforts, such as CommonWell27 and the Sequoia Project’s Carequality collaborative,28 which are initiatives that seek to provide a common interoperability framework across a diverse set of stakeholders, including provider organizations with different EHR systems, in a similar fashion to HIE modules like CareEverywhere. To date, growth in these cross-vendor collaborations has been slow, and they have limited participation. While HIE networks that involve EHR vendors are likely to grow, it is difficult to predict how quickly because they are still in an early phase of development, and face nontechnical barriers such as patient consent policies that vary between providers and across states.
Community HIE networks—also referred to as health information organizations (HIOs) or regional health information organizations (RHIOs)—exist when provider organizations in a community, frequently state-level organizations that were funded through HITECH grants,14 set up the technical infrastructure and governance approach to engage in HIE to improve patient care. In contrast to enterprise or vendor HIE networks that have pursued HIE in ways that appear strategically beneficial, the only restriction on participation in community and state HIE networks is usually geography because they view information exchange as a public good. Seventyone percent of hospital service areas (HSAs) are covered by at least 1 of the 106 operational HIOs, with 309,793 clinicians (licensed prescribers) participating in those exchange networks. Even with early infusions of public and other grant-funding, community HIE networks have experienced significant challenges to sustained operation, and many have ceased operating.29
Thus, for any given provider organization, available HIE networks are primarily shaped by 3 factors:
1. Geographic location, which determines the available community and state HIE networks (as well as other basic information technology and connectivity infrastructure); providers located outside the service areas covered by an operational HIE have little incentive to participate because they do not connect them to providers with whom they share patients. Providers in rural areas may simply not have the needed infrastructure to pursue HIE.
2. Type of organization to which they belong, which determines the available enterprise HIE networks; providers who are not members of large health systems may be excluded from participation in these types of networks.
3. EHR vendor, which determines whether they have access to an EHR vendor HIE network.
ONGOING CHALLENGES
Despite agreement about the substantial potential of HIE to reduce costs and increase the quality of care delivered across a broad range of providers, HIE progress has been slow. While HITECH has successfully increased EHR adoption in hospitals and ambulatory practices,30 HIE has lagged. This is largely because many complex, intertwined barriers must be addressed for HIE to be widespread.
Lack of a Defined Goal
The cost and complexity associated with the exchange of a single type of data (eg, medications) is substantially less than the cost and complexity of sharing complete patient records. There has been little industry consensus on the target goal—do we need to enable sharing of complete patient records across all providers, or will summary of care records suffice? If the latter, as is the focus of the current MU criteria, what types of information should be included in a summary of care record, and should content and/or structure vary depending on the type of care transition? While the MU criteria require the exchange of a summary of care record with defined data fields, it remains unclear whether this is the end state or whether we should continue to push towards broad-based sharing of all patient data as structured elements. Without a clear picture of the ideal end state, there has been significant heterogeneity in the development of HIE capabilities across providers and vendors, and difficulty coordinating efforts to continue to advance towards a nationwide approach. Addressing this issue also requires progress to define HIE usability, that is, how information from external organizations should be presented and integrated into clinical workflow and clinical decisions. Currently, where HIE is occurring and clinicians are receiving summary of care records, they find them long, cluttered, and difficult to locate key information.
Numerous, Complex Barriers Spanning Multiple Stakeholders
In the context of any individual HIE effort, even after the goal is defined, there are a myriad of challenges. In a recent survey of HIO efforts, many identified the following barriers as substantially impeding their development: establishing a sustainable business model, lack of funding, integration of HIE into provider workflow, limitations of current data standards, and working with governmental policy and mandates.30 What is notable about this list is that the barriers span an array of areas, including financial incentives and identifying a sustainable business model, technical barriers such as working within the limitations of data standards, and regulatory issues such as state laws that govern the requirements for patient consent to exchange personal health information. Overcoming any of these issues is challenging, but trying to tackle all of them simultaneously clearly reveals why progress has been slow. Further, resolving many of the issues involve different groups of stakeholders. For example, implementing appropriate patient consent procedures can require engaging with and harmonizing the regulations of multiple states, as well as the Health Insurance Portability and Accountability Act (HIPAA) and regulations specific to substance abuse data.
Weak or Misaligned Incentives
Among the top barriers to HIE efforts are those related to funding and lack of a sustainable business model. This reflects the fact that economic incentives in the current market have not promoted provider engagement in HIE. Traditional fee-for-service payment structures do not reward providers for avoiding duplicative care.31 Further, hospitals perceive patient data as a “key strategic asset, tying physicians and patients to their organization,”24 and are reluctant to share data with competitors. Compounding the problem is that EHR vendors have a business interest in using HIE as a lever to increase revenue. In the short-term, they can charge high fees for interfaces and other HIE-related functionality. In the long-run, vendors may try to influence provider choice of system by making it difficult to engage in cross-vendor exchange.32 Information blocking—when providers or vendors knowingly interfere with HIE33—reflects not only weak incentives, but perverse incentives. While not all providers and vendors experience perverse incentives, the combination of weak and perverse incentives suggests the need to strengthen incentives, so that both types of stakeholders are motivated to tackle the barriers to HIE development. Key to strengthening incentives are payers, who are thought to be the largest beneficiaries of HIE. Payers have been reluctant to make significant investments in HIE without a more active voice in its implementation,34 but a shift to value-based payment may increase their engagement.
THE PATH FORWARD
Despite the continued challenges to nationwide HIE, several policy and technology developments show promise. Stage 3 meaningful use criteria continue to build on previous stages in increasing HIE requirements, raising the threshold for electronic exchange and EHR integration of summary of care documentation in patient transitions. The recently released Medicare Access and CHIP Reauthorization Act (MACRA) Merit-based Incentive Payment System (MIPS) proposed rule replaces stage 3 meaningful use for Medicare-eligible providers with advancing care information (ACI), which accounts for 25% of a provider’s overall incentive reimbursement and includes multiple HIE criteria for providers to report as part of the base and performance score, and follows a very similar framework to stage 3 MU with its criteria regarding HIE.35 While the Centers for Medicare and Medicaid Services (CMS) has not publicly declared that stage 3 MU will be replaced by ACI for hospitals and Medicaid providers, it is likely it will align those programs with the newly announced Medicare incentives.
MACRA also included changes to the Office of the National Coordinator (ONC) EHR certification program in an attempt to further encourage HIE. Vendors and providers must attest that they do not engage in information blocking and will cooperate with the Office’s surveillance programs to that effect. They also must attest that, to the greatest degree possible, their EHR systems allow for bi-directional interoperability with other providers, including those with different EHR vendors, and timely access for patients to view, download, and transmit their health data. In addition, there are emerging federal efforts to pursue a more standardized approach to patient matching and harmonize consent policies across states. These types of new policy initiatives indicate a continued interest in prioritizing HIE and interoperability.21
New technologies may also help spur HIE progress. The newest policy initiatives from CMS, including stage 3 MU and MACRA, have looked to incentivize the creation of application program interfaces (APIs), a set of publicly available tools from EHR vendors to allow developers to build applications that can directly interface with, and retrieve data from, their EHRs. While most patient access to electronic health data to date has been accomplished via patient portals, open APIs would enable developers to build an array of programs for consumers to view, download, and transmit their health data.
Even more promising is the development of the newest Health Level 7 data transmission standard, Fast Healthcare Interoperability Resources (FHIR), which promises to dramatically simplify the technical aspects of interoperability. FHIR utilizes a human-readable, easy to implement modular “resources” standard that may alleviate many technical challenges that come with implementation of an HIE system, enabling cheaper and simpler interoperability.36 A consortium of EHR vendors are working together to test these standards.28 The new FHIR standards also work in conjunction with APIs to allow easier development of consumer-facing applications37 that may empower patients to take ownership of their health data.
CONCLUSION
While HIE holds great promise to reduce the cost and improve the quality of care, progress towards a nationally interoperable health system has been slow. Simply defining HIE and what types of HIE are needed in different clinical scenarios has proven challenging. The additional challenges to implementing HIE in complex technology, legal/regulatory, governance, and incentive environment are not without solutions. Continued policy interventions, private sector collaborations, and new technologies may hold the keys to realizing the vast potential of electronic HIE.
Disclosure
Nothing to report.
1. Pham HH, Schrag D, O’Malley AS, Wu B, Bach PB. Care patterns in Medicare and their implications for pay for performance. N Engl J Med. 2007;356(11):1130-1139. PubMed
2. Finnell JT, Overhage JM, Dexter PR, Perkins SM, Lane KA, McDonald CJ. Community clinical data exchange for emergency medicine patients. Paper presented at: AMIA Annual Symposium Proceedings 2003. PubMed
3. Bodenheimer T. Coordinating care-a perilous journey through the health care system. N Engl J Med. 2008;358(10):1064-1071. PubMed
4. Franczak MJ, Klein M, Raslau F, Bergholte J, Mark LP, Ulmer JL. In emergency departments, radiologists’ access to EHRs may influence interpretations and medical management. Health Aff (Millwood). 2014;33(5):800-806. PubMed
5. Shapiro JS, Kannry J, Kushniruk AW, Kuperman G; New York Clinical Information Exchange (NYCLIX) Clinical Advisory Subcommittee. Emergency physicians’ perceptions of health information exchange. J Am Med Inform Assoc. 2007;14(6):700-705. PubMed
6. Shapiro JS, Kannry J, Lipton M, et al. Approaches to patient health information exchange and their impact on emergency medicine. Ann Emerg Med. 2006;48(4):426-432. PubMed
7. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med.. 2004;79(2):186-194. PubMed
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9. Smith PC, Araya-Guerra R, Bublitz C, et al. MIssing clinical information during primary care visits. JAMA. 2005;293(5):565-571. PubMed
10. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital-based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381-386. PubMed
11. van Walraven C, Taljaard M, Bell CM, et al. A prospective cohort study found that provider and information continuity was low after patient discharge from hospital. J Clin Epidemiol. 2010;63(9):1000-1010. PubMed
12. Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B. The value of health care information exchange and interoperability. Health Aff (Millwood). 2005:(suppl)W5-10-W5-18. PubMed
13. Shekelle PG, Morton SC, Keeler EB. Costs and benefits of health information technology. Evid Rep Technol Assess (Full Rep). 2006;132:1-71. PubMed
14. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362(5):382-385. PubMed
15. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-504. PubMed
16. Kuperman G, McGowan J. Potential unintended consequences of health information exchange. J Gen Intern Med. 2013;28(12):1663-1666. PubMed
17. Mathematica Policy Research and Harvard School of Public Health. DesRoches CM, Painter MW, Jha AK, eds. Health Information Technology in the United States, 2015: Transition to a Post-HITECH World (Executive Summary). September 18, 2015. Princeton, NJ: Robert Wood Johnson Foundation; 2015.
18. O’Malley AS, Anglin G, Bond AM, Cunningham PJ, Stark LB, Yee T. Greenville & Spartanburg: Surging Hospital Employment of Physicians Poses Opportunities and Challenges. Washington, DC: Center for Studying Health System Change (HSC); February 2011. 6.
19. Katz A, Bond AM, Carrier E, Docteur E, Quach CW, Yee T. Cleveland Hospital Systems Expand Despite Weak Economy. Washington, DC: Center for Studying Health System Change (HSC); September 2010. 2.
20. Grossman JM, Bodenheimer TS, McKenzie K. Hospital-physician portals: the role of competition in driving clinical data exchange. Health Aff (Millwood). 2006;25(6):1629-1636. PubMed
21. De Salvo KB, Galvez E. Connecting Health and Care for the Nation A Shared Nationwide Interoperability Roadmap - Version 1.0. In: Office of the National Coordinator for Health Information Technology. ed 2015. https://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/interoperability-electronic-health-and-medical-records/connecting-health-care-nation-shared-nationwide-interoperability-roadmap-version-10/. Accessed September 3, 2016.
22. Adler-Milstein J, DesRoches C, Jha AK. Health information exchange among US hospitals. Am J Manag Care. 2011;17(11):761-768. PubMed
23. Vest JR. More than just a question of technology: factors related to hospitals’ adoption and implementation of health information exchange. Int J Med Inform. 2010;79(12):797-806. PubMed
24. Grossman JM, Kushner KL, November EA. Creating sustainable local health information exchanges: can barriers to stakeholder participation be overcome? Res Brief. 2008;2:1-12. PubMed
25. Grossman JM, Cohen G. Despite regulatory changes, hospitals cautious in helping physicians purchase electronic medical records. Issue Brief Cent Stud Health Syst Change 2008;123:1-4. PubMed
26. Kaelber DC, Waheed R, Einstadter D, Love TE, Cebul RD. Use and perceived value of health information exchange: one public healthcare system’s experience. Am J Manag Care. 2013;19(10 spec no):SP337-SP343. PubMed
27. Commonwell Health Alliance. http://www.commonwellalliance.org/, 2016. Accessed September 3, 2016.
28. Carequality. http://sequoiaproject.org/carequality/, 2016. Accessed September 3, 2016.
29. Adler-Milstein J, Lin SC, Jha AK. The number of health information exchange efforts is declining, leaving the viability of broad clinical data exchange uncertain. Health Aff (Millwood). 2016;35(7):1278-1285. PubMed
30. Adler-Milstein J, DesRoches CM, Kralovec P, et al. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff (Millwood). 2015:34(12):2174-2180. PubMed
31. Health IT Policy Committee Report to Congress: Challenges and Barriers to Interoperability. 2015. https://www.healthit.gov/facas/health-it-policy-committee/health-it-policy-committee-recommendations-national-coordinator-health-it. Accessed September 3, 2016.
32. Everson J, Adler-Milstein J. Engagement in hospital health information exchange is associated with vendor marketplace dominance. Health Aff (MIllwood). 2016;35(7):1286-1293. PubMed
33. Downing K, Mason J. ONC targets information blocking. J AHIMA. 2015;86(7):36-38. PubMed
34. Cross DA, Lin SC, Adler-Milstein J. Assessing payer perspectives on health information exchange. J Am Med Inform Assoc. 2016;23(2):297-303. PubMed
35. Centers for Medicare & Medicaid Services. MACRA: MIPS and APMs. 2016; https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/MACRA-MIPS-and-APMs.html. Accessed September 3, 2016.
36. Raths D. Trend: standards development. Catching FHIR. A new HL7 draft standard may boost web services development in healthcare. Healthc Inform. 2014;31(2):13,16. PubMed
37. Alterovitz G, Warner J, Zhang P, et al. SMART on FHIR genomics: facilitating
The US healthcare system is highly fragmented, with patients typically receiving treatment from multiple providers during an episode of care and from many more providers over their lifetime.1,2 As patients move between care delivery settings, whether and how their information follows them is determined by a haphazard and error-prone patchwork of telephone, fax, and electronic communication channels.3 The existence of more robust electronic communication channels is often dictated by factors such as which providers share the same electronic health record (EHR) vendor rather than which providers share the highest volume of patients. As a result, providers often make clinical decisions with incomplete information, increasing the chances of misdiagnosis, unsafe or suboptimal treatment, and duplicative utilization.
Providers across the continuum of care encounter challenges to optimal clinical decision-making as a result of incomplete information. These are particularly problematic among clinicians in hospitals and emergency departments (EDs). Clinical decision-making in EDs often involves urgent and critical conditions in which decisions are made under pressure. Time constraints limit provider ability to find key clinical information to accurately diagnose and safely treat patients.4-6 Even for planned inpatient care, providers are often unfamiliar with patients, and they make safer decisions when they have full access to information from outside providers.7,8
Transitions of care between hospitals and primary care settings are also fraught with gaps in information sharing. Clinical decisions made in primary care can set patients on treatment trajectories that are greatly affected by the quality of information available to the care team at the time of initial diagnosis as well as in their subsequent treatment. Primary care physicians are not universally notified when their patients are hospitalized and may not have access to detailed information about the hospitalization, which can impair their ability to provide high quality care.9-11
Widespread and effective electronic health information exchange (HIE) holds the potential to address these challenges.3 With robust, interconnected electronic systems, key pieces of a patient’s health record can be electronically accessed and reconciled during planned and unplanned care transitions. The concept of HIE is simple—make all relevant patient data available to the clinical care team at the point of care, regardless of where that information was generated. The estimated value of nationwide interoperable EHR adoption suggests large savings from the more efficient, less duplicative, and higher quality care that likely results.12,13
There has been substantial funding and activity at federal, state, and local levels to promote the development of HIE in the US. The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act has the specific goal of accelerating adoption and use of certified EHR technology coupled with the ability to exchange clinical information to support patient care.14 The HITECH programs supported specific types of HIE that were believed to be particularly critical to improving patient care and included them in the federally-defined criteria for Meaningful Use (MU) of EHRs (ie, providers receive financial incentives for achieving specific objectives). The MU criteria evolve, moving from data capture in stage 1 to improved patient outcomes in stage 3.15 The HIE criteria focus on sending and receiving summary-of-care records during care transitions.
Despite the clear benefits of HIE and substantial support stated in policy initiatives, the spread of national HIE has been slow. Today, HIE in the US is highly heterogeneous: as a result of multiple federal-, state-, community-, enterprise- and EHR vendor-level efforts, only some provider organizations are able to engage in HIE with the other provider organizations with which they routinely share patients. In this review, we offer a framework and a corresponding set of definitions to understand the current state of HIE in the US. We describe key challenges to HIE progress and offer insights into the likely path to ensure that clinicians have routine, electronic access to patient information.
FOUR KEY DIMENSIONS OF HEALTH INFORMATION EXCHANGE
While the concept of HIE is simple—electronic access to clinical information across healthcare settings—the operationalization of HIE occurs in many different ways.16 While the terms “health information exchange” and “interoperability” are often used interchangeably, they can have different meanings. In this section, we describe 4 important dimensions that serve as a framework for understanding any given effort to enable HIE (Table).
(1) What Is Exchanged? Types of Information
The term “health information exchange” is ambiguous with respect to the type(s) of information that are accessible. Health information exchange may refer to the process of 2 providers electronically sharing a wide range of data, from a single type of information (eg, lab test results), summary of care records, to complete patient records.17 Part of this ambiguity may stem from uncertainty about the scope of information that should be shared, and how this varies based on the type of clinical encounter. For example, critical types of information in the ED setting may differ from those relevant to a primary care team after a referral. While the ability to access only particular types of information will not address all information gaps, providing access to complete patient records may result in information overload that inhibits the ability to find the subset of information relevant in a given clinical encounter.
(2) Who is Exchanging? Relationship Between Provider Organizations
The types of information accessed electronically are effectively agnostic to the relationship between the provider organizations that are sharing information. Traditionally, HIE has been considered as information that is electronically shared among 2 or more unaffiliated organizations. However, there is increasing recognition that some providers may not have electronic access to all information about their patients that exists within their organization, often after a merger or acquisition between 2 providers with different EHR systems.18,19 In these cases, a primary care team in a large integrated delivery system may have as many information gaps as a primary care team in a small, independent practice. Fulfilling clinical information needs may require both intra- and interorganizational HIE, which complicates the design of HIE processes and how the care team approaches incorporating information from both types of organizations into their decision-making. It is also important to recognize that some provider organizations, particularly small, rural practices, may not have the information technology and connectivity infrastructure required to engage in HIE.
(3) How Is Information Exchanged? Types of Electronic Access: Push vs Pull Exchange
To minimize information gaps, electronic access to information from external settings needs to offer both “push” and “pull” options. Push exchange, which can direct information electronically to a targeted recipient, works in scenarios in which there is a known information gap and known information source. The classic use for push exchange is care coordination, such as primary care physician-specialist referrals or hospital-primary care physician transitions postdischarge. Pull exchange accommodates scenarios in which there is a known information gap but the source(s) of information are unknown; it requires that clinical care teams search for and locate the clinical information that exists about the patient in external settings. Here, the classic use is emergency care in which the care team may encounter a new patient and want to retrieve records.
Widespread use of provider portals that offer view-only access into EHRs and other clinical data repositories maintained by external organizations complicate the picture. Portals are commonly used by hospitals to enable community providers to view information from a hospitalization.21 While this does not fall under the commonly held notion of HIE because no exchange occurs, portals support a pull approach to accessing information electronically among care settings that treat the same patients but use different EHRs.
Regardless of whether information is pushed or pulled, this may happen with varying degrees of human effort. This distinction gives rise to the difference between HIE and interoperability. Health information exchange reflects the ability of EHRs to exchange information, while interoperability additionally requires that EHRs be able to use exchanged information. From an operational perspective, the key distinction between HIE and interoperability is the extent of human involvement. Health information exchange requires that a human read and decide how to enter information from external settings (eg, a chart in PDF format sent between 2 EHRs), while interoperability enables the EHR that receives the information to understand the content and automatically triage or reconcile information, such as a medication list, without any human action.21 Health information exchange, therefore, relies on the diligence of the receiving clinician, while interoperability does not.
(4) What Governance Entity Defines the “Rules” of Exchange?
When more than 1 provider organization shares patient-identified data, a governance entity must specify the framework that governs the exchange. While the specifics of HIE governance vary, there are 3 predominant types of HIE networks, based on the type of organization that governs exchange: enterprise HIE networks, EHR vendor HIE networks or community HIE networks.
Enterprise HIE networks exist when 1 or more provider organizations electronically share clinical information to support patient care with some restriction, beyond geography, that dictates which organizations are involved. Typically, restrictions are driven by strategic, proprietary interests.22,23 Although broad-based information access across settings would be in the best interest of the patient, provider organizations are sensitive to the competitive implications of sharing data and may pursue such sharing in a strategic way.24 A common scenario is when hospitals choose to strategically affiliate with select ambulatory providers and exclusively exchange information with them. This should facilitate better care coordination for patients shared by the hospital and those providers but can also benefit the hospital by increasing the referrals from those providers. While there is little direct evidence quantifying the extent to which this type of strategic sharing takes place, there have been anecdotal reports as well as indirect findings that for-profit hospitals in competitive markets are less likely to share patient data.19,25
EHR vendor HIE networks exist when exchange occurs within a community of provider organizations that use an EHR from the same vendor. A subset of EHR vendors have made this capability available; EPIC’s CareEverywhere solution27 is the best-known example. Providers with an EPIC EHR are able to query for and retrieve summary of care records and other documents from any provider organization with EPIC that has activated this functionality. There are also multivendor efforts, such as CommonWell27 and the Sequoia Project’s Carequality collaborative,28 which are initiatives that seek to provide a common interoperability framework across a diverse set of stakeholders, including provider organizations with different EHR systems, in a similar fashion to HIE modules like CareEverywhere. To date, growth in these cross-vendor collaborations has been slow, and they have limited participation. While HIE networks that involve EHR vendors are likely to grow, it is difficult to predict how quickly because they are still in an early phase of development, and face nontechnical barriers such as patient consent policies that vary between providers and across states.
Community HIE networks—also referred to as health information organizations (HIOs) or regional health information organizations (RHIOs)—exist when provider organizations in a community, frequently state-level organizations that were funded through HITECH grants,14 set up the technical infrastructure and governance approach to engage in HIE to improve patient care. In contrast to enterprise or vendor HIE networks that have pursued HIE in ways that appear strategically beneficial, the only restriction on participation in community and state HIE networks is usually geography because they view information exchange as a public good. Seventyone percent of hospital service areas (HSAs) are covered by at least 1 of the 106 operational HIOs, with 309,793 clinicians (licensed prescribers) participating in those exchange networks. Even with early infusions of public and other grant-funding, community HIE networks have experienced significant challenges to sustained operation, and many have ceased operating.29
Thus, for any given provider organization, available HIE networks are primarily shaped by 3 factors:
1. Geographic location, which determines the available community and state HIE networks (as well as other basic information technology and connectivity infrastructure); providers located outside the service areas covered by an operational HIE have little incentive to participate because they do not connect them to providers with whom they share patients. Providers in rural areas may simply not have the needed infrastructure to pursue HIE.
2. Type of organization to which they belong, which determines the available enterprise HIE networks; providers who are not members of large health systems may be excluded from participation in these types of networks.
3. EHR vendor, which determines whether they have access to an EHR vendor HIE network.
ONGOING CHALLENGES
Despite agreement about the substantial potential of HIE to reduce costs and increase the quality of care delivered across a broad range of providers, HIE progress has been slow. While HITECH has successfully increased EHR adoption in hospitals and ambulatory practices,30 HIE has lagged. This is largely because many complex, intertwined barriers must be addressed for HIE to be widespread.
Lack of a Defined Goal
The cost and complexity associated with the exchange of a single type of data (eg, medications) is substantially less than the cost and complexity of sharing complete patient records. There has been little industry consensus on the target goal—do we need to enable sharing of complete patient records across all providers, or will summary of care records suffice? If the latter, as is the focus of the current MU criteria, what types of information should be included in a summary of care record, and should content and/or structure vary depending on the type of care transition? While the MU criteria require the exchange of a summary of care record with defined data fields, it remains unclear whether this is the end state or whether we should continue to push towards broad-based sharing of all patient data as structured elements. Without a clear picture of the ideal end state, there has been significant heterogeneity in the development of HIE capabilities across providers and vendors, and difficulty coordinating efforts to continue to advance towards a nationwide approach. Addressing this issue also requires progress to define HIE usability, that is, how information from external organizations should be presented and integrated into clinical workflow and clinical decisions. Currently, where HIE is occurring and clinicians are receiving summary of care records, they find them long, cluttered, and difficult to locate key information.
Numerous, Complex Barriers Spanning Multiple Stakeholders
In the context of any individual HIE effort, even after the goal is defined, there are a myriad of challenges. In a recent survey of HIO efforts, many identified the following barriers as substantially impeding their development: establishing a sustainable business model, lack of funding, integration of HIE into provider workflow, limitations of current data standards, and working with governmental policy and mandates.30 What is notable about this list is that the barriers span an array of areas, including financial incentives and identifying a sustainable business model, technical barriers such as working within the limitations of data standards, and regulatory issues such as state laws that govern the requirements for patient consent to exchange personal health information. Overcoming any of these issues is challenging, but trying to tackle all of them simultaneously clearly reveals why progress has been slow. Further, resolving many of the issues involve different groups of stakeholders. For example, implementing appropriate patient consent procedures can require engaging with and harmonizing the regulations of multiple states, as well as the Health Insurance Portability and Accountability Act (HIPAA) and regulations specific to substance abuse data.
Weak or Misaligned Incentives
Among the top barriers to HIE efforts are those related to funding and lack of a sustainable business model. This reflects the fact that economic incentives in the current market have not promoted provider engagement in HIE. Traditional fee-for-service payment structures do not reward providers for avoiding duplicative care.31 Further, hospitals perceive patient data as a “key strategic asset, tying physicians and patients to their organization,”24 and are reluctant to share data with competitors. Compounding the problem is that EHR vendors have a business interest in using HIE as a lever to increase revenue. In the short-term, they can charge high fees for interfaces and other HIE-related functionality. In the long-run, vendors may try to influence provider choice of system by making it difficult to engage in cross-vendor exchange.32 Information blocking—when providers or vendors knowingly interfere with HIE33—reflects not only weak incentives, but perverse incentives. While not all providers and vendors experience perverse incentives, the combination of weak and perverse incentives suggests the need to strengthen incentives, so that both types of stakeholders are motivated to tackle the barriers to HIE development. Key to strengthening incentives are payers, who are thought to be the largest beneficiaries of HIE. Payers have been reluctant to make significant investments in HIE without a more active voice in its implementation,34 but a shift to value-based payment may increase their engagement.
THE PATH FORWARD
Despite the continued challenges to nationwide HIE, several policy and technology developments show promise. Stage 3 meaningful use criteria continue to build on previous stages in increasing HIE requirements, raising the threshold for electronic exchange and EHR integration of summary of care documentation in patient transitions. The recently released Medicare Access and CHIP Reauthorization Act (MACRA) Merit-based Incentive Payment System (MIPS) proposed rule replaces stage 3 meaningful use for Medicare-eligible providers with advancing care information (ACI), which accounts for 25% of a provider’s overall incentive reimbursement and includes multiple HIE criteria for providers to report as part of the base and performance score, and follows a very similar framework to stage 3 MU with its criteria regarding HIE.35 While the Centers for Medicare and Medicaid Services (CMS) has not publicly declared that stage 3 MU will be replaced by ACI for hospitals and Medicaid providers, it is likely it will align those programs with the newly announced Medicare incentives.
MACRA also included changes to the Office of the National Coordinator (ONC) EHR certification program in an attempt to further encourage HIE. Vendors and providers must attest that they do not engage in information blocking and will cooperate with the Office’s surveillance programs to that effect. They also must attest that, to the greatest degree possible, their EHR systems allow for bi-directional interoperability with other providers, including those with different EHR vendors, and timely access for patients to view, download, and transmit their health data. In addition, there are emerging federal efforts to pursue a more standardized approach to patient matching and harmonize consent policies across states. These types of new policy initiatives indicate a continued interest in prioritizing HIE and interoperability.21
New technologies may also help spur HIE progress. The newest policy initiatives from CMS, including stage 3 MU and MACRA, have looked to incentivize the creation of application program interfaces (APIs), a set of publicly available tools from EHR vendors to allow developers to build applications that can directly interface with, and retrieve data from, their EHRs. While most patient access to electronic health data to date has been accomplished via patient portals, open APIs would enable developers to build an array of programs for consumers to view, download, and transmit their health data.
Even more promising is the development of the newest Health Level 7 data transmission standard, Fast Healthcare Interoperability Resources (FHIR), which promises to dramatically simplify the technical aspects of interoperability. FHIR utilizes a human-readable, easy to implement modular “resources” standard that may alleviate many technical challenges that come with implementation of an HIE system, enabling cheaper and simpler interoperability.36 A consortium of EHR vendors are working together to test these standards.28 The new FHIR standards also work in conjunction with APIs to allow easier development of consumer-facing applications37 that may empower patients to take ownership of their health data.
CONCLUSION
While HIE holds great promise to reduce the cost and improve the quality of care, progress towards a nationally interoperable health system has been slow. Simply defining HIE and what types of HIE are needed in different clinical scenarios has proven challenging. The additional challenges to implementing HIE in complex technology, legal/regulatory, governance, and incentive environment are not without solutions. Continued policy interventions, private sector collaborations, and new technologies may hold the keys to realizing the vast potential of electronic HIE.
Disclosure
Nothing to report.
The US healthcare system is highly fragmented, with patients typically receiving treatment from multiple providers during an episode of care and from many more providers over their lifetime.1,2 As patients move between care delivery settings, whether and how their information follows them is determined by a haphazard and error-prone patchwork of telephone, fax, and electronic communication channels.3 The existence of more robust electronic communication channels is often dictated by factors such as which providers share the same electronic health record (EHR) vendor rather than which providers share the highest volume of patients. As a result, providers often make clinical decisions with incomplete information, increasing the chances of misdiagnosis, unsafe or suboptimal treatment, and duplicative utilization.
Providers across the continuum of care encounter challenges to optimal clinical decision-making as a result of incomplete information. These are particularly problematic among clinicians in hospitals and emergency departments (EDs). Clinical decision-making in EDs often involves urgent and critical conditions in which decisions are made under pressure. Time constraints limit provider ability to find key clinical information to accurately diagnose and safely treat patients.4-6 Even for planned inpatient care, providers are often unfamiliar with patients, and they make safer decisions when they have full access to information from outside providers.7,8
Transitions of care between hospitals and primary care settings are also fraught with gaps in information sharing. Clinical decisions made in primary care can set patients on treatment trajectories that are greatly affected by the quality of information available to the care team at the time of initial diagnosis as well as in their subsequent treatment. Primary care physicians are not universally notified when their patients are hospitalized and may not have access to detailed information about the hospitalization, which can impair their ability to provide high quality care.9-11
Widespread and effective electronic health information exchange (HIE) holds the potential to address these challenges.3 With robust, interconnected electronic systems, key pieces of a patient’s health record can be electronically accessed and reconciled during planned and unplanned care transitions. The concept of HIE is simple—make all relevant patient data available to the clinical care team at the point of care, regardless of where that information was generated. The estimated value of nationwide interoperable EHR adoption suggests large savings from the more efficient, less duplicative, and higher quality care that likely results.12,13
There has been substantial funding and activity at federal, state, and local levels to promote the development of HIE in the US. The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act has the specific goal of accelerating adoption and use of certified EHR technology coupled with the ability to exchange clinical information to support patient care.14 The HITECH programs supported specific types of HIE that were believed to be particularly critical to improving patient care and included them in the federally-defined criteria for Meaningful Use (MU) of EHRs (ie, providers receive financial incentives for achieving specific objectives). The MU criteria evolve, moving from data capture in stage 1 to improved patient outcomes in stage 3.15 The HIE criteria focus on sending and receiving summary-of-care records during care transitions.
Despite the clear benefits of HIE and substantial support stated in policy initiatives, the spread of national HIE has been slow. Today, HIE in the US is highly heterogeneous: as a result of multiple federal-, state-, community-, enterprise- and EHR vendor-level efforts, only some provider organizations are able to engage in HIE with the other provider organizations with which they routinely share patients. In this review, we offer a framework and a corresponding set of definitions to understand the current state of HIE in the US. We describe key challenges to HIE progress and offer insights into the likely path to ensure that clinicians have routine, electronic access to patient information.
FOUR KEY DIMENSIONS OF HEALTH INFORMATION EXCHANGE
While the concept of HIE is simple—electronic access to clinical information across healthcare settings—the operationalization of HIE occurs in many different ways.16 While the terms “health information exchange” and “interoperability” are often used interchangeably, they can have different meanings. In this section, we describe 4 important dimensions that serve as a framework for understanding any given effort to enable HIE (Table).
(1) What Is Exchanged? Types of Information
The term “health information exchange” is ambiguous with respect to the type(s) of information that are accessible. Health information exchange may refer to the process of 2 providers electronically sharing a wide range of data, from a single type of information (eg, lab test results), summary of care records, to complete patient records.17 Part of this ambiguity may stem from uncertainty about the scope of information that should be shared, and how this varies based on the type of clinical encounter. For example, critical types of information in the ED setting may differ from those relevant to a primary care team after a referral. While the ability to access only particular types of information will not address all information gaps, providing access to complete patient records may result in information overload that inhibits the ability to find the subset of information relevant in a given clinical encounter.
(2) Who is Exchanging? Relationship Between Provider Organizations
The types of information accessed electronically are effectively agnostic to the relationship between the provider organizations that are sharing information. Traditionally, HIE has been considered as information that is electronically shared among 2 or more unaffiliated organizations. However, there is increasing recognition that some providers may not have electronic access to all information about their patients that exists within their organization, often after a merger or acquisition between 2 providers with different EHR systems.18,19 In these cases, a primary care team in a large integrated delivery system may have as many information gaps as a primary care team in a small, independent practice. Fulfilling clinical information needs may require both intra- and interorganizational HIE, which complicates the design of HIE processes and how the care team approaches incorporating information from both types of organizations into their decision-making. It is also important to recognize that some provider organizations, particularly small, rural practices, may not have the information technology and connectivity infrastructure required to engage in HIE.
(3) How Is Information Exchanged? Types of Electronic Access: Push vs Pull Exchange
To minimize information gaps, electronic access to information from external settings needs to offer both “push” and “pull” options. Push exchange, which can direct information electronically to a targeted recipient, works in scenarios in which there is a known information gap and known information source. The classic use for push exchange is care coordination, such as primary care physician-specialist referrals or hospital-primary care physician transitions postdischarge. Pull exchange accommodates scenarios in which there is a known information gap but the source(s) of information are unknown; it requires that clinical care teams search for and locate the clinical information that exists about the patient in external settings. Here, the classic use is emergency care in which the care team may encounter a new patient and want to retrieve records.
Widespread use of provider portals that offer view-only access into EHRs and other clinical data repositories maintained by external organizations complicate the picture. Portals are commonly used by hospitals to enable community providers to view information from a hospitalization.21 While this does not fall under the commonly held notion of HIE because no exchange occurs, portals support a pull approach to accessing information electronically among care settings that treat the same patients but use different EHRs.
Regardless of whether information is pushed or pulled, this may happen with varying degrees of human effort. This distinction gives rise to the difference between HIE and interoperability. Health information exchange reflects the ability of EHRs to exchange information, while interoperability additionally requires that EHRs be able to use exchanged information. From an operational perspective, the key distinction between HIE and interoperability is the extent of human involvement. Health information exchange requires that a human read and decide how to enter information from external settings (eg, a chart in PDF format sent between 2 EHRs), while interoperability enables the EHR that receives the information to understand the content and automatically triage or reconcile information, such as a medication list, without any human action.21 Health information exchange, therefore, relies on the diligence of the receiving clinician, while interoperability does not.
(4) What Governance Entity Defines the “Rules” of Exchange?
When more than 1 provider organization shares patient-identified data, a governance entity must specify the framework that governs the exchange. While the specifics of HIE governance vary, there are 3 predominant types of HIE networks, based on the type of organization that governs exchange: enterprise HIE networks, EHR vendor HIE networks or community HIE networks.
Enterprise HIE networks exist when 1 or more provider organizations electronically share clinical information to support patient care with some restriction, beyond geography, that dictates which organizations are involved. Typically, restrictions are driven by strategic, proprietary interests.22,23 Although broad-based information access across settings would be in the best interest of the patient, provider organizations are sensitive to the competitive implications of sharing data and may pursue such sharing in a strategic way.24 A common scenario is when hospitals choose to strategically affiliate with select ambulatory providers and exclusively exchange information with them. This should facilitate better care coordination for patients shared by the hospital and those providers but can also benefit the hospital by increasing the referrals from those providers. While there is little direct evidence quantifying the extent to which this type of strategic sharing takes place, there have been anecdotal reports as well as indirect findings that for-profit hospitals in competitive markets are less likely to share patient data.19,25
EHR vendor HIE networks exist when exchange occurs within a community of provider organizations that use an EHR from the same vendor. A subset of EHR vendors have made this capability available; EPIC’s CareEverywhere solution27 is the best-known example. Providers with an EPIC EHR are able to query for and retrieve summary of care records and other documents from any provider organization with EPIC that has activated this functionality. There are also multivendor efforts, such as CommonWell27 and the Sequoia Project’s Carequality collaborative,28 which are initiatives that seek to provide a common interoperability framework across a diverse set of stakeholders, including provider organizations with different EHR systems, in a similar fashion to HIE modules like CareEverywhere. To date, growth in these cross-vendor collaborations has been slow, and they have limited participation. While HIE networks that involve EHR vendors are likely to grow, it is difficult to predict how quickly because they are still in an early phase of development, and face nontechnical barriers such as patient consent policies that vary between providers and across states.
Community HIE networks—also referred to as health information organizations (HIOs) or regional health information organizations (RHIOs)—exist when provider organizations in a community, frequently state-level organizations that were funded through HITECH grants,14 set up the technical infrastructure and governance approach to engage in HIE to improve patient care. In contrast to enterprise or vendor HIE networks that have pursued HIE in ways that appear strategically beneficial, the only restriction on participation in community and state HIE networks is usually geography because they view information exchange as a public good. Seventyone percent of hospital service areas (HSAs) are covered by at least 1 of the 106 operational HIOs, with 309,793 clinicians (licensed prescribers) participating in those exchange networks. Even with early infusions of public and other grant-funding, community HIE networks have experienced significant challenges to sustained operation, and many have ceased operating.29
Thus, for any given provider organization, available HIE networks are primarily shaped by 3 factors:
1. Geographic location, which determines the available community and state HIE networks (as well as other basic information technology and connectivity infrastructure); providers located outside the service areas covered by an operational HIE have little incentive to participate because they do not connect them to providers with whom they share patients. Providers in rural areas may simply not have the needed infrastructure to pursue HIE.
2. Type of organization to which they belong, which determines the available enterprise HIE networks; providers who are not members of large health systems may be excluded from participation in these types of networks.
3. EHR vendor, which determines whether they have access to an EHR vendor HIE network.
ONGOING CHALLENGES
Despite agreement about the substantial potential of HIE to reduce costs and increase the quality of care delivered across a broad range of providers, HIE progress has been slow. While HITECH has successfully increased EHR adoption in hospitals and ambulatory practices,30 HIE has lagged. This is largely because many complex, intertwined barriers must be addressed for HIE to be widespread.
Lack of a Defined Goal
The cost and complexity associated with the exchange of a single type of data (eg, medications) is substantially less than the cost and complexity of sharing complete patient records. There has been little industry consensus on the target goal—do we need to enable sharing of complete patient records across all providers, or will summary of care records suffice? If the latter, as is the focus of the current MU criteria, what types of information should be included in a summary of care record, and should content and/or structure vary depending on the type of care transition? While the MU criteria require the exchange of a summary of care record with defined data fields, it remains unclear whether this is the end state or whether we should continue to push towards broad-based sharing of all patient data as structured elements. Without a clear picture of the ideal end state, there has been significant heterogeneity in the development of HIE capabilities across providers and vendors, and difficulty coordinating efforts to continue to advance towards a nationwide approach. Addressing this issue also requires progress to define HIE usability, that is, how information from external organizations should be presented and integrated into clinical workflow and clinical decisions. Currently, where HIE is occurring and clinicians are receiving summary of care records, they find them long, cluttered, and difficult to locate key information.
Numerous, Complex Barriers Spanning Multiple Stakeholders
In the context of any individual HIE effort, even after the goal is defined, there are a myriad of challenges. In a recent survey of HIO efforts, many identified the following barriers as substantially impeding their development: establishing a sustainable business model, lack of funding, integration of HIE into provider workflow, limitations of current data standards, and working with governmental policy and mandates.30 What is notable about this list is that the barriers span an array of areas, including financial incentives and identifying a sustainable business model, technical barriers such as working within the limitations of data standards, and regulatory issues such as state laws that govern the requirements for patient consent to exchange personal health information. Overcoming any of these issues is challenging, but trying to tackle all of them simultaneously clearly reveals why progress has been slow. Further, resolving many of the issues involve different groups of stakeholders. For example, implementing appropriate patient consent procedures can require engaging with and harmonizing the regulations of multiple states, as well as the Health Insurance Portability and Accountability Act (HIPAA) and regulations specific to substance abuse data.
Weak or Misaligned Incentives
Among the top barriers to HIE efforts are those related to funding and lack of a sustainable business model. This reflects the fact that economic incentives in the current market have not promoted provider engagement in HIE. Traditional fee-for-service payment structures do not reward providers for avoiding duplicative care.31 Further, hospitals perceive patient data as a “key strategic asset, tying physicians and patients to their organization,”24 and are reluctant to share data with competitors. Compounding the problem is that EHR vendors have a business interest in using HIE as a lever to increase revenue. In the short-term, they can charge high fees for interfaces and other HIE-related functionality. In the long-run, vendors may try to influence provider choice of system by making it difficult to engage in cross-vendor exchange.32 Information blocking—when providers or vendors knowingly interfere with HIE33—reflects not only weak incentives, but perverse incentives. While not all providers and vendors experience perverse incentives, the combination of weak and perverse incentives suggests the need to strengthen incentives, so that both types of stakeholders are motivated to tackle the barriers to HIE development. Key to strengthening incentives are payers, who are thought to be the largest beneficiaries of HIE. Payers have been reluctant to make significant investments in HIE without a more active voice in its implementation,34 but a shift to value-based payment may increase their engagement.
THE PATH FORWARD
Despite the continued challenges to nationwide HIE, several policy and technology developments show promise. Stage 3 meaningful use criteria continue to build on previous stages in increasing HIE requirements, raising the threshold for electronic exchange and EHR integration of summary of care documentation in patient transitions. The recently released Medicare Access and CHIP Reauthorization Act (MACRA) Merit-based Incentive Payment System (MIPS) proposed rule replaces stage 3 meaningful use for Medicare-eligible providers with advancing care information (ACI), which accounts for 25% of a provider’s overall incentive reimbursement and includes multiple HIE criteria for providers to report as part of the base and performance score, and follows a very similar framework to stage 3 MU with its criteria regarding HIE.35 While the Centers for Medicare and Medicaid Services (CMS) has not publicly declared that stage 3 MU will be replaced by ACI for hospitals and Medicaid providers, it is likely it will align those programs with the newly announced Medicare incentives.
MACRA also included changes to the Office of the National Coordinator (ONC) EHR certification program in an attempt to further encourage HIE. Vendors and providers must attest that they do not engage in information blocking and will cooperate with the Office’s surveillance programs to that effect. They also must attest that, to the greatest degree possible, their EHR systems allow for bi-directional interoperability with other providers, including those with different EHR vendors, and timely access for patients to view, download, and transmit their health data. In addition, there are emerging federal efforts to pursue a more standardized approach to patient matching and harmonize consent policies across states. These types of new policy initiatives indicate a continued interest in prioritizing HIE and interoperability.21
New technologies may also help spur HIE progress. The newest policy initiatives from CMS, including stage 3 MU and MACRA, have looked to incentivize the creation of application program interfaces (APIs), a set of publicly available tools from EHR vendors to allow developers to build applications that can directly interface with, and retrieve data from, their EHRs. While most patient access to electronic health data to date has been accomplished via patient portals, open APIs would enable developers to build an array of programs for consumers to view, download, and transmit their health data.
Even more promising is the development of the newest Health Level 7 data transmission standard, Fast Healthcare Interoperability Resources (FHIR), which promises to dramatically simplify the technical aspects of interoperability. FHIR utilizes a human-readable, easy to implement modular “resources” standard that may alleviate many technical challenges that come with implementation of an HIE system, enabling cheaper and simpler interoperability.36 A consortium of EHR vendors are working together to test these standards.28 The new FHIR standards also work in conjunction with APIs to allow easier development of consumer-facing applications37 that may empower patients to take ownership of their health data.
CONCLUSION
While HIE holds great promise to reduce the cost and improve the quality of care, progress towards a nationally interoperable health system has been slow. Simply defining HIE and what types of HIE are needed in different clinical scenarios has proven challenging. The additional challenges to implementing HIE in complex technology, legal/regulatory, governance, and incentive environment are not without solutions. Continued policy interventions, private sector collaborations, and new technologies may hold the keys to realizing the vast potential of electronic HIE.
Disclosure
Nothing to report.
1. Pham HH, Schrag D, O’Malley AS, Wu B, Bach PB. Care patterns in Medicare and their implications for pay for performance. N Engl J Med. 2007;356(11):1130-1139. PubMed
2. Finnell JT, Overhage JM, Dexter PR, Perkins SM, Lane KA, McDonald CJ. Community clinical data exchange for emergency medicine patients. Paper presented at: AMIA Annual Symposium Proceedings 2003. PubMed
3. Bodenheimer T. Coordinating care-a perilous journey through the health care system. N Engl J Med. 2008;358(10):1064-1071. PubMed
4. Franczak MJ, Klein M, Raslau F, Bergholte J, Mark LP, Ulmer JL. In emergency departments, radiologists’ access to EHRs may influence interpretations and medical management. Health Aff (Millwood). 2014;33(5):800-806. PubMed
5. Shapiro JS, Kannry J, Kushniruk AW, Kuperman G; New York Clinical Information Exchange (NYCLIX) Clinical Advisory Subcommittee. Emergency physicians’ perceptions of health information exchange. J Am Med Inform Assoc. 2007;14(6):700-705. PubMed
6. Shapiro JS, Kannry J, Lipton M, et al. Approaches to patient health information exchange and their impact on emergency medicine. Ann Emerg Med. 2006;48(4):426-432. PubMed
7. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med.. 2004;79(2):186-194. PubMed
8. Kaelber DC, Bates DW. Health information exchange and patient safety. J Biomed Inform. 2007;40(suppl 6):S40-S45. PubMed
9. Smith PC, Araya-Guerra R, Bublitz C, et al. MIssing clinical information during primary care visits. JAMA. 2005;293(5):565-571. PubMed
10. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital-based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381-386. PubMed
11. van Walraven C, Taljaard M, Bell CM, et al. A prospective cohort study found that provider and information continuity was low after patient discharge from hospital. J Clin Epidemiol. 2010;63(9):1000-1010. PubMed
12. Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B. The value of health care information exchange and interoperability. Health Aff (Millwood). 2005:(suppl)W5-10-W5-18. PubMed
13. Shekelle PG, Morton SC, Keeler EB. Costs and benefits of health information technology. Evid Rep Technol Assess (Full Rep). 2006;132:1-71. PubMed
14. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362(5):382-385. PubMed
15. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-504. PubMed
16. Kuperman G, McGowan J. Potential unintended consequences of health information exchange. J Gen Intern Med. 2013;28(12):1663-1666. PubMed
17. Mathematica Policy Research and Harvard School of Public Health. DesRoches CM, Painter MW, Jha AK, eds. Health Information Technology in the United States, 2015: Transition to a Post-HITECH World (Executive Summary). September 18, 2015. Princeton, NJ: Robert Wood Johnson Foundation; 2015.
18. O’Malley AS, Anglin G, Bond AM, Cunningham PJ, Stark LB, Yee T. Greenville & Spartanburg: Surging Hospital Employment of Physicians Poses Opportunities and Challenges. Washington, DC: Center for Studying Health System Change (HSC); February 2011. 6.
19. Katz A, Bond AM, Carrier E, Docteur E, Quach CW, Yee T. Cleveland Hospital Systems Expand Despite Weak Economy. Washington, DC: Center for Studying Health System Change (HSC); September 2010. 2.
20. Grossman JM, Bodenheimer TS, McKenzie K. Hospital-physician portals: the role of competition in driving clinical data exchange. Health Aff (Millwood). 2006;25(6):1629-1636. PubMed
21. De Salvo KB, Galvez E. Connecting Health and Care for the Nation A Shared Nationwide Interoperability Roadmap - Version 1.0. In: Office of the National Coordinator for Health Information Technology. ed 2015. https://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/interoperability-electronic-health-and-medical-records/connecting-health-care-nation-shared-nationwide-interoperability-roadmap-version-10/. Accessed September 3, 2016.
22. Adler-Milstein J, DesRoches C, Jha AK. Health information exchange among US hospitals. Am J Manag Care. 2011;17(11):761-768. PubMed
23. Vest JR. More than just a question of technology: factors related to hospitals’ adoption and implementation of health information exchange. Int J Med Inform. 2010;79(12):797-806. PubMed
24. Grossman JM, Kushner KL, November EA. Creating sustainable local health information exchanges: can barriers to stakeholder participation be overcome? Res Brief. 2008;2:1-12. PubMed
25. Grossman JM, Cohen G. Despite regulatory changes, hospitals cautious in helping physicians purchase electronic medical records. Issue Brief Cent Stud Health Syst Change 2008;123:1-4. PubMed
26. Kaelber DC, Waheed R, Einstadter D, Love TE, Cebul RD. Use and perceived value of health information exchange: one public healthcare system’s experience. Am J Manag Care. 2013;19(10 spec no):SP337-SP343. PubMed
27. Commonwell Health Alliance. http://www.commonwellalliance.org/, 2016. Accessed September 3, 2016.
28. Carequality. http://sequoiaproject.org/carequality/, 2016. Accessed September 3, 2016.
29. Adler-Milstein J, Lin SC, Jha AK. The number of health information exchange efforts is declining, leaving the viability of broad clinical data exchange uncertain. Health Aff (Millwood). 2016;35(7):1278-1285. PubMed
30. Adler-Milstein J, DesRoches CM, Kralovec P, et al. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff (Millwood). 2015:34(12):2174-2180. PubMed
31. Health IT Policy Committee Report to Congress: Challenges and Barriers to Interoperability. 2015. https://www.healthit.gov/facas/health-it-policy-committee/health-it-policy-committee-recommendations-national-coordinator-health-it. Accessed September 3, 2016.
32. Everson J, Adler-Milstein J. Engagement in hospital health information exchange is associated with vendor marketplace dominance. Health Aff (MIllwood). 2016;35(7):1286-1293. PubMed
33. Downing K, Mason J. ONC targets information blocking. J AHIMA. 2015;86(7):36-38. PubMed
34. Cross DA, Lin SC, Adler-Milstein J. Assessing payer perspectives on health information exchange. J Am Med Inform Assoc. 2016;23(2):297-303. PubMed
35. Centers for Medicare & Medicaid Services. MACRA: MIPS and APMs. 2016; https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/MACRA-MIPS-and-APMs.html. Accessed September 3, 2016.
36. Raths D. Trend: standards development. Catching FHIR. A new HL7 draft standard may boost web services development in healthcare. Healthc Inform. 2014;31(2):13,16. PubMed
37. Alterovitz G, Warner J, Zhang P, et al. SMART on FHIR genomics: facilitating
1. Pham HH, Schrag D, O’Malley AS, Wu B, Bach PB. Care patterns in Medicare and their implications for pay for performance. N Engl J Med. 2007;356(11):1130-1139. PubMed
2. Finnell JT, Overhage JM, Dexter PR, Perkins SM, Lane KA, McDonald CJ. Community clinical data exchange for emergency medicine patients. Paper presented at: AMIA Annual Symposium Proceedings 2003. PubMed
3. Bodenheimer T. Coordinating care-a perilous journey through the health care system. N Engl J Med. 2008;358(10):1064-1071. PubMed
4. Franczak MJ, Klein M, Raslau F, Bergholte J, Mark LP, Ulmer JL. In emergency departments, radiologists’ access to EHRs may influence interpretations and medical management. Health Aff (Millwood). 2014;33(5):800-806. PubMed
5. Shapiro JS, Kannry J, Kushniruk AW, Kuperman G; New York Clinical Information Exchange (NYCLIX) Clinical Advisory Subcommittee. Emergency physicians’ perceptions of health information exchange. J Am Med Inform Assoc. 2007;14(6):700-705. PubMed
6. Shapiro JS, Kannry J, Lipton M, et al. Approaches to patient health information exchange and their impact on emergency medicine. Ann Emerg Med. 2006;48(4):426-432. PubMed
7. Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med.. 2004;79(2):186-194. PubMed
8. Kaelber DC, Bates DW. Health information exchange and patient safety. J Biomed Inform. 2007;40(suppl 6):S40-S45. PubMed
9. Smith PC, Araya-Guerra R, Bublitz C, et al. MIssing clinical information during primary care visits. JAMA. 2005;293(5):565-571. PubMed
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