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A new perspective on immunotherapy
Chimeric antigen receptor-modified T cells represent a new approach to immune therapy in the treatment of hematologic malignancies. The clinical activity of chimeric antigen receptors (CARs) has been published in acute lymphoblastic leukemia (ALL)and chronic lymphocytic leukemia (CLL).1 The results have been remarkable, although only a very small number of patients have been treated. We are anticipating further clinical trials and further development of this technology for more wide spread treatment opportunities for patients. The CARs that have been the most successful clinically have a similar basic make-up. They are genetically modified T cells. The T cells are collected from the patients through leukapheresis, then they are genetically
modified to express an extracellular recognition domain that is connected in the intracellular signaling domains of the T cells. Various extracellular recognition domains have been engineered, but the target of CD19 has proven most successful in patients with B cell malignancies, and CD19 is widely expressed on CLL and B-cell ALL. The cells are infused back into the patient, sometimes after undergoing chemotherapy to lymphodeplete the patient (which may improve the recovery and persistence of the cells after treatment). The infusion responses have been
dramatic in some patients, with severe cytokine storm described in reports, usually several days after treatment.2 This is thought to reflect the very rapid identification of the target protein and response of the T cells to the target. Those patients with acute leukemia who have responded also appear to respond rapidly, with disappearance of blasts from the peripheral blood within a month. The cells have been detectable in some patients for months after treatment.
Please click here to view the PDF.
Chimeric antigen receptor-modified T cells represent a new approach to immune therapy in the treatment of hematologic malignancies. The clinical activity of chimeric antigen receptors (CARs) has been published in acute lymphoblastic leukemia (ALL)and chronic lymphocytic leukemia (CLL).1 The results have been remarkable, although only a very small number of patients have been treated. We are anticipating further clinical trials and further development of this technology for more wide spread treatment opportunities for patients. The CARs that have been the most successful clinically have a similar basic make-up. They are genetically modified T cells. The T cells are collected from the patients through leukapheresis, then they are genetically
modified to express an extracellular recognition domain that is connected in the intracellular signaling domains of the T cells. Various extracellular recognition domains have been engineered, but the target of CD19 has proven most successful in patients with B cell malignancies, and CD19 is widely expressed on CLL and B-cell ALL. The cells are infused back into the patient, sometimes after undergoing chemotherapy to lymphodeplete the patient (which may improve the recovery and persistence of the cells after treatment). The infusion responses have been
dramatic in some patients, with severe cytokine storm described in reports, usually several days after treatment.2 This is thought to reflect the very rapid identification of the target protein and response of the T cells to the target. Those patients with acute leukemia who have responded also appear to respond rapidly, with disappearance of blasts from the peripheral blood within a month. The cells have been detectable in some patients for months after treatment.
Please click here to view the PDF.
Chimeric antigen receptor-modified T cells represent a new approach to immune therapy in the treatment of hematologic malignancies. The clinical activity of chimeric antigen receptors (CARs) has been published in acute lymphoblastic leukemia (ALL)and chronic lymphocytic leukemia (CLL).1 The results have been remarkable, although only a very small number of patients have been treated. We are anticipating further clinical trials and further development of this technology for more wide spread treatment opportunities for patients. The CARs that have been the most successful clinically have a similar basic make-up. They are genetically modified T cells. The T cells are collected from the patients through leukapheresis, then they are genetically
modified to express an extracellular recognition domain that is connected in the intracellular signaling domains of the T cells. Various extracellular recognition domains have been engineered, but the target of CD19 has proven most successful in patients with B cell malignancies, and CD19 is widely expressed on CLL and B-cell ALL. The cells are infused back into the patient, sometimes after undergoing chemotherapy to lymphodeplete the patient (which may improve the recovery and persistence of the cells after treatment). The infusion responses have been
dramatic in some patients, with severe cytokine storm described in reports, usually several days after treatment.2 This is thought to reflect the very rapid identification of the target protein and response of the T cells to the target. Those patients with acute leukemia who have responded also appear to respond rapidly, with disappearance of blasts from the peripheral blood within a month. The cells have been detectable in some patients for months after treatment.
Please click here to view the PDF.
Information Exchange Among Hospitals, Healthcare Providers Spikes
A new report that shows double-digit gains in hospitals’ electronic health information exchanges with other providers is a boon to healthcare, says one of SHM’s leading health information technology experts.
Published last month at HealthAffairs.org, “Hospital Electronic Health Information Exchange Grew Substantially in 2008-2012,” found that nearly 6 in 10 hospitals actively exchanged electronic health information with providers and hospitals outside of their own organization in 2012, a 41% jump since 2008.
Kendall Rogers, MD, FACP, SFHM, chief of the division of hospital medicine at the University of New Mexico Health Sciences Center in Albuquerque, says in an email to The Hospitalist that the growth is a good thing.
“Obviously, flow of information is never a bad thing for hospital medicine,” writes Dr. Rogers, chair of SHM’s Information Technology Executive Committee. “I think we have made more progress getting information back out to providers in the community, [and] helping with a safer transition (though we still have a long way to go), but we still lack significantly [in] getting info from providers or other hospitals on admission.”
The report notes that while more information has flowed among hospitals and providers, exchanges of clinical-care summaries and medication lists remain limited. The authors suggest that “new and ongoing policy initiatives and payment reforms may accelerate” the process.
Dr. Rogers adds that making systems more user-friendly may also encourage meaningful participation. “We have a health information exchange here in New Mexico that includes most hospitals”; however, he writes, “it is cumbersome and not routinely used, but definitely a step in the right direction.”
Visit our website for more information on health information technology.
A new report that shows double-digit gains in hospitals’ electronic health information exchanges with other providers is a boon to healthcare, says one of SHM’s leading health information technology experts.
Published last month at HealthAffairs.org, “Hospital Electronic Health Information Exchange Grew Substantially in 2008-2012,” found that nearly 6 in 10 hospitals actively exchanged electronic health information with providers and hospitals outside of their own organization in 2012, a 41% jump since 2008.
Kendall Rogers, MD, FACP, SFHM, chief of the division of hospital medicine at the University of New Mexico Health Sciences Center in Albuquerque, says in an email to The Hospitalist that the growth is a good thing.
“Obviously, flow of information is never a bad thing for hospital medicine,” writes Dr. Rogers, chair of SHM’s Information Technology Executive Committee. “I think we have made more progress getting information back out to providers in the community, [and] helping with a safer transition (though we still have a long way to go), but we still lack significantly [in] getting info from providers or other hospitals on admission.”
The report notes that while more information has flowed among hospitals and providers, exchanges of clinical-care summaries and medication lists remain limited. The authors suggest that “new and ongoing policy initiatives and payment reforms may accelerate” the process.
Dr. Rogers adds that making systems more user-friendly may also encourage meaningful participation. “We have a health information exchange here in New Mexico that includes most hospitals”; however, he writes, “it is cumbersome and not routinely used, but definitely a step in the right direction.”
Visit our website for more information on health information technology.
A new report that shows double-digit gains in hospitals’ electronic health information exchanges with other providers is a boon to healthcare, says one of SHM’s leading health information technology experts.
Published last month at HealthAffairs.org, “Hospital Electronic Health Information Exchange Grew Substantially in 2008-2012,” found that nearly 6 in 10 hospitals actively exchanged electronic health information with providers and hospitals outside of their own organization in 2012, a 41% jump since 2008.
Kendall Rogers, MD, FACP, SFHM, chief of the division of hospital medicine at the University of New Mexico Health Sciences Center in Albuquerque, says in an email to The Hospitalist that the growth is a good thing.
“Obviously, flow of information is never a bad thing for hospital medicine,” writes Dr. Rogers, chair of SHM’s Information Technology Executive Committee. “I think we have made more progress getting information back out to providers in the community, [and] helping with a safer transition (though we still have a long way to go), but we still lack significantly [in] getting info from providers or other hospitals on admission.”
The report notes that while more information has flowed among hospitals and providers, exchanges of clinical-care summaries and medication lists remain limited. The authors suggest that “new and ongoing policy initiatives and payment reforms may accelerate” the process.
Dr. Rogers adds that making systems more user-friendly may also encourage meaningful participation. “We have a health information exchange here in New Mexico that includes most hospitals”; however, he writes, “it is cumbersome and not routinely used, but definitely a step in the right direction.”
Visit our website for more information on health information technology.
Healthcare Cost Containment Not High Priority for Most Physicians
When it comes to controlling healthcare costs, only 36% of physicians agree that practicing physicians have a “major responsibility” to participate in cost containment, according to a recently published Journal of the American Medical Association study, "Views of U.S. Physicians About Controlling Health Care Costs.”
More than half of the 2,556 physicians who responded to a survey said trial lawyers, health insurance companies, hospitals and health systems, pharmaceutical and device manufacturers, and patients have a major responsibility for controlling healthcare costs.
In an accompanying editorial, Ezekiel Emanuel, MD, PhD, and Andrew Steinmetz, BA, of the department of medical ethics and health policy at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, labeled the responses as “somewhat discouraging” and “a denial of responsibility” by physicians about their role in bringing costs under control.
Christopher Moriates, MD, a hospitalist at the University of California at San Francisco (UCSF) who developed a cost-awareness curriculum for physicians and serves as co-chair of UCSF’s High Value Care Committee, calls the survey a snapshot of changing attitudes in medicine because it does not include medical students or residents who, he says, are more engaged in fighting wasteful spending.
“Younger physicians are growing up in a medical world that has stressed systems-thinking and teamwork,” Dr. Moriates says. “They are ready to take that major responsibility for our healthcare system. We just need to make sure that we are teaching them how.”
Visit our website for more information on controlling healthcare costs.
When it comes to controlling healthcare costs, only 36% of physicians agree that practicing physicians have a “major responsibility” to participate in cost containment, according to a recently published Journal of the American Medical Association study, "Views of U.S. Physicians About Controlling Health Care Costs.”
More than half of the 2,556 physicians who responded to a survey said trial lawyers, health insurance companies, hospitals and health systems, pharmaceutical and device manufacturers, and patients have a major responsibility for controlling healthcare costs.
In an accompanying editorial, Ezekiel Emanuel, MD, PhD, and Andrew Steinmetz, BA, of the department of medical ethics and health policy at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, labeled the responses as “somewhat discouraging” and “a denial of responsibility” by physicians about their role in bringing costs under control.
Christopher Moriates, MD, a hospitalist at the University of California at San Francisco (UCSF) who developed a cost-awareness curriculum for physicians and serves as co-chair of UCSF’s High Value Care Committee, calls the survey a snapshot of changing attitudes in medicine because it does not include medical students or residents who, he says, are more engaged in fighting wasteful spending.
“Younger physicians are growing up in a medical world that has stressed systems-thinking and teamwork,” Dr. Moriates says. “They are ready to take that major responsibility for our healthcare system. We just need to make sure that we are teaching them how.”
Visit our website for more information on controlling healthcare costs.
When it comes to controlling healthcare costs, only 36% of physicians agree that practicing physicians have a “major responsibility” to participate in cost containment, according to a recently published Journal of the American Medical Association study, "Views of U.S. Physicians About Controlling Health Care Costs.”
More than half of the 2,556 physicians who responded to a survey said trial lawyers, health insurance companies, hospitals and health systems, pharmaceutical and device manufacturers, and patients have a major responsibility for controlling healthcare costs.
In an accompanying editorial, Ezekiel Emanuel, MD, PhD, and Andrew Steinmetz, BA, of the department of medical ethics and health policy at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, labeled the responses as “somewhat discouraging” and “a denial of responsibility” by physicians about their role in bringing costs under control.
Christopher Moriates, MD, a hospitalist at the University of California at San Francisco (UCSF) who developed a cost-awareness curriculum for physicians and serves as co-chair of UCSF’s High Value Care Committee, calls the survey a snapshot of changing attitudes in medicine because it does not include medical students or residents who, he says, are more engaged in fighting wasteful spending.
“Younger physicians are growing up in a medical world that has stressed systems-thinking and teamwork,” Dr. Moriates says. “They are ready to take that major responsibility for our healthcare system. We just need to make sure that we are teaching them how.”
Visit our website for more information on controlling healthcare costs.
Hospitalists and PCPs, a potentially formidable force
We as hospitalists have been missing a huge piece of the puzzle when it comes to readmissions. With such a huge push to reduce the readmission rate at our hospitals and avoid the resultant penalties, have we been too internally focused?
In a recent article in, titled, "A primary care physician’s ideal transitions of care – where’s the evidence?" Dr. Ning Tang gives a PCP’s perspective on how outpatient providers can greatly facilitate our common goal of optimizing patients’ transition from hospital to home (J. Hosp. Med. 2013;8:472-7). After all, most of our patients do have a PCP, who has known them for a long time and who will have much more insight into their values and support systems, their idiosyncrasies, what they will and won’t follow through on, and even their pet peeves. When we who may interact with them for only a couple of hours try to use a cookie-cutter approach to care, it simply may not be received well, if at all.
Dr. Tang suggests that PCP communication begins at the point of admission. While some ERs and admissions offices have automated systems in place to contact PCPs when their patients are admitted, for most of us, this communication comes by way of a phone call or as an electronic or faxed copy of the admission note. While I do not think anyone would argue that early involvement by the PCP has a tremendous potential to improve both the patient’s transition from home into the hospital and vice versa, in real life doctors are frequently too busy and stressed to meet this basic expectation. Hopefully that will change in the future.
Some PCPs have no desire to talk with a hospitalist each time a patient is admitted because it takes them away from seeing patients in their office. Yet others would welcome the opportunity for early involvement. It is an individual preference, one we should strive to understand in order to optimize our patients’ experience – and the experience of the physician who has entrusted patients to us.
Medication reconciliation is but the tip of the iceberg of issues the PCP could assist with, and the realization that their patient may not actually be taking all the medications they prescribed (or taking medications they didn’t) can help improve the level of care patients receive once discharged.
In the midst of brutal day, we have all had medication nightmares that make us cringe, as we slowly count to three while practicing deep-breathing exercises. You know, the patient who pulls out a crumpled list of medications. Some have been crossed out and others are too illegible to read. Then, the spouse pulls out another "updated" list, and the physician and pharmacist each have their own list, and no two lists are exactly alike.
But these nightmares could soon end. I was surprised to find out that in January of this year, the Centers for Medicare and Medicaid Services introduced new codes to reimburse primary care providers for care coordination after hospital discharge. These codes, 99495 and 99496 reimburse a substantial fee, carrying weights of 3.96 and 5.81 RVUs (relative value units), respectively, a lot more than we typically make for even an extended history and physical.
So, I have to agree with Dr. Tang. We, PCPs and hospitalists alike, are missing a huge potential to optimize care transitions, decrease our readmission rate, and lower medical costs. Dialogue needs to take place between hospitalist and the PCPs they serve to bridge some of these gaps.
Dr. Hester is a hospitalist with Baltimore-Washington Medical Center who has a passion for empowering patients to partner in their health care. She is the creator of the Patient Whiz, a patient-engagement app for iOS.
We as hospitalists have been missing a huge piece of the puzzle when it comes to readmissions. With such a huge push to reduce the readmission rate at our hospitals and avoid the resultant penalties, have we been too internally focused?
In a recent article in, titled, "A primary care physician’s ideal transitions of care – where’s the evidence?" Dr. Ning Tang gives a PCP’s perspective on how outpatient providers can greatly facilitate our common goal of optimizing patients’ transition from hospital to home (J. Hosp. Med. 2013;8:472-7). After all, most of our patients do have a PCP, who has known them for a long time and who will have much more insight into their values and support systems, their idiosyncrasies, what they will and won’t follow through on, and even their pet peeves. When we who may interact with them for only a couple of hours try to use a cookie-cutter approach to care, it simply may not be received well, if at all.
Dr. Tang suggests that PCP communication begins at the point of admission. While some ERs and admissions offices have automated systems in place to contact PCPs when their patients are admitted, for most of us, this communication comes by way of a phone call or as an electronic or faxed copy of the admission note. While I do not think anyone would argue that early involvement by the PCP has a tremendous potential to improve both the patient’s transition from home into the hospital and vice versa, in real life doctors are frequently too busy and stressed to meet this basic expectation. Hopefully that will change in the future.
Some PCPs have no desire to talk with a hospitalist each time a patient is admitted because it takes them away from seeing patients in their office. Yet others would welcome the opportunity for early involvement. It is an individual preference, one we should strive to understand in order to optimize our patients’ experience – and the experience of the physician who has entrusted patients to us.
Medication reconciliation is but the tip of the iceberg of issues the PCP could assist with, and the realization that their patient may not actually be taking all the medications they prescribed (or taking medications they didn’t) can help improve the level of care patients receive once discharged.
In the midst of brutal day, we have all had medication nightmares that make us cringe, as we slowly count to three while practicing deep-breathing exercises. You know, the patient who pulls out a crumpled list of medications. Some have been crossed out and others are too illegible to read. Then, the spouse pulls out another "updated" list, and the physician and pharmacist each have their own list, and no two lists are exactly alike.
But these nightmares could soon end. I was surprised to find out that in January of this year, the Centers for Medicare and Medicaid Services introduced new codes to reimburse primary care providers for care coordination after hospital discharge. These codes, 99495 and 99496 reimburse a substantial fee, carrying weights of 3.96 and 5.81 RVUs (relative value units), respectively, a lot more than we typically make for even an extended history and physical.
So, I have to agree with Dr. Tang. We, PCPs and hospitalists alike, are missing a huge potential to optimize care transitions, decrease our readmission rate, and lower medical costs. Dialogue needs to take place between hospitalist and the PCPs they serve to bridge some of these gaps.
Dr. Hester is a hospitalist with Baltimore-Washington Medical Center who has a passion for empowering patients to partner in their health care. She is the creator of the Patient Whiz, a patient-engagement app for iOS.
We as hospitalists have been missing a huge piece of the puzzle when it comes to readmissions. With such a huge push to reduce the readmission rate at our hospitals and avoid the resultant penalties, have we been too internally focused?
In a recent article in, titled, "A primary care physician’s ideal transitions of care – where’s the evidence?" Dr. Ning Tang gives a PCP’s perspective on how outpatient providers can greatly facilitate our common goal of optimizing patients’ transition from hospital to home (J. Hosp. Med. 2013;8:472-7). After all, most of our patients do have a PCP, who has known them for a long time and who will have much more insight into their values and support systems, their idiosyncrasies, what they will and won’t follow through on, and even their pet peeves. When we who may interact with them for only a couple of hours try to use a cookie-cutter approach to care, it simply may not be received well, if at all.
Dr. Tang suggests that PCP communication begins at the point of admission. While some ERs and admissions offices have automated systems in place to contact PCPs when their patients are admitted, for most of us, this communication comes by way of a phone call or as an electronic or faxed copy of the admission note. While I do not think anyone would argue that early involvement by the PCP has a tremendous potential to improve both the patient’s transition from home into the hospital and vice versa, in real life doctors are frequently too busy and stressed to meet this basic expectation. Hopefully that will change in the future.
Some PCPs have no desire to talk with a hospitalist each time a patient is admitted because it takes them away from seeing patients in their office. Yet others would welcome the opportunity for early involvement. It is an individual preference, one we should strive to understand in order to optimize our patients’ experience – and the experience of the physician who has entrusted patients to us.
Medication reconciliation is but the tip of the iceberg of issues the PCP could assist with, and the realization that their patient may not actually be taking all the medications they prescribed (or taking medications they didn’t) can help improve the level of care patients receive once discharged.
In the midst of brutal day, we have all had medication nightmares that make us cringe, as we slowly count to three while practicing deep-breathing exercises. You know, the patient who pulls out a crumpled list of medications. Some have been crossed out and others are too illegible to read. Then, the spouse pulls out another "updated" list, and the physician and pharmacist each have their own list, and no two lists are exactly alike.
But these nightmares could soon end. I was surprised to find out that in January of this year, the Centers for Medicare and Medicaid Services introduced new codes to reimburse primary care providers for care coordination after hospital discharge. These codes, 99495 and 99496 reimburse a substantial fee, carrying weights of 3.96 and 5.81 RVUs (relative value units), respectively, a lot more than we typically make for even an extended history and physical.
So, I have to agree with Dr. Tang. We, PCPs and hospitalists alike, are missing a huge potential to optimize care transitions, decrease our readmission rate, and lower medical costs. Dialogue needs to take place between hospitalist and the PCPs they serve to bridge some of these gaps.
Dr. Hester is a hospitalist with Baltimore-Washington Medical Center who has a passion for empowering patients to partner in their health care. She is the creator of the Patient Whiz, a patient-engagement app for iOS.
Ethnic Differences in Hospice Enrollment
Studies have documented the persisting lower rates of hospice enrollment among ethnic minority groups.[1, 2] Given the positive outcomes related to hospice enrollment,[3] investigating interventions that may reduce these disparities is critical.
Inpatient palliative care (IPC) programs were developed to improve pain and symptom management, provide patients with holistic and comprehensive prognosis and treatment options, and help patient and families clarify goals of care.[4] Although significant evidence of IPC program effectiveness in improving patient outcomes exists,[5] studies have not examined the ability of IPC programs to diminish ethnic disparities in access to hospice. We conducted a retrospective cohort study to determine if ethnic differences in hospice enrollment are experienced among patients following receipt of IPC consultation.
METHODS
A retrospective study was conducted in a nonprofit health maintenance organization medical center. The sample included seriously ill patients aged 65 years and over who received an IPC consultation and survived to hospital discharge. Data were collected from IPC databases, IPC consultation checklist (which included recording of code status discussion), and electronic medical records. The IPC team recorded discharge disposition including discharge to hospice care, home‐based palliative care (a standard program similar to hospice but offered for patients with an estimated prognosis of 1 year or less and without the caveat of foregoing curative care),[6] home with home healthcare, nursing facility, and home with standard outpatient care. Ethnicity was collected via patient report.
2 and t tests were conducted to compare those admitted to hospice with those who were not. We used logistic regression to determine the effects of ethnicity on enrollment in hospice, adjusting for demographics and clinical factors. We conducted analysis using IBM SPSS 19 (IBM, Armonk, NY).
FINDINGS
From 2007 to 2009, 408 patients received IPC consults and were subsequently discharged from the hospital. Forty‐four had missing data on ethnicity or discharge disposition, leaving 364 in the analytic sample. The mean age was 80.1 years (standard deviation [SD]=8.2), and 48.9% were female. The sample was diverse; 42.6% were white, 25.5% Latino, 23.1% black, and 8.8% of other ethnic background. Primary diagnosis included cancer (33.8%), congestive heart failure (CHF) (17.4%), coronary artery disease (12.6%), dementia (12.4%), chronic obstructive pulmonary disease (6%), cerebral vascular accident (CVA) (5.2%), and other conditions (13.6%). More than half (57.7%) were discharged to hospice, 15.4% to home‐based palliative care,[6] 14.6% to a nursing facility, 8.2% to home with usual outpatient care, and 4.1% to home with home healthcare. Code status was discussed by the IPC team among 81% of the patients, with no difference between ethnic groups.
Those discharged to hospice were older (80.8, SD=8.4 vs 79.1, SD=7.8), more likely to have cancer (71.5%) or CVA (79.5%) and less likely to have end stage renal disease (28.6%) or CHF (39%), and more likely to have had a code discussion (85.8%). There were no differences between hospice users and nonusers in gender, marital status, ethnicity, and number of chronic conditions (Table 1).
| Variable | All, N=364 | Hospice Users, n=210 | Nonhospice Users, n=154 | P Value |
|---|---|---|---|---|
| ||||
| Age, y, mean (SD) | 80.1 (8.2) | 80.8 (8.4) | 79.1 (7.8) | 0.049 |
| Gender (female), % | 48.9 | 56.2 | 43.8 | 0.568 |
| Ethnicity, % | 0.702 | |||
| White | 42.6 | 43.3 | 41.6 | |
| Latino | 25.5 | 27.1 | 23.4 | |
| African American | 23.1 | 21.4 | 25.3 | |
| Other | 8.8 | 8.1 | 9.7 | |
| Marital status, % | 0.809 | |||
| Married | 45.6 | 43.8 | 48.1 | |
| Widowed | 36.0 | 38.1 | 33.1 | |
| Divorced | 7.7 | 7.6 | 7.8 | |
| Other | 7.7 | 7.6 | 7.8 | |
| Missing | 3.0 | 2.9 | 3.2 | |
| Diagnosis, % | 0.001 | |||
| Cancer | 33.8 | 42.1 | 22.9 | |
| CHF | 16.2 | 11.0 | 23.5 | |
| CAD | 12.6 | 12.4 | 13.1 | |
| Dementia | 12.4 | 12.4 | 12.4 | |
| COPD | 6.0 | 5.3 | 7.2 | |
| CVA | 5.2 | 7.2 | 2.6 | |
| Other | 13.6 | 9.6 | 18.3 | |
| Number of chronic conditions, mean (SD) | 1.0 | 1.7 (0.8) | 1.7 (0.9) | 0.949 |
| Code status discussed, % | 81.1 | 87.0 | 72.8 | 0.001 |
Significant differences between hospice users and nonusers were controlled in a regression adjusting for age, gender, marital status, and number of chronic conditions. Compared to whites, no significant differences in hospice use were found for blacks (odds ratio [OR]: 0.67; 95% confidence interval [CI]: 0.37‐1.21), Latinos (OR: 1.24; 95% CI: 0.68‐2.25), or other ethnic groups (OR: 0.78; 95% CI: 0.34‐1.56). Compared with other diagnoses, those with cancer (OR: 3.66; 95% CI: 1.77‐7.59) and older patients (OR: 1.05; 95% CI: 1.01‐1.08) were significantly more likely to receive hospice care following IPC consult. Those discussing code status were twice as likely to be discharged to hospice (OR: 2.14; 95% CI: 1.20‐3.79).
DISCUSSION
This study found similar rates of hospice enrollment following IPC consult among Latinos, blacks, and other ethnic groups as compared with whites. Others found comparable rates of advance directive completion between whites and African Americans following IPC consultation,[7]and that IPC intensity resulting in a plan of care was highly associated with receipt of hospice care.[8] Likewise, our study found that discussion of code status, another marker of intensity, was positively associated with hospice use.
Our findings among patients receiving IPC consultation contrast with previous studies examining ethnic variation in hospice use among general samples of decedents. A study of California dual eligibles found that blacks were 26% and Asians 34% less likely than whites to use hospice. Others have found similar results among patients with CHF and lung cancer.[9, 10]
Misconceptions and lack of awareness, knowledge, and trust in healthcare providers serve as barriers to hospice care for minorities.[11, 12] IPC consultations may overcome these barriers by discussing goals of care including discussing the condition, eliciting patient/family understanding of the condition, and presenting options for code status.
This study employed a single‐cohort design without a comparison group. It was conducted within a health maintenance organization with strong hospice and palliative care programs and may not represent other settings. Nevertheless, this study provides promise for IPC consultation to increase equitable access to hospice care among minority groups. Further studies are needed to confirm the preliminary findings reported here.
Disclosures: Supported in part by a career development award from the National Palliative Care Research Center and by a grant from the Archstone Foundation. Evie Vesper and Dr. Rebecca Goldstein were employees of the healthcare organization at the time of the study. Susan Enguidanos received compensation for project evaluation during the original study. The sponsors had no role in the design, implementation, or analysis of the study. The authors report no conflicts of interest.
- , , . Ethnic variation in site of death among Medicaid/Medicare dually eligible older adults. J Am Geriatr Soc. 2005;53(8):1411–1416.
- . Racial/ethnic disparities in hospice care: a systematic review. J Palliat Med. 2008;11(5):763–768.
- . The Medicare hospice benefit: 15 years of success. J Palliat Med. 1998;1(2):139–146.
- . Palliative care in hospitals. J Hosp Med. 2006;1(1):21–28.
- , , , et al. Impact of an inpatient palliative care team: a randomized control trial. J Palliat Med. 2008;11(2):180–190.
- , , , et al. Increased satisfaction with care and lower costs: results of a randomized trial of in‐home palliative care. J Am Geriatr Soc. 2007;55(7):993–1000.
- , , , et al. Ethnicity, race, and advance directives in an inpatient palliative care consultation service. Palliat Support Care. 2012;6(1):1–7.
- , , . Hospice referrals and code status: outcomes of inpatient palliative care consultations among Asian Americans and Pacific Islanders with cancer. J Pain Symptom Manage. 2011;42(4):557–564.
- , , , , . Racial differences in hospice use and patterns of care after enrollment in hospice among Medicare beneficiaries with heart failure. Am Heart J. 2012;163(6):987–993.
- , , , et al. Racial disparities in length of stay in hospice care by tumor stage in a large elderly cohort with non‐small cell lung cancer. Palliat Med. 2012;26(1):61–71.
- , , , , . Knowledge, attitudes and beliefs about end‐of‐life care among inner‐city African Americans and Latino/Hispanic Americans. J Palliat Med. 2004;7(2):247–256.
- , , . Does caregiver knowledge matter for hospice enrollment and beyond? Pilot study of minority hospice patients. Am J Hospice Palliat Med. 2009;26(3):165–171.
Studies have documented the persisting lower rates of hospice enrollment among ethnic minority groups.[1, 2] Given the positive outcomes related to hospice enrollment,[3] investigating interventions that may reduce these disparities is critical.
Inpatient palliative care (IPC) programs were developed to improve pain and symptom management, provide patients with holistic and comprehensive prognosis and treatment options, and help patient and families clarify goals of care.[4] Although significant evidence of IPC program effectiveness in improving patient outcomes exists,[5] studies have not examined the ability of IPC programs to diminish ethnic disparities in access to hospice. We conducted a retrospective cohort study to determine if ethnic differences in hospice enrollment are experienced among patients following receipt of IPC consultation.
METHODS
A retrospective study was conducted in a nonprofit health maintenance organization medical center. The sample included seriously ill patients aged 65 years and over who received an IPC consultation and survived to hospital discharge. Data were collected from IPC databases, IPC consultation checklist (which included recording of code status discussion), and electronic medical records. The IPC team recorded discharge disposition including discharge to hospice care, home‐based palliative care (a standard program similar to hospice but offered for patients with an estimated prognosis of 1 year or less and without the caveat of foregoing curative care),[6] home with home healthcare, nursing facility, and home with standard outpatient care. Ethnicity was collected via patient report.
2 and t tests were conducted to compare those admitted to hospice with those who were not. We used logistic regression to determine the effects of ethnicity on enrollment in hospice, adjusting for demographics and clinical factors. We conducted analysis using IBM SPSS 19 (IBM, Armonk, NY).
FINDINGS
From 2007 to 2009, 408 patients received IPC consults and were subsequently discharged from the hospital. Forty‐four had missing data on ethnicity or discharge disposition, leaving 364 in the analytic sample. The mean age was 80.1 years (standard deviation [SD]=8.2), and 48.9% were female. The sample was diverse; 42.6% were white, 25.5% Latino, 23.1% black, and 8.8% of other ethnic background. Primary diagnosis included cancer (33.8%), congestive heart failure (CHF) (17.4%), coronary artery disease (12.6%), dementia (12.4%), chronic obstructive pulmonary disease (6%), cerebral vascular accident (CVA) (5.2%), and other conditions (13.6%). More than half (57.7%) were discharged to hospice, 15.4% to home‐based palliative care,[6] 14.6% to a nursing facility, 8.2% to home with usual outpatient care, and 4.1% to home with home healthcare. Code status was discussed by the IPC team among 81% of the patients, with no difference between ethnic groups.
Those discharged to hospice were older (80.8, SD=8.4 vs 79.1, SD=7.8), more likely to have cancer (71.5%) or CVA (79.5%) and less likely to have end stage renal disease (28.6%) or CHF (39%), and more likely to have had a code discussion (85.8%). There were no differences between hospice users and nonusers in gender, marital status, ethnicity, and number of chronic conditions (Table 1).
| Variable | All, N=364 | Hospice Users, n=210 | Nonhospice Users, n=154 | P Value |
|---|---|---|---|---|
| ||||
| Age, y, mean (SD) | 80.1 (8.2) | 80.8 (8.4) | 79.1 (7.8) | 0.049 |
| Gender (female), % | 48.9 | 56.2 | 43.8 | 0.568 |
| Ethnicity, % | 0.702 | |||
| White | 42.6 | 43.3 | 41.6 | |
| Latino | 25.5 | 27.1 | 23.4 | |
| African American | 23.1 | 21.4 | 25.3 | |
| Other | 8.8 | 8.1 | 9.7 | |
| Marital status, % | 0.809 | |||
| Married | 45.6 | 43.8 | 48.1 | |
| Widowed | 36.0 | 38.1 | 33.1 | |
| Divorced | 7.7 | 7.6 | 7.8 | |
| Other | 7.7 | 7.6 | 7.8 | |
| Missing | 3.0 | 2.9 | 3.2 | |
| Diagnosis, % | 0.001 | |||
| Cancer | 33.8 | 42.1 | 22.9 | |
| CHF | 16.2 | 11.0 | 23.5 | |
| CAD | 12.6 | 12.4 | 13.1 | |
| Dementia | 12.4 | 12.4 | 12.4 | |
| COPD | 6.0 | 5.3 | 7.2 | |
| CVA | 5.2 | 7.2 | 2.6 | |
| Other | 13.6 | 9.6 | 18.3 | |
| Number of chronic conditions, mean (SD) | 1.0 | 1.7 (0.8) | 1.7 (0.9) | 0.949 |
| Code status discussed, % | 81.1 | 87.0 | 72.8 | 0.001 |
Significant differences between hospice users and nonusers were controlled in a regression adjusting for age, gender, marital status, and number of chronic conditions. Compared to whites, no significant differences in hospice use were found for blacks (odds ratio [OR]: 0.67; 95% confidence interval [CI]: 0.37‐1.21), Latinos (OR: 1.24; 95% CI: 0.68‐2.25), or other ethnic groups (OR: 0.78; 95% CI: 0.34‐1.56). Compared with other diagnoses, those with cancer (OR: 3.66; 95% CI: 1.77‐7.59) and older patients (OR: 1.05; 95% CI: 1.01‐1.08) were significantly more likely to receive hospice care following IPC consult. Those discussing code status were twice as likely to be discharged to hospice (OR: 2.14; 95% CI: 1.20‐3.79).
DISCUSSION
This study found similar rates of hospice enrollment following IPC consult among Latinos, blacks, and other ethnic groups as compared with whites. Others found comparable rates of advance directive completion between whites and African Americans following IPC consultation,[7]and that IPC intensity resulting in a plan of care was highly associated with receipt of hospice care.[8] Likewise, our study found that discussion of code status, another marker of intensity, was positively associated with hospice use.
Our findings among patients receiving IPC consultation contrast with previous studies examining ethnic variation in hospice use among general samples of decedents. A study of California dual eligibles found that blacks were 26% and Asians 34% less likely than whites to use hospice. Others have found similar results among patients with CHF and lung cancer.[9, 10]
Misconceptions and lack of awareness, knowledge, and trust in healthcare providers serve as barriers to hospice care for minorities.[11, 12] IPC consultations may overcome these barriers by discussing goals of care including discussing the condition, eliciting patient/family understanding of the condition, and presenting options for code status.
This study employed a single‐cohort design without a comparison group. It was conducted within a health maintenance organization with strong hospice and palliative care programs and may not represent other settings. Nevertheless, this study provides promise for IPC consultation to increase equitable access to hospice care among minority groups. Further studies are needed to confirm the preliminary findings reported here.
Disclosures: Supported in part by a career development award from the National Palliative Care Research Center and by a grant from the Archstone Foundation. Evie Vesper and Dr. Rebecca Goldstein were employees of the healthcare organization at the time of the study. Susan Enguidanos received compensation for project evaluation during the original study. The sponsors had no role in the design, implementation, or analysis of the study. The authors report no conflicts of interest.
Studies have documented the persisting lower rates of hospice enrollment among ethnic minority groups.[1, 2] Given the positive outcomes related to hospice enrollment,[3] investigating interventions that may reduce these disparities is critical.
Inpatient palliative care (IPC) programs were developed to improve pain and symptom management, provide patients with holistic and comprehensive prognosis and treatment options, and help patient and families clarify goals of care.[4] Although significant evidence of IPC program effectiveness in improving patient outcomes exists,[5] studies have not examined the ability of IPC programs to diminish ethnic disparities in access to hospice. We conducted a retrospective cohort study to determine if ethnic differences in hospice enrollment are experienced among patients following receipt of IPC consultation.
METHODS
A retrospective study was conducted in a nonprofit health maintenance organization medical center. The sample included seriously ill patients aged 65 years and over who received an IPC consultation and survived to hospital discharge. Data were collected from IPC databases, IPC consultation checklist (which included recording of code status discussion), and electronic medical records. The IPC team recorded discharge disposition including discharge to hospice care, home‐based palliative care (a standard program similar to hospice but offered for patients with an estimated prognosis of 1 year or less and without the caveat of foregoing curative care),[6] home with home healthcare, nursing facility, and home with standard outpatient care. Ethnicity was collected via patient report.
2 and t tests were conducted to compare those admitted to hospice with those who were not. We used logistic regression to determine the effects of ethnicity on enrollment in hospice, adjusting for demographics and clinical factors. We conducted analysis using IBM SPSS 19 (IBM, Armonk, NY).
FINDINGS
From 2007 to 2009, 408 patients received IPC consults and were subsequently discharged from the hospital. Forty‐four had missing data on ethnicity or discharge disposition, leaving 364 in the analytic sample. The mean age was 80.1 years (standard deviation [SD]=8.2), and 48.9% were female. The sample was diverse; 42.6% were white, 25.5% Latino, 23.1% black, and 8.8% of other ethnic background. Primary diagnosis included cancer (33.8%), congestive heart failure (CHF) (17.4%), coronary artery disease (12.6%), dementia (12.4%), chronic obstructive pulmonary disease (6%), cerebral vascular accident (CVA) (5.2%), and other conditions (13.6%). More than half (57.7%) were discharged to hospice, 15.4% to home‐based palliative care,[6] 14.6% to a nursing facility, 8.2% to home with usual outpatient care, and 4.1% to home with home healthcare. Code status was discussed by the IPC team among 81% of the patients, with no difference between ethnic groups.
Those discharged to hospice were older (80.8, SD=8.4 vs 79.1, SD=7.8), more likely to have cancer (71.5%) or CVA (79.5%) and less likely to have end stage renal disease (28.6%) or CHF (39%), and more likely to have had a code discussion (85.8%). There were no differences between hospice users and nonusers in gender, marital status, ethnicity, and number of chronic conditions (Table 1).
| Variable | All, N=364 | Hospice Users, n=210 | Nonhospice Users, n=154 | P Value |
|---|---|---|---|---|
| ||||
| Age, y, mean (SD) | 80.1 (8.2) | 80.8 (8.4) | 79.1 (7.8) | 0.049 |
| Gender (female), % | 48.9 | 56.2 | 43.8 | 0.568 |
| Ethnicity, % | 0.702 | |||
| White | 42.6 | 43.3 | 41.6 | |
| Latino | 25.5 | 27.1 | 23.4 | |
| African American | 23.1 | 21.4 | 25.3 | |
| Other | 8.8 | 8.1 | 9.7 | |
| Marital status, % | 0.809 | |||
| Married | 45.6 | 43.8 | 48.1 | |
| Widowed | 36.0 | 38.1 | 33.1 | |
| Divorced | 7.7 | 7.6 | 7.8 | |
| Other | 7.7 | 7.6 | 7.8 | |
| Missing | 3.0 | 2.9 | 3.2 | |
| Diagnosis, % | 0.001 | |||
| Cancer | 33.8 | 42.1 | 22.9 | |
| CHF | 16.2 | 11.0 | 23.5 | |
| CAD | 12.6 | 12.4 | 13.1 | |
| Dementia | 12.4 | 12.4 | 12.4 | |
| COPD | 6.0 | 5.3 | 7.2 | |
| CVA | 5.2 | 7.2 | 2.6 | |
| Other | 13.6 | 9.6 | 18.3 | |
| Number of chronic conditions, mean (SD) | 1.0 | 1.7 (0.8) | 1.7 (0.9) | 0.949 |
| Code status discussed, % | 81.1 | 87.0 | 72.8 | 0.001 |
Significant differences between hospice users and nonusers were controlled in a regression adjusting for age, gender, marital status, and number of chronic conditions. Compared to whites, no significant differences in hospice use were found for blacks (odds ratio [OR]: 0.67; 95% confidence interval [CI]: 0.37‐1.21), Latinos (OR: 1.24; 95% CI: 0.68‐2.25), or other ethnic groups (OR: 0.78; 95% CI: 0.34‐1.56). Compared with other diagnoses, those with cancer (OR: 3.66; 95% CI: 1.77‐7.59) and older patients (OR: 1.05; 95% CI: 1.01‐1.08) were significantly more likely to receive hospice care following IPC consult. Those discussing code status were twice as likely to be discharged to hospice (OR: 2.14; 95% CI: 1.20‐3.79).
DISCUSSION
This study found similar rates of hospice enrollment following IPC consult among Latinos, blacks, and other ethnic groups as compared with whites. Others found comparable rates of advance directive completion between whites and African Americans following IPC consultation,[7]and that IPC intensity resulting in a plan of care was highly associated with receipt of hospice care.[8] Likewise, our study found that discussion of code status, another marker of intensity, was positively associated with hospice use.
Our findings among patients receiving IPC consultation contrast with previous studies examining ethnic variation in hospice use among general samples of decedents. A study of California dual eligibles found that blacks were 26% and Asians 34% less likely than whites to use hospice. Others have found similar results among patients with CHF and lung cancer.[9, 10]
Misconceptions and lack of awareness, knowledge, and trust in healthcare providers serve as barriers to hospice care for minorities.[11, 12] IPC consultations may overcome these barriers by discussing goals of care including discussing the condition, eliciting patient/family understanding of the condition, and presenting options for code status.
This study employed a single‐cohort design without a comparison group. It was conducted within a health maintenance organization with strong hospice and palliative care programs and may not represent other settings. Nevertheless, this study provides promise for IPC consultation to increase equitable access to hospice care among minority groups. Further studies are needed to confirm the preliminary findings reported here.
Disclosures: Supported in part by a career development award from the National Palliative Care Research Center and by a grant from the Archstone Foundation. Evie Vesper and Dr. Rebecca Goldstein were employees of the healthcare organization at the time of the study. Susan Enguidanos received compensation for project evaluation during the original study. The sponsors had no role in the design, implementation, or analysis of the study. The authors report no conflicts of interest.
- , , . Ethnic variation in site of death among Medicaid/Medicare dually eligible older adults. J Am Geriatr Soc. 2005;53(8):1411–1416.
- . Racial/ethnic disparities in hospice care: a systematic review. J Palliat Med. 2008;11(5):763–768.
- . The Medicare hospice benefit: 15 years of success. J Palliat Med. 1998;1(2):139–146.
- . Palliative care in hospitals. J Hosp Med. 2006;1(1):21–28.
- , , , et al. Impact of an inpatient palliative care team: a randomized control trial. J Palliat Med. 2008;11(2):180–190.
- , , , et al. Increased satisfaction with care and lower costs: results of a randomized trial of in‐home palliative care. J Am Geriatr Soc. 2007;55(7):993–1000.
- , , , et al. Ethnicity, race, and advance directives in an inpatient palliative care consultation service. Palliat Support Care. 2012;6(1):1–7.
- , , . Hospice referrals and code status: outcomes of inpatient palliative care consultations among Asian Americans and Pacific Islanders with cancer. J Pain Symptom Manage. 2011;42(4):557–564.
- , , , , . Racial differences in hospice use and patterns of care after enrollment in hospice among Medicare beneficiaries with heart failure. Am Heart J. 2012;163(6):987–993.
- , , , et al. Racial disparities in length of stay in hospice care by tumor stage in a large elderly cohort with non‐small cell lung cancer. Palliat Med. 2012;26(1):61–71.
- , , , , . Knowledge, attitudes and beliefs about end‐of‐life care among inner‐city African Americans and Latino/Hispanic Americans. J Palliat Med. 2004;7(2):247–256.
- , , . Does caregiver knowledge matter for hospice enrollment and beyond? Pilot study of minority hospice patients. Am J Hospice Palliat Med. 2009;26(3):165–171.
- , , . Ethnic variation in site of death among Medicaid/Medicare dually eligible older adults. J Am Geriatr Soc. 2005;53(8):1411–1416.
- . Racial/ethnic disparities in hospice care: a systematic review. J Palliat Med. 2008;11(5):763–768.
- . The Medicare hospice benefit: 15 years of success. J Palliat Med. 1998;1(2):139–146.
- . Palliative care in hospitals. J Hosp Med. 2006;1(1):21–28.
- , , , et al. Impact of an inpatient palliative care team: a randomized control trial. J Palliat Med. 2008;11(2):180–190.
- , , , et al. Increased satisfaction with care and lower costs: results of a randomized trial of in‐home palliative care. J Am Geriatr Soc. 2007;55(7):993–1000.
- , , , et al. Ethnicity, race, and advance directives in an inpatient palliative care consultation service. Palliat Support Care. 2012;6(1):1–7.
- , , . Hospice referrals and code status: outcomes of inpatient palliative care consultations among Asian Americans and Pacific Islanders with cancer. J Pain Symptom Manage. 2011;42(4):557–564.
- , , , , . Racial differences in hospice use and patterns of care after enrollment in hospice among Medicare beneficiaries with heart failure. Am Heart J. 2012;163(6):987–993.
- , , , et al. Racial disparities in length of stay in hospice care by tumor stage in a large elderly cohort with non‐small cell lung cancer. Palliat Med. 2012;26(1):61–71.
- , , , , . Knowledge, attitudes and beliefs about end‐of‐life care among inner‐city African Americans and Latino/Hispanic Americans. J Palliat Med. 2004;7(2):247–256.
- , , . Does caregiver knowledge matter for hospice enrollment and beyond? Pilot study of minority hospice patients. Am J Hospice Palliat Med. 2009;26(3):165–171.
Drug Resistance in Pneumonia and BSI
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
© 2013 Society of Hospital Medicine
Mitral replacement may grow with infant
NEW YORK – Physicians at Boston Children’s Hospital replaced the mitral valves of eight infants with irreparable mitral valve disease with a valve that offers the opportunity of sequential expansion as the child grows, according to Dr. Sitaram M. Emani. The results were presented at the 2013 Mitral Valve Conclave earlier this year.
"The Melody valve retains its competence if you expand it before putting it in. We asked whether the valve retains the ability to maintain competence even if expansion is performed after implantation as the patient grows," said Dr. Emani, a pediatric cardiac surgeon at Boston Children’s Hospital.
According to Dr. Emani, the current options for infants with damaged mitral valves that are beyond repair are replacement with mechanical or bioprosthetic valves or the Ross mitral procedure. Perhaps the main disadvantage of these options is the lack of a prosthetic valve small enough for an infant, one that is less than 12 mm in diameter. Another problem is the possibility of stenosis developing as the child grows, since the diameters of the prosthetics are fixed. Other drawbacks are that supra-annular fixation is generally associated with poor outcomes and that annular fixation limits the ability to upsize at reoperation.
The Melody valve is an externally stented bovine jugular vein graft that was designed for transcatheter pulmonary valve replacement. In this study, the valve was inserted surgically. The valve maintains competence over a range of sizes up to 22 mm. Although this valve is not approved for use for mitral valve replacement, the hope of using such a prosthetic is that it can be enlarged in the catheterization laboratory as the child grows.
Dr. Emani did a retrospective study of his experience with the Melody valve for mitral valve replacement in eight infants less than 12 months of age. The median age at implantation was 6 months (range, 1-9 months). Four infants had an atrioventricular canal (AVC) defect and four had congenital mitral valve stenosis. Most of the children had two prior operations for mitral valve repair. The longest follow-up to date has been 2 years.
At a median follow-up of 8 months, regurgitation on the echocardiogram was considered to be mild or less in all patients. The median gradient was 3 mm Hg (range, 2-7 mm Hg) on the immediate postoperative echocardiogram. Three patients developed a mild paravalvular leak; one of these patients had undergone aggressive stent resection, a modification Dr. Emani does not recommend. One patient developed left ventricular outflow tract obstruction (LVOTO), which Dr. Emani attributed to the lack of distal stent fixation in this patient. Another patient with an AVC defect developed complete heart block.
One patient who died 3 days postoperatively had heterotaxy, severe mitral regurgitation, and prior ventricular failure on extracorporeal membrane oxygenation support. That patient had undergone valve implantation as a last resort.
Three patients underwent sequential expansion about 6 months after implantation. After valve expansion, the median balloon size was 12 mm, ranging from 12 to 16 mm. None of the patients developed worsening valvular function and all had relief of obstruction. Transcatheter intervention was used to correct a paravalvular leak in one patient and to treat a left ventricular outflow tract problem in another. None of the patients developed endocarditis or a strut fracture, "although I worry about strut fracture if aggressive stent resection and manipulation is performed," he said at the meeting, which was sponsored by the AATS.
Dr. Emani offered some procedural tips. First, the Melody valve must be optimized for surgical implantation in infants. The length of the valve must be reduced by trimming it to reduce the chance of LVOTO or pulmonary vein obstruction. He recommends sizing the valves by echocardiogram and fixating the distal stent to the inferior free wall of the ventricle.
He reported that friction of the stent against the annulus prevents leakage. Early on he used a pericardial cuff to anchor to the annulus, particularly in patients who had undergone failed AVC repair. He tries to preserve at least part of the anterior leaflet to facilitate suture placement and create a "stand-off" from the LVOTO.
Dr. Emani also advised limiting intraoperative dilation to no more than 1 mm greater than the measured annulus. "Try not to overdilate at implantation to avoid heart block, LVOTO, and coronary compression. The nice thing is you don’t have to decide then and there what size you want. You can go back to the cath lab and, under direct visualization with the coronary view, you can dilate it under more controlled circumstances.
"The hope is that we will be able to dilate these valves as the patients grow into adolescence. If we can dilate them up to 22 mm, hopefully we will decrease the number of repeat replacements, delay the time to reoperation, and perhaps modify our thresholds for tolerating significant disease after unsuccessful repairs."
Dr. Emani reported no disclosures.
NEW YORK – Physicians at Boston Children’s Hospital replaced the mitral valves of eight infants with irreparable mitral valve disease with a valve that offers the opportunity of sequential expansion as the child grows, according to Dr. Sitaram M. Emani. The results were presented at the 2013 Mitral Valve Conclave earlier this year.
"The Melody valve retains its competence if you expand it before putting it in. We asked whether the valve retains the ability to maintain competence even if expansion is performed after implantation as the patient grows," said Dr. Emani, a pediatric cardiac surgeon at Boston Children’s Hospital.
According to Dr. Emani, the current options for infants with damaged mitral valves that are beyond repair are replacement with mechanical or bioprosthetic valves or the Ross mitral procedure. Perhaps the main disadvantage of these options is the lack of a prosthetic valve small enough for an infant, one that is less than 12 mm in diameter. Another problem is the possibility of stenosis developing as the child grows, since the diameters of the prosthetics are fixed. Other drawbacks are that supra-annular fixation is generally associated with poor outcomes and that annular fixation limits the ability to upsize at reoperation.
The Melody valve is an externally stented bovine jugular vein graft that was designed for transcatheter pulmonary valve replacement. In this study, the valve was inserted surgically. The valve maintains competence over a range of sizes up to 22 mm. Although this valve is not approved for use for mitral valve replacement, the hope of using such a prosthetic is that it can be enlarged in the catheterization laboratory as the child grows.
Dr. Emani did a retrospective study of his experience with the Melody valve for mitral valve replacement in eight infants less than 12 months of age. The median age at implantation was 6 months (range, 1-9 months). Four infants had an atrioventricular canal (AVC) defect and four had congenital mitral valve stenosis. Most of the children had two prior operations for mitral valve repair. The longest follow-up to date has been 2 years.
At a median follow-up of 8 months, regurgitation on the echocardiogram was considered to be mild or less in all patients. The median gradient was 3 mm Hg (range, 2-7 mm Hg) on the immediate postoperative echocardiogram. Three patients developed a mild paravalvular leak; one of these patients had undergone aggressive stent resection, a modification Dr. Emani does not recommend. One patient developed left ventricular outflow tract obstruction (LVOTO), which Dr. Emani attributed to the lack of distal stent fixation in this patient. Another patient with an AVC defect developed complete heart block.
One patient who died 3 days postoperatively had heterotaxy, severe mitral regurgitation, and prior ventricular failure on extracorporeal membrane oxygenation support. That patient had undergone valve implantation as a last resort.
Three patients underwent sequential expansion about 6 months after implantation. After valve expansion, the median balloon size was 12 mm, ranging from 12 to 16 mm. None of the patients developed worsening valvular function and all had relief of obstruction. Transcatheter intervention was used to correct a paravalvular leak in one patient and to treat a left ventricular outflow tract problem in another. None of the patients developed endocarditis or a strut fracture, "although I worry about strut fracture if aggressive stent resection and manipulation is performed," he said at the meeting, which was sponsored by the AATS.
Dr. Emani offered some procedural tips. First, the Melody valve must be optimized for surgical implantation in infants. The length of the valve must be reduced by trimming it to reduce the chance of LVOTO or pulmonary vein obstruction. He recommends sizing the valves by echocardiogram and fixating the distal stent to the inferior free wall of the ventricle.
He reported that friction of the stent against the annulus prevents leakage. Early on he used a pericardial cuff to anchor to the annulus, particularly in patients who had undergone failed AVC repair. He tries to preserve at least part of the anterior leaflet to facilitate suture placement and create a "stand-off" from the LVOTO.
Dr. Emani also advised limiting intraoperative dilation to no more than 1 mm greater than the measured annulus. "Try not to overdilate at implantation to avoid heart block, LVOTO, and coronary compression. The nice thing is you don’t have to decide then and there what size you want. You can go back to the cath lab and, under direct visualization with the coronary view, you can dilate it under more controlled circumstances.
"The hope is that we will be able to dilate these valves as the patients grow into adolescence. If we can dilate them up to 22 mm, hopefully we will decrease the number of repeat replacements, delay the time to reoperation, and perhaps modify our thresholds for tolerating significant disease after unsuccessful repairs."
Dr. Emani reported no disclosures.
NEW YORK – Physicians at Boston Children’s Hospital replaced the mitral valves of eight infants with irreparable mitral valve disease with a valve that offers the opportunity of sequential expansion as the child grows, according to Dr. Sitaram M. Emani. The results were presented at the 2013 Mitral Valve Conclave earlier this year.
"The Melody valve retains its competence if you expand it before putting it in. We asked whether the valve retains the ability to maintain competence even if expansion is performed after implantation as the patient grows," said Dr. Emani, a pediatric cardiac surgeon at Boston Children’s Hospital.
According to Dr. Emani, the current options for infants with damaged mitral valves that are beyond repair are replacement with mechanical or bioprosthetic valves or the Ross mitral procedure. Perhaps the main disadvantage of these options is the lack of a prosthetic valve small enough for an infant, one that is less than 12 mm in diameter. Another problem is the possibility of stenosis developing as the child grows, since the diameters of the prosthetics are fixed. Other drawbacks are that supra-annular fixation is generally associated with poor outcomes and that annular fixation limits the ability to upsize at reoperation.
The Melody valve is an externally stented bovine jugular vein graft that was designed for transcatheter pulmonary valve replacement. In this study, the valve was inserted surgically. The valve maintains competence over a range of sizes up to 22 mm. Although this valve is not approved for use for mitral valve replacement, the hope of using such a prosthetic is that it can be enlarged in the catheterization laboratory as the child grows.
Dr. Emani did a retrospective study of his experience with the Melody valve for mitral valve replacement in eight infants less than 12 months of age. The median age at implantation was 6 months (range, 1-9 months). Four infants had an atrioventricular canal (AVC) defect and four had congenital mitral valve stenosis. Most of the children had two prior operations for mitral valve repair. The longest follow-up to date has been 2 years.
At a median follow-up of 8 months, regurgitation on the echocardiogram was considered to be mild or less in all patients. The median gradient was 3 mm Hg (range, 2-7 mm Hg) on the immediate postoperative echocardiogram. Three patients developed a mild paravalvular leak; one of these patients had undergone aggressive stent resection, a modification Dr. Emani does not recommend. One patient developed left ventricular outflow tract obstruction (LVOTO), which Dr. Emani attributed to the lack of distal stent fixation in this patient. Another patient with an AVC defect developed complete heart block.
One patient who died 3 days postoperatively had heterotaxy, severe mitral regurgitation, and prior ventricular failure on extracorporeal membrane oxygenation support. That patient had undergone valve implantation as a last resort.
Three patients underwent sequential expansion about 6 months after implantation. After valve expansion, the median balloon size was 12 mm, ranging from 12 to 16 mm. None of the patients developed worsening valvular function and all had relief of obstruction. Transcatheter intervention was used to correct a paravalvular leak in one patient and to treat a left ventricular outflow tract problem in another. None of the patients developed endocarditis or a strut fracture, "although I worry about strut fracture if aggressive stent resection and manipulation is performed," he said at the meeting, which was sponsored by the AATS.
Dr. Emani offered some procedural tips. First, the Melody valve must be optimized for surgical implantation in infants. The length of the valve must be reduced by trimming it to reduce the chance of LVOTO or pulmonary vein obstruction. He recommends sizing the valves by echocardiogram and fixating the distal stent to the inferior free wall of the ventricle.
He reported that friction of the stent against the annulus prevents leakage. Early on he used a pericardial cuff to anchor to the annulus, particularly in patients who had undergone failed AVC repair. He tries to preserve at least part of the anterior leaflet to facilitate suture placement and create a "stand-off" from the LVOTO.
Dr. Emani also advised limiting intraoperative dilation to no more than 1 mm greater than the measured annulus. "Try not to overdilate at implantation to avoid heart block, LVOTO, and coronary compression. The nice thing is you don’t have to decide then and there what size you want. You can go back to the cath lab and, under direct visualization with the coronary view, you can dilate it under more controlled circumstances.
"The hope is that we will be able to dilate these valves as the patients grow into adolescence. If we can dilate them up to 22 mm, hopefully we will decrease the number of repeat replacements, delay the time to reoperation, and perhaps modify our thresholds for tolerating significant disease after unsuccessful repairs."
Dr. Emani reported no disclosures.
Mentored Implementation Program Highlights Need for Improved Medication Reconciliation
What is the best possible medication history? How is it done? Who should do it? When should it be done during a patient’s journey in and out of the hospital? What medication discrepancies—and potential adverse drug events—are most likely?
Those are questions veteran hospitalist Jason Stein, MD, tried to answer during an HM13 breakout session on medication reconciliation at the Gaylord National Resort and Conference Center in National Harbor, Md.
"How do you know as the discharging provider if the medication list you’re looking at is gold or garbage?" said Dr. Stein, associate director for quality improvement (QI) at Emory University in Atlanta and a mentor for SHM’s Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) quality-research initiative.
“Sometimes it’s impossible to know what the patient was or wasn’t taking, but it doesn’t mean you don’t do your best,” he said, adding that hospitalists should attempt to get at least one reliable, corroborating source of information for a patient’s medical history.
Sometimes it is necessary to speak to family members or the community pharmacy, Dr. Schnipper said, because many patients can’t remember all of the drugs they are taking. Trying to do medication reconciliation at the time of discharge when BPMH has not been done can lead to more work for the provider, medication errors, or rehospitalizations. Ideally, knowledge of what the patient was taking before admission, as well as the patient’s health literacy and adherence history, should be gathered and documented once, early, and well during the hospitalization by a trained provider, according to Dr. Schnipper.
An SHM survey, however, showed 50% to 70% percent of front-line providers have never received BPMH training, and 60% say they are not given the time.1
“Not knowing means a diligent provider would need to take a BPMH at discharge, which is a waste,” Dr. Stein said. It would be nice to tell from the electronic health record whether a true BPMH had been taken for every hospitalized patient—or at least every high-risk patient—but this goal is not well-supported by current information technology, MARQUIS investigators said they have learned.
The MARQUIS program was launched in 2011 with a grant from the federal Agency for Healthcare Research and Quality. It began with a thorough review of the literature on medication reconciliation and the development of a toolkit of best practices. In 2012, six pilot sites were offered a menu of 11 MARQUIS medication-reconciliation interventions to choose from and help in implementing them from an SHM mentor, with expertise in both QI and medication safety.
Listen to more of our interview with MARQUIS principal investigator Jeffrey Schnipper MD, MPH, FHM.
Participating sites have mobilized high-level hospital leadership and utilize a local champion, usually a hospitalist, tools for assessing high-risk patients, medication-reconciliation assistants or counselors, and pharmacist involvement. Different sites have employed different professional staff to take medication histories.
Dr. Schnipper said he expects another round of MARQUIS-mentored implementation, probably in 2014, after data from the first round have been analyzed. The program is tracking such outcomes as the number of potentially harmful, unintentional medication discrepancies per patient at participating sites.
The MARQUIS toolkit is available on the SHM website. TH
Larry Beresford is a freelance writer in San Francisco.
Reference
1. Schnipper JL, Mueller SK, Salanitro AH, Stein J. Got Med Wreck? Targeted Repairs from the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS). PowerPoint presentation at Society of Hospital Medicine annual meeting, May 16-19, 2013, National Harbor, Md.
What is the best possible medication history? How is it done? Who should do it? When should it be done during a patient’s journey in and out of the hospital? What medication discrepancies—and potential adverse drug events—are most likely?
Those are questions veteran hospitalist Jason Stein, MD, tried to answer during an HM13 breakout session on medication reconciliation at the Gaylord National Resort and Conference Center in National Harbor, Md.
"How do you know as the discharging provider if the medication list you’re looking at is gold or garbage?" said Dr. Stein, associate director for quality improvement (QI) at Emory University in Atlanta and a mentor for SHM’s Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) quality-research initiative.
“Sometimes it’s impossible to know what the patient was or wasn’t taking, but it doesn’t mean you don’t do your best,” he said, adding that hospitalists should attempt to get at least one reliable, corroborating source of information for a patient’s medical history.
Sometimes it is necessary to speak to family members or the community pharmacy, Dr. Schnipper said, because many patients can’t remember all of the drugs they are taking. Trying to do medication reconciliation at the time of discharge when BPMH has not been done can lead to more work for the provider, medication errors, or rehospitalizations. Ideally, knowledge of what the patient was taking before admission, as well as the patient’s health literacy and adherence history, should be gathered and documented once, early, and well during the hospitalization by a trained provider, according to Dr. Schnipper.
An SHM survey, however, showed 50% to 70% percent of front-line providers have never received BPMH training, and 60% say they are not given the time.1
“Not knowing means a diligent provider would need to take a BPMH at discharge, which is a waste,” Dr. Stein said. It would be nice to tell from the electronic health record whether a true BPMH had been taken for every hospitalized patient—or at least every high-risk patient—but this goal is not well-supported by current information technology, MARQUIS investigators said they have learned.
The MARQUIS program was launched in 2011 with a grant from the federal Agency for Healthcare Research and Quality. It began with a thorough review of the literature on medication reconciliation and the development of a toolkit of best practices. In 2012, six pilot sites were offered a menu of 11 MARQUIS medication-reconciliation interventions to choose from and help in implementing them from an SHM mentor, with expertise in both QI and medication safety.
Listen to more of our interview with MARQUIS principal investigator Jeffrey Schnipper MD, MPH, FHM.
Participating sites have mobilized high-level hospital leadership and utilize a local champion, usually a hospitalist, tools for assessing high-risk patients, medication-reconciliation assistants or counselors, and pharmacist involvement. Different sites have employed different professional staff to take medication histories.
Dr. Schnipper said he expects another round of MARQUIS-mentored implementation, probably in 2014, after data from the first round have been analyzed. The program is tracking such outcomes as the number of potentially harmful, unintentional medication discrepancies per patient at participating sites.
The MARQUIS toolkit is available on the SHM website. TH
Larry Beresford is a freelance writer in San Francisco.
Reference
1. Schnipper JL, Mueller SK, Salanitro AH, Stein J. Got Med Wreck? Targeted Repairs from the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS). PowerPoint presentation at Society of Hospital Medicine annual meeting, May 16-19, 2013, National Harbor, Md.
What is the best possible medication history? How is it done? Who should do it? When should it be done during a patient’s journey in and out of the hospital? What medication discrepancies—and potential adverse drug events—are most likely?
Those are questions veteran hospitalist Jason Stein, MD, tried to answer during an HM13 breakout session on medication reconciliation at the Gaylord National Resort and Conference Center in National Harbor, Md.
"How do you know as the discharging provider if the medication list you’re looking at is gold or garbage?" said Dr. Stein, associate director for quality improvement (QI) at Emory University in Atlanta and a mentor for SHM’s Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) quality-research initiative.
“Sometimes it’s impossible to know what the patient was or wasn’t taking, but it doesn’t mean you don’t do your best,” he said, adding that hospitalists should attempt to get at least one reliable, corroborating source of information for a patient’s medical history.
Sometimes it is necessary to speak to family members or the community pharmacy, Dr. Schnipper said, because many patients can’t remember all of the drugs they are taking. Trying to do medication reconciliation at the time of discharge when BPMH has not been done can lead to more work for the provider, medication errors, or rehospitalizations. Ideally, knowledge of what the patient was taking before admission, as well as the patient’s health literacy and adherence history, should be gathered and documented once, early, and well during the hospitalization by a trained provider, according to Dr. Schnipper.
An SHM survey, however, showed 50% to 70% percent of front-line providers have never received BPMH training, and 60% say they are not given the time.1
“Not knowing means a diligent provider would need to take a BPMH at discharge, which is a waste,” Dr. Stein said. It would be nice to tell from the electronic health record whether a true BPMH had been taken for every hospitalized patient—or at least every high-risk patient—but this goal is not well-supported by current information technology, MARQUIS investigators said they have learned.
The MARQUIS program was launched in 2011 with a grant from the federal Agency for Healthcare Research and Quality. It began with a thorough review of the literature on medication reconciliation and the development of a toolkit of best practices. In 2012, six pilot sites were offered a menu of 11 MARQUIS medication-reconciliation interventions to choose from and help in implementing them from an SHM mentor, with expertise in both QI and medication safety.
Listen to more of our interview with MARQUIS principal investigator Jeffrey Schnipper MD, MPH, FHM.
Participating sites have mobilized high-level hospital leadership and utilize a local champion, usually a hospitalist, tools for assessing high-risk patients, medication-reconciliation assistants or counselors, and pharmacist involvement. Different sites have employed different professional staff to take medication histories.
Dr. Schnipper said he expects another round of MARQUIS-mentored implementation, probably in 2014, after data from the first round have been analyzed. The program is tracking such outcomes as the number of potentially harmful, unintentional medication discrepancies per patient at participating sites.
The MARQUIS toolkit is available on the SHM website. TH
Larry Beresford is a freelance writer in San Francisco.
Reference
1. Schnipper JL, Mueller SK, Salanitro AH, Stein J. Got Med Wreck? Targeted Repairs from the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS). PowerPoint presentation at Society of Hospital Medicine annual meeting, May 16-19, 2013, National Harbor, Md.
Getting a handle on goals of care
She presented to the trauma bay after transfer from another hospital. She had fallen out of bed at the nursing home, and they had sent her to the emergency department for evaluation. Her head CT demonstrated a subacute chronic subdural hematoma. She had fallen a month ago and had been seen at the same hospital and was transferred to us then, too, but not as a trauma. Admitted to another service for a few days, she had subsequently been sent to the nursing home with weekly head CT scans for follow-up. Today’s CT showed continued resolution of her subdural hematoma, but since she had fallen and had an abnormal CT scan, she was transferred to us as a trauma for further evaluation.
The patient was elderly, in her 90s, with end-stage dementia. The trauma team descended on her as we do with all traumas – to evaluate for life-threatening injuries. Airway, breathing, circulation. Does she need to be intubated? What is her blood pressure? Place IVs and draw blood. Put her quickly on the monitors, undress her completely. Roll her on her side to examine her back. Make sure she is in a rigid C-collar and cannot move her neck until we are sure it isn’t fractured. She cannot sit up despite her desire to do so, thus requiring us to hold her down, so she doesn’t injure herself or others. In the midst of all this, she kept screaming, "Why do you keep doing this to me?" That was all she said. Repeatedly. As I sorted out the events of the past month, read the radiologist report from the referring institution that documented improvement in her scans, and reviewed all the CTs on disc, I wondered the same, "Why are we doing this to you?" She didn’t need a trauma center or the trauma team. What she needed was a goals of care discussion and POLST (Physician Orders for Life-Sustaining Treatment) document.
We, as doctors, are poor at discussing goals of care. Even for those patients who are expected to do well, we do not address code status, or ask them what they want if things go poorly. Recently, the University of California published their results with a quality improvement program to document advance care planning discussions. Between July 2011 and May 2012 on the medical service, they created an incentive program for documentation of goals of care and identification of a surrogate decision maker. If 75% of patients had the two items documented in the medical record, then the residents received a $400 incentive. Documentation (and likely actual discussion) increased from 22% in July to 90% by October and remained at that level. There were reminders and feedback, and it seems likely a component of peer pressure among the residents to ensure everyone received the incentive. The study did not track outcomes or documentation rates after the program was over. The study did show that behavior of initiating difficult end-of-life (EOL) planning discussions can be improved in a quality improvement program. (JAMA Intern. Med. 2013 [doi: 10.1001/jamainternmed.2013.8158]).
Ideally, the next step would be to document the use of POLST (www.polst.org) orders. POLST is a bright pink form that documents the patient’s preferences for code status, treatment options (full including ICU, limited, or comfort measures including no transport to hospital) artificial nutrition and hydration, and antibiotics. POLST is signed by a physician and, therefore, it can be applied across care settings. If it is signed by the patient, it cannot be overridden by the surrogate, and there are legal protections for health care providers.
We admitted the patient in the trauma bay, not because she needed acute care, but because she needed goals of care defined. We consulted Palliative Medicine and had the social worker identify a decision maker. Palliative Medicine worked with the surrogate decision maker to set goals of care: feeding tube, follow-up scans, code status, and most importantly POLST orders. Regrettably, it took a trip to the Trauma Bay after multiple interactions with the health care system to evaluate what really was in the best interest of the patient and what she would have wanted. She told us as best she could that she did not want what we were doing to her. This time, we listened.
Dr Toevs is a trauma critical care surgeon at Allegheny General Hospital in Pittsburgh, Pa. She has a Masters degree in bioethics and board-certification in hospice and palliative medicine.
She presented to the trauma bay after transfer from another hospital. She had fallen out of bed at the nursing home, and they had sent her to the emergency department for evaluation. Her head CT demonstrated a subacute chronic subdural hematoma. She had fallen a month ago and had been seen at the same hospital and was transferred to us then, too, but not as a trauma. Admitted to another service for a few days, she had subsequently been sent to the nursing home with weekly head CT scans for follow-up. Today’s CT showed continued resolution of her subdural hematoma, but since she had fallen and had an abnormal CT scan, she was transferred to us as a trauma for further evaluation.
The patient was elderly, in her 90s, with end-stage dementia. The trauma team descended on her as we do with all traumas – to evaluate for life-threatening injuries. Airway, breathing, circulation. Does she need to be intubated? What is her blood pressure? Place IVs and draw blood. Put her quickly on the monitors, undress her completely. Roll her on her side to examine her back. Make sure she is in a rigid C-collar and cannot move her neck until we are sure it isn’t fractured. She cannot sit up despite her desire to do so, thus requiring us to hold her down, so she doesn’t injure herself or others. In the midst of all this, she kept screaming, "Why do you keep doing this to me?" That was all she said. Repeatedly. As I sorted out the events of the past month, read the radiologist report from the referring institution that documented improvement in her scans, and reviewed all the CTs on disc, I wondered the same, "Why are we doing this to you?" She didn’t need a trauma center or the trauma team. What she needed was a goals of care discussion and POLST (Physician Orders for Life-Sustaining Treatment) document.
We, as doctors, are poor at discussing goals of care. Even for those patients who are expected to do well, we do not address code status, or ask them what they want if things go poorly. Recently, the University of California published their results with a quality improvement program to document advance care planning discussions. Between July 2011 and May 2012 on the medical service, they created an incentive program for documentation of goals of care and identification of a surrogate decision maker. If 75% of patients had the two items documented in the medical record, then the residents received a $400 incentive. Documentation (and likely actual discussion) increased from 22% in July to 90% by October and remained at that level. There were reminders and feedback, and it seems likely a component of peer pressure among the residents to ensure everyone received the incentive. The study did not track outcomes or documentation rates after the program was over. The study did show that behavior of initiating difficult end-of-life (EOL) planning discussions can be improved in a quality improvement program. (JAMA Intern. Med. 2013 [doi: 10.1001/jamainternmed.2013.8158]).
Ideally, the next step would be to document the use of POLST (www.polst.org) orders. POLST is a bright pink form that documents the patient’s preferences for code status, treatment options (full including ICU, limited, or comfort measures including no transport to hospital) artificial nutrition and hydration, and antibiotics. POLST is signed by a physician and, therefore, it can be applied across care settings. If it is signed by the patient, it cannot be overridden by the surrogate, and there are legal protections for health care providers.
We admitted the patient in the trauma bay, not because she needed acute care, but because she needed goals of care defined. We consulted Palliative Medicine and had the social worker identify a decision maker. Palliative Medicine worked with the surrogate decision maker to set goals of care: feeding tube, follow-up scans, code status, and most importantly POLST orders. Regrettably, it took a trip to the Trauma Bay after multiple interactions with the health care system to evaluate what really was in the best interest of the patient and what she would have wanted. She told us as best she could that she did not want what we were doing to her. This time, we listened.
Dr Toevs is a trauma critical care surgeon at Allegheny General Hospital in Pittsburgh, Pa. She has a Masters degree in bioethics and board-certification in hospice and palliative medicine.
She presented to the trauma bay after transfer from another hospital. She had fallen out of bed at the nursing home, and they had sent her to the emergency department for evaluation. Her head CT demonstrated a subacute chronic subdural hematoma. She had fallen a month ago and had been seen at the same hospital and was transferred to us then, too, but not as a trauma. Admitted to another service for a few days, she had subsequently been sent to the nursing home with weekly head CT scans for follow-up. Today’s CT showed continued resolution of her subdural hematoma, but since she had fallen and had an abnormal CT scan, she was transferred to us as a trauma for further evaluation.
The patient was elderly, in her 90s, with end-stage dementia. The trauma team descended on her as we do with all traumas – to evaluate for life-threatening injuries. Airway, breathing, circulation. Does she need to be intubated? What is her blood pressure? Place IVs and draw blood. Put her quickly on the monitors, undress her completely. Roll her on her side to examine her back. Make sure she is in a rigid C-collar and cannot move her neck until we are sure it isn’t fractured. She cannot sit up despite her desire to do so, thus requiring us to hold her down, so she doesn’t injure herself or others. In the midst of all this, she kept screaming, "Why do you keep doing this to me?" That was all she said. Repeatedly. As I sorted out the events of the past month, read the radiologist report from the referring institution that documented improvement in her scans, and reviewed all the CTs on disc, I wondered the same, "Why are we doing this to you?" She didn’t need a trauma center or the trauma team. What she needed was a goals of care discussion and POLST (Physician Orders for Life-Sustaining Treatment) document.
We, as doctors, are poor at discussing goals of care. Even for those patients who are expected to do well, we do not address code status, or ask them what they want if things go poorly. Recently, the University of California published their results with a quality improvement program to document advance care planning discussions. Between July 2011 and May 2012 on the medical service, they created an incentive program for documentation of goals of care and identification of a surrogate decision maker. If 75% of patients had the two items documented in the medical record, then the residents received a $400 incentive. Documentation (and likely actual discussion) increased from 22% in July to 90% by October and remained at that level. There were reminders and feedback, and it seems likely a component of peer pressure among the residents to ensure everyone received the incentive. The study did not track outcomes or documentation rates after the program was over. The study did show that behavior of initiating difficult end-of-life (EOL) planning discussions can be improved in a quality improvement program. (JAMA Intern. Med. 2013 [doi: 10.1001/jamainternmed.2013.8158]).
Ideally, the next step would be to document the use of POLST (www.polst.org) orders. POLST is a bright pink form that documents the patient’s preferences for code status, treatment options (full including ICU, limited, or comfort measures including no transport to hospital) artificial nutrition and hydration, and antibiotics. POLST is signed by a physician and, therefore, it can be applied across care settings. If it is signed by the patient, it cannot be overridden by the surrogate, and there are legal protections for health care providers.
We admitted the patient in the trauma bay, not because she needed acute care, but because she needed goals of care defined. We consulted Palliative Medicine and had the social worker identify a decision maker. Palliative Medicine worked with the surrogate decision maker to set goals of care: feeding tube, follow-up scans, code status, and most importantly POLST orders. Regrettably, it took a trip to the Trauma Bay after multiple interactions with the health care system to evaluate what really was in the best interest of the patient and what she would have wanted. She told us as best she could that she did not want what we were doing to her. This time, we listened.
Dr Toevs is a trauma critical care surgeon at Allegheny General Hospital in Pittsburgh, Pa. She has a Masters degree in bioethics and board-certification in hospice and palliative medicine.
Hospitals Strategies to Reduce Readmissions
With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]
In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.
Although previous research has examined strategies used by hospitals enrolled in H2H,10 we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.
Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.
METHODS
Study Design and Sample
We conducted a national Web‐based survey of all hospitals that had enrolled in H2H and/or STAAR from May 2009 through June 2010 (n=658 hospitals); the survey was conducted from November 1, 2010 through June 30, 2011 and completed by 599 hospitals (response rate of 91%) (see the survey tool in the Supporting Information, Appendix, in the online version of this article). To initiate contact with each hospital, we emailed the primary liaison person for the initiative at the hospital (n=594 hospitals enrolled in the H2H campaign and n=64 hospitals from Massachusetts, Michigan, and Washington enrolled in STAAR). Respondents were instructed to coordinate with other relevant staff to complete a single survey reflecting the hospital's response. Of the total 658 hospitals, 599 completed the survey, for a response rate of 91%. A total of 532 of these 599 hospitals were enrolled in H2H, 55 hospitals were enrolled in STAAR, and 12 hospitals were enrolled in both STAAR and H2H. We excluded the 12 hospitals that were enrolled in both campaigns from our analysis. All research procedures were approved by the institutional review board at the Yale School of Medicine.
Measures
We examined hospital strategies in 3 areas: quality improvement resources and performance monitoring, medication management, and discharge and follow‐up procedures. In addition, consistent with our earlier work,[10] we summarized strategies using an index of 10 specific strategies across the 3 domains. The first domain (quality improvement resources and performance monitoring) includes having a quality improvement team for reducing readmissions for heart failure, or for acute myocardial infarction, or for both; monitoring the percent of patients with follow‐up appointments within 7 days of discharge; and monitoring 30‐day readmission rates. The second domain (medication management) includes providing patient education about the purpose of each medication and any alterations to the medication list, having a pharmacist primarily responsible for conducting medication reconciliation at discharge, and having a pharmacy technician primarily responsible for obtaining medication history as part of medication reconciliation process. The third domain (discharge and follow‐up procedures) includes discharge processes in which patients or their caregivers receive an emergency plan, patients usually or always leave the hospital with an outpatient follow‐up appointment already arranged, a process is in place to ensure the outpatient physicians are alerted to the patient's discharge status within 48 hours of discharge, and patients are called after discharge to follow up on postdischarge needs or to provide additional patient education. The summary score ranged from 0 to 10, and its items are supported by a number of studies,[3, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] although definitive evidence on their effectiveness is lacking.
We also examined hospital characteristics including the number of staffed hospital beds, teaching status (hospital that is a member of Council of Teaching Hospitals [COTH], non‐COTH teaching hospital with residency approved by the Accreditation Council for Graduate Medical Education, or nonteaching hospital), multihospital affiliation (yes or no), and ownership (for profit, nonprofit, or government) using data from the Annual Survey of the American Hospital Association from 2009. We determined census regions from the US Census Bureau and urban/suburban/rural location from the 2003 Urban Influence Codes. Hospital 30‐day risk‐standardized readmission rates (RSRRs) were derived from the most recent year of data (July 2010 to June 2011) collected by the Centers for Medicare and Medicaid Services (CMS). RSRRs were calculated using the statistical model as specified by the CMS for public reporting of 30‐day RSRRs.[29, 30]
Data Analysis
We used standard frequency analysis to describe the sample of hospitals, the prevalence of each hospital strategy, and the distribution of summary variables, for both H2H and the STAAR hospitals. We examined the statistical significance of differences between the reported use of strategies to reduce readmissions in H2H versus STARR hospitals using logistic and linear regression, adjusted for hospital characteristics that differed significantly between the 2 groups in the bivariate analyses (ownership type and census region). We adjusted for hospital characteristics to isolate the independent association between the initiative (H2H or STAAR) and hospital strategies being employed. This was important given the significant differences in types of hospitals (by ownership and census region) in the H2H versus STAAR initiatives and reported variation of strategies used by hospital characteristics. Because hospitals completed the questionnaire at different times during the survey period, we adjusted for month of survey completion, but this variable was nonsignificant and therefore eliminated from the final model. We employed P<0.01 as our significance level to adjust for multiple comparisons conducted. This research was funded by the Commonwealth Fund, which had no influence on the methodology, findings, or interpretation. All analyses were conducted in SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
Characteristics of Hospital Sample
Of the 587 hospitals in our sample, 55 hospitals (9%) were enrolled in STAAR and 532 hospitals (91%) were enrolled in H2H. The roles reported by respondents varied, and many respondents reported having more than 1 role; nearly 60% were from quality management departments, 24% were from cardiology departments, 24% had other clinical roles, 17% were from case management or care coordination, and 7% reported working in nonclinical roles. Hospital characteristics are reported in Table 1.
| Characteristic | H2H, N=532 | STAAR, N=55 | 2 P Value |
|---|---|---|---|
| |||
| Teaching status, N (%) | 0.185 | ||
| COTH teaching | 70 (13.2) | 12 (22.2) | |
| Non‐COTH teaching | 105 (19.7) | 9 (16.7) | |
| Nonteaching | 357 (67.1) | 33 (61.1) | |
| Number of staffed beds, N (%) | 0.598 | ||
| <200 beds | 180 (34.2) | 22 (42.3) | |
| 200399 beds | 199 (37.8) | 19 (36.5) | |
| 400599 beds | 90 (17.1) | 6 (11.5) | |
| 600+ beds | 58 (11.0) | 5 (9.6) | |
| Mean (SD) | 315 (218) | 254 (206) | 0.056a |
| Census region, N (%) | <0.001 | ||
| New England | 21 (4.0) | 14 (26.4) | |
| Middle Atlantic | 58 (10.9) | 0 | |
| East North Central | 95 (17.9) | 27 (50.9) | |
| West North Central | 45 (8.5) | 0 | |
| South Atlantic | 122 (23.0) | 0 | |
| East South Central | 52 (9.8) | 0 | |
| West South Central | 54 (10.2) | 0 | |
| Mountain | 33 (6.2) | 0 | |
| Pacific | 50 (9.4) | 12 (22.6) | |
| Puerto Rico | 1 (0.2) | 0 | |
| Geographic location, N (%) | 0.184 | ||
| Urban | 451 (85.1) | 40 (75.5) | |
| Suburban | 53 (10.0) | 9 (17.0) | |
| Rural | 26 (4.9) | 4 (7.6) | |
| Ownership type, N (%) | <0.001 | ||
| For profit | 129 (24.3) | 1 (1.9) | |
| Nonprofit | 355 (66.9) | 44 (83.0) | |
| Government | 47 (8.9) | 8 (15.1) | |
| Multihospital affiliation, N (%) | 0.032 | ||
| Yes | 385 (72.5) | 31 (58.5) | |
| No | 146 (27.5) | 22 (41.5) | |
| Risk‐standardized readmission rate (per 100 patients)b | |||
| For patients with HF, Mean (SD) | 24.7 (0.06) | 25.1 (0.06) | 0.088a |
| For patients with AMI, Mean (SD) | 19.5 (0.06) | 19.6 (0.07) | 0.722a |
Hospital Strategies to Reduce Readmission Rates
Many hospitals were not implementing recommended strategies at the time of enrollment. Only 52.7% of STAAR hospitals and 53.4% of H2H hospitals had a quality improvement team devoted to reducing readmissions for patients with AMI (Table 2). Half or fewer hospitals in either initiative reported that they monitored the proportion of discharge summaries sent to the primary care physician or the percent of patients with follow‐up appointments within 7 days. Less than 20% of hospitals in either initiative were monitoring readmissions to another hospital (Table 2). Most hospitals in STAAR and in H2H did not have the pharmacists responsible for medication reconciliation, with most assigning nurses this task, and few employed a third‐party database regularly for checking historical fill and current refill information (Table 3). In both initiatives, a small minority of hospitals reported that patients were always discharged with a follow‐up appointment already made, and less than half of hospitals had assigned someone to follow up on test results that return after the patient was discharged (Table 4).
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Hospital has reducing preventable readmissions as a written objective | ||
| Strongly agree/agree | 478 (89.9%) | 53 (96.4%) |
| Not sure/disagree/strongly disagree | 54 (10.2%) | 2 (3.6%) |
| Hospital has a reliable process in place to identify patients with HF at the time they are admitted | 438 (82.6%) | 50 (90.9%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with HF | 462 (86.8%) | 49 (89.1%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with AMI | 284 (53.4%) | 29 (52.7%) |
| Hospital has a multidisciplinary team to manage the care of patients who are at high risk of readmission | 299 (56.4%) | 42 (76.4%)a |
| Hospital has partnered with the following to reduce readmission rates | ||
| Community homecare agencies and/or skilled nursing facilities | 358 (67.6%) | 48 (87.3%)a |
| Community physicians or physician groups | 262 (49.6%) | 42 (76.4%)a |
| Other local hospitals | 123 (23.3%) | 23 (41.8%)a |
| Hospital tracks the following for quality improvement efforts: | ||
| Timeliness of discharge summary | 373 (70.6%) | 40 (72.7%) |
| Proportion of discharge summaries sent to primary physician | 121 (23.0%) | 17 (31.5%) |
| Percent of patients discharged with follow‐up appointment 7 days | 168 (31.9%) | 27 (50.0%) |
| Accuracy of medication reconciliation | 385 (72.9%) | 36 (66.7%) |
| 30‐day readmission rate | 499 (94.5%) | 54 (98.2%) |
| Early (<7 day) readmission rate | 293 (55.5%) | 26 (48.2%)a |
| Proportion of patients readmitted to another hospital | 61 (11.6%) | 9 (16.7%) |
| Has a designated person or group to review unplanned readmissions that occur within 30 days of the original discharge | 338 (63.9%) | 43 (78.2%) |
| Estimates risk of readmission in a formal way and uses it in clinical care during patient hospitalization | 118 (22.3%) | 22 (40.0%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Who is responsible for medication reconciliation at discharge? | ||
| Nurse | ||
| Never | 53 (10.0%) | 12 (22.2%)b |
| Sometimes | 51 (9.6%) | 13 (24.1%) |
| Usually | 49 (9.3%) | 5 (9.3%) |
| Always | 376 (71.1%) | 24 (44.4%) |
| Pharmacist | ||
| Never | 309 (58.5%) | 30 (55.6%) |
| Sometimes | 163 (30.9%) | 21 (38.9%) |
| Usually | 21 (4.0%) | 1 (1.9%) |
| Always | 35 (6.6%) | 2 (3.7%) |
| Responsibility is not formally assigned | ||
| Never | 453 (86.1%) | 41 (77.4%) |
| Sometimes | 23 (4.4%) | 6 (11.3%) |
| Usually | 21 (4.0%) | 4 (7.6%) |
| Always | 29 (5.5%) | 2 (3.8%) |
| Tools in place to facilitate medication reconciliationc | ||
| Paper‐based standardization form | 290 (54.5%) | 31 (56.4%) |
| Electronic medical record/Web‐based form | 392 (73.7%) | 38 (69.1%) |
| How often does each of the following occur as part of the medication reconciliation process at your hospital? | ||
| Emergency medicine staff obtains medication history | ||
| Never | 3 (0.6%) | 0 |
| Sometimes | 39 (7.4%) | 5 (9.1%) |
| Usually | 152 (28.7%) | 20 (36.4%) |
| Always | 336 (63.4%) | 30 (54.6%) |
| Admitting medical team obtains medication history | ||
| Never | 8 (1.5%) | 1 (1.8%) |
| Sometimes | 33 (6.2%) | 6 (10.9%) |
| Usually | 97 (18.3%) | 15 (27.3%) |
| Always | 392 (74.0%) | 33 (60.0%) |
| Pharmacist or pharmacy technician obtains medication history | ||
| Never | 244 (46.1%) | 19 (34.6%) |
| Sometimes | 160 (30.3%) | 16 (29.1%) |
| Usually | 47 (8.9%) | 10 (18.2%) |
| Always | 78 (14.7%) | 10 (18.2%) |
| Contact is made with outside pharmacies | ||
| Never | 76 (14.4%) | 3 (5.5%) |
| Sometimes | 366 (69.3%) | 42 (76.4%) |
| Usually | 69 (13.1%) | 6 (10.9%) |
| Always | 17 (3.2%) | 4 (7.3%) |
| Contact is made with primary physician | ||
| Never | 27 (5.1%) | 2 (3.6%) |
| Sometimes | 280 (52.9%) | 30 (54.6%) |
| Usually | 148 (28.0%) | 18 (32.7%) |
| Always | 74 (14.0%) | 5 (9.1%) |
| Outpatient and inpatient prescription records are linked electronically | ||
| Never | 324 (61.4%) | 28 (50.9%) |
| Sometimes | 91 (17.2%) | 14 (25.5%) |
| Usually | 61 (11.6%) | 8 (14.6%) |
| Always | 52 (9.9%) | 5 (9.1%) |
| Third‐party prescription database that provides historical fill and refill information (eg, Health Care Systems) | ||
| Never | 441 (83.5%) | 37 (67.3%) |
| Sometimes | 54 (10.2%) | 10 (18.2%) |
| Usually | 14 (2.7%) | 4 (7.3%) |
| Always | 19 (3.6%) | 4 (7.3%) |
| All patients (or their caregivers) receive at the time of discharge information about the purpose of each medication, which medications are new, which medications have changed in dose or frequency, and/or which medications are to be stopped | 407 (76.9%) | 35 (63.6%) |
| Hospital promotes use of teach‐back techniques (having the patient teach new information back to educator) | 371 (69.9%) | 48 (87.3%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| For all patients | ||
| All patients (or their caregivers) receive the following in written form at the time of discharge: | ||
| Discharge instructions | 485 (91.3%) | 45 (81.8%) |
| Names, doses, and frequency of all discharge medications | 463 (87.4%) | 42 (76.4%) |
| Educational information about heart failure, when relevant | 385 (72.5%) | 37 (67.3%) |
| Symptoms that prompt an immediate call to a physician or return to hospital | 352 (66.4%) | 33 (60.0%) |
| Educational information about AMI | 348 (65.5%) | 36 (66.7%) |
| Any type of emergency plana | 312 (58.8%) | 26 (47.3%) |
| Action plan for heart failure patients for managing changes in condition | 282 (53.1%) | 28 (50.9%) |
| Personal health record | 139 (26.3%) | 23 (41.8%) |
| Discharge summary | 104 (19.6%) | 12 (21.8%) |
| Patients are discharged from the hospital with an outpatient follow‐up appointment already arranged | ||
| Never | 20 (3.8%) | 1 (1.8%) |
| Sometimes | 222 (41.9%) | 26 (47.3%) |
| Usually | 233 (44.0%) | 26 (47.3%) |
| Always | 55 (10.4%) | 2 (3.6%) |
| Patients with home health services are provided direct contact information for a specific inpatient physician in case of questions | 249 (47.1%) | 35 (63.6%) |
| Process is in place to ensure outpatient physicians are alerted to the patient's discharge within 48 hours of discharge | 199 (37.6%) | 37 (67.3%)b |
| Proportion of patients for whom a paper or electronic discharge summary is sent directly to the patient's primary physician | ||
| None | 43 (8.1%) | 3 (5.5%) |
| Some | 153 (28.9%) | 14 (25.5%) |
| Most | 200 (37.8%) | 18 (32.7%) |
| All | 133 (25.1%) | 20 (36.4%) |
| Patient's discharge summary typically completed and available for viewing | ||
| Upon discharge | 42 (8.0%) | 5 (9.1%) |
| Within 48 hours of discharge | 222 (42.1%) | 33 (60.0%) |
| Within 7 days | 94 (17.8%) | 10 (18.2%) |
| Within 30 days | 157 (29.7%) | 7 (12.7%) |
| There are no explicit goals or policies defining a time‐frame for completing the discharge summary | 13 (2.5%) | 0 |
| Someone in the hospital is assigned to follow up on test results that return after the patient is discharged | 191 (36.2%) | 27 (49.1%) |
| Patients are regularly called after discharge to either follow up on postdischarge needs or to provide additional education | 334 (63.0%) | 38 (69.1%) |
| Home visits are arranged for all or most patients after discharge | 114 (21.5%) | 9 (16.4%) |
| After discharge, patients: | ||
| Receive telemonitoring | ||
| None | 241 (45.5%) | 12 (21.8%)a |
| Some | 265 (50.0%) | 41 (74.6%) |
| Most | 23 (4.3%) | 1 (1.8%) |
| All | 1 (0.2%) | 1 (1.8%) |
| Receive referrals to cardiac rehabilitation | ||
| None | 27 (5.1%) | 4 (7.4%)b |
| Some | 190 (36.0%) | 28 (51.9%) |
| Most | 203 (38.5%) | 17 (31.5%) |
| All | 108 (20.5%) | 5 (9.3%) |
| Are enrolled in chronic disease management programs | ||
| None | 161 (30.4%) | 13 (23.6%) |
| Some | 321 (60.7%) | 34 (61.8%) |
| Most | 41 (7.8%) | 7 (12.7%) |
| All | 6 (1.1%) | 1 (1.8%) |
| For patients transferred to skilled nursing facilities | ||
| Nurse‐to‐nurse report is always conducted prior to transfer | 326 (61.5%) | 22 (40.0%)a |
| Information always provided to the facility upon discharge | ||
| Completed discharge summary | 252 (47.6%) | 27 (49.1%) |
| Reconciled medication list | 436 (82.3%) | 46 (83.6%) |
| Medication administration record | 352 (66.4%) | 38 (69.1%) |
| Direct contact number of inpatient treating physician | 180 (34.0%) | 29 (52.7%)b |
Differences in the use of strategies by STAAR versus H2H hospitals were significant (P<0.01) in unadjusted analysis for several strategies that were attenuated and nonsignificant after adjustment for census region and ownership type (Tables 24). STAAR compared with H2H hospitals were more likely to have: (1) used a multidisciplinary team to care for patients at high risk of readmission, (2) partnered with community homecare agencies and/or skilled nursing facilities, (3) partnered with community physicians or physician groups, (4) partnered with other local hospitals to reduce preventable readmissions, (5) estimated risk of readmission in a formal way and used it in clinical care, (6) used teach‐back techniques, and (7) used telemonitoring. In contrast, H2H hospitals were more likely than STAAR hospitals to have monitored 7‐day readmission rates and to have conducted nurse‐to‐nurse report usually or always prior to discharge to nursing home facilities.
In multivariable analysis, STAAR and H2H hospitals differed significantly (P<0.01) for 4 additional strategies. STAAR hospitals were more likely to have (1) ensured outpatient physicians were alerted within 48 hours of patient discharge, and (2) provided skilled nursing facilities the direct contact number of the inpatient treating physician for patients transferred. H2H hospitals were more likely to have (1) assigned responsibility for medication reconciliation to nurses, and (2) referred discharged patients to cardiac rehabilitation services.
DISCUSSION
We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.
Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.
The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.
Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.
The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.
In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.
Disclosures
This work was funded by the Commonwealth Fund and the Donaghue Foundation. Dr. Krumholz is supported by grant U01 HL105270‐03 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute in Bethesda, Maryland. Dr. Horwitz is supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Horwitz is also a Pepper Scholar with support from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA). Dr. Krumholz discloses that he is the recipient of a research grant from Medtronic, Inc. through Yale University and is chair of a cardiac scientific advisory board for UnitedHealth.
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- , , , , . “Learning by doing”—resident perspectives on developing competency in high‐quality discharge care. J Gen Intern Med. 2012;27:1188–1194.
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- Society of Hospital Medicine. Project BOOST: Better Outcomes by Optimizing Safe Transitions Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed January 19, 2013.
- Society of Hospital Medicine. The BOOST Tools. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/html_CC/06Boost/07_Boost_Tools.cfm. Accessed January 19, 2013.
- , , , et al. Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists. J Hosp Med. 2006;1:354–360.
- Institute for Healthcare Improvement. Overview: STate action on avoidable rehospitalizations (STAAR) initiative. Available at: http://www.ihi.org/offerings/Initiatives/STAAR/Pages/default.aspx. Accessed February 20, 2010.
- , , , et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60:607–614.
- , , . Effectiveness and feasibility of pharmacist‐led admission medication reconciliation for geriatric patients. J Pharm Pract. 2012;25:136–141.
- , , , et al. Effect of admission medication reconciliation on adverse drug events from admission medication changes. Arch Intern Med. 2011;171:860–861.
- , , , . Potential risk of medication discrepancies and reconciliation errors at admission and discharge from an inpatient medical service. Ann Pharmacother. 2010;44:1747–1754.
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- , . Pharmacists' medication reconciliation‐related clinical interventions in a children's hospital. Jt Comm J Qual Patient Saf. 2009;35:278–282.
- , , , et al. Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: an analysis of medication reconciliation errors and risk factors at hospital admission. J Gen Intern Med. 2010;25:441–447.
- , , BS, . Pharmacist‐conducted medication reconciliation in an emergency department. Am J Health Syst Pharm. 2007;64:1720–1723.
- , , , et al. Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303:1716–1722.
- , , , et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178–187.
- , , , et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. 2002;39:83–89.
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- National Quality Forum (NQF). Safe practices for better healthcare—2010 update: A consensus report. 2010. Available at: http://www. qualityforum.org/Publications/2010/04/Safe_Practices_for_Better_Health care_%Ed%80%93_2010_Update.aspx. Accessed September 28, 2012.
- , , , et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166:565–571.
- , , , et al. Medication history reconciliation by clinical pharmacists in elderly inpatients admitted from home or a nursing home. Ann Pharmacother. 2010;44:1596–1603.
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- , , , et al. An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4:243–252.
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- , , , et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
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With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]
In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.
Although previous research has examined strategies used by hospitals enrolled in H2H,10 we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.
Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.
METHODS
Study Design and Sample
We conducted a national Web‐based survey of all hospitals that had enrolled in H2H and/or STAAR from May 2009 through June 2010 (n=658 hospitals); the survey was conducted from November 1, 2010 through June 30, 2011 and completed by 599 hospitals (response rate of 91%) (see the survey tool in the Supporting Information, Appendix, in the online version of this article). To initiate contact with each hospital, we emailed the primary liaison person for the initiative at the hospital (n=594 hospitals enrolled in the H2H campaign and n=64 hospitals from Massachusetts, Michigan, and Washington enrolled in STAAR). Respondents were instructed to coordinate with other relevant staff to complete a single survey reflecting the hospital's response. Of the total 658 hospitals, 599 completed the survey, for a response rate of 91%. A total of 532 of these 599 hospitals were enrolled in H2H, 55 hospitals were enrolled in STAAR, and 12 hospitals were enrolled in both STAAR and H2H. We excluded the 12 hospitals that were enrolled in both campaigns from our analysis. All research procedures were approved by the institutional review board at the Yale School of Medicine.
Measures
We examined hospital strategies in 3 areas: quality improvement resources and performance monitoring, medication management, and discharge and follow‐up procedures. In addition, consistent with our earlier work,[10] we summarized strategies using an index of 10 specific strategies across the 3 domains. The first domain (quality improvement resources and performance monitoring) includes having a quality improvement team for reducing readmissions for heart failure, or for acute myocardial infarction, or for both; monitoring the percent of patients with follow‐up appointments within 7 days of discharge; and monitoring 30‐day readmission rates. The second domain (medication management) includes providing patient education about the purpose of each medication and any alterations to the medication list, having a pharmacist primarily responsible for conducting medication reconciliation at discharge, and having a pharmacy technician primarily responsible for obtaining medication history as part of medication reconciliation process. The third domain (discharge and follow‐up procedures) includes discharge processes in which patients or their caregivers receive an emergency plan, patients usually or always leave the hospital with an outpatient follow‐up appointment already arranged, a process is in place to ensure the outpatient physicians are alerted to the patient's discharge status within 48 hours of discharge, and patients are called after discharge to follow up on postdischarge needs or to provide additional patient education. The summary score ranged from 0 to 10, and its items are supported by a number of studies,[3, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] although definitive evidence on their effectiveness is lacking.
We also examined hospital characteristics including the number of staffed hospital beds, teaching status (hospital that is a member of Council of Teaching Hospitals [COTH], non‐COTH teaching hospital with residency approved by the Accreditation Council for Graduate Medical Education, or nonteaching hospital), multihospital affiliation (yes or no), and ownership (for profit, nonprofit, or government) using data from the Annual Survey of the American Hospital Association from 2009. We determined census regions from the US Census Bureau and urban/suburban/rural location from the 2003 Urban Influence Codes. Hospital 30‐day risk‐standardized readmission rates (RSRRs) were derived from the most recent year of data (July 2010 to June 2011) collected by the Centers for Medicare and Medicaid Services (CMS). RSRRs were calculated using the statistical model as specified by the CMS for public reporting of 30‐day RSRRs.[29, 30]
Data Analysis
We used standard frequency analysis to describe the sample of hospitals, the prevalence of each hospital strategy, and the distribution of summary variables, for both H2H and the STAAR hospitals. We examined the statistical significance of differences between the reported use of strategies to reduce readmissions in H2H versus STARR hospitals using logistic and linear regression, adjusted for hospital characteristics that differed significantly between the 2 groups in the bivariate analyses (ownership type and census region). We adjusted for hospital characteristics to isolate the independent association between the initiative (H2H or STAAR) and hospital strategies being employed. This was important given the significant differences in types of hospitals (by ownership and census region) in the H2H versus STAAR initiatives and reported variation of strategies used by hospital characteristics. Because hospitals completed the questionnaire at different times during the survey period, we adjusted for month of survey completion, but this variable was nonsignificant and therefore eliminated from the final model. We employed P<0.01 as our significance level to adjust for multiple comparisons conducted. This research was funded by the Commonwealth Fund, which had no influence on the methodology, findings, or interpretation. All analyses were conducted in SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
Characteristics of Hospital Sample
Of the 587 hospitals in our sample, 55 hospitals (9%) were enrolled in STAAR and 532 hospitals (91%) were enrolled in H2H. The roles reported by respondents varied, and many respondents reported having more than 1 role; nearly 60% were from quality management departments, 24% were from cardiology departments, 24% had other clinical roles, 17% were from case management or care coordination, and 7% reported working in nonclinical roles. Hospital characteristics are reported in Table 1.
| Characteristic | H2H, N=532 | STAAR, N=55 | 2 P Value |
|---|---|---|---|
| |||
| Teaching status, N (%) | 0.185 | ||
| COTH teaching | 70 (13.2) | 12 (22.2) | |
| Non‐COTH teaching | 105 (19.7) | 9 (16.7) | |
| Nonteaching | 357 (67.1) | 33 (61.1) | |
| Number of staffed beds, N (%) | 0.598 | ||
| <200 beds | 180 (34.2) | 22 (42.3) | |
| 200399 beds | 199 (37.8) | 19 (36.5) | |
| 400599 beds | 90 (17.1) | 6 (11.5) | |
| 600+ beds | 58 (11.0) | 5 (9.6) | |
| Mean (SD) | 315 (218) | 254 (206) | 0.056a |
| Census region, N (%) | <0.001 | ||
| New England | 21 (4.0) | 14 (26.4) | |
| Middle Atlantic | 58 (10.9) | 0 | |
| East North Central | 95 (17.9) | 27 (50.9) | |
| West North Central | 45 (8.5) | 0 | |
| South Atlantic | 122 (23.0) | 0 | |
| East South Central | 52 (9.8) | 0 | |
| West South Central | 54 (10.2) | 0 | |
| Mountain | 33 (6.2) | 0 | |
| Pacific | 50 (9.4) | 12 (22.6) | |
| Puerto Rico | 1 (0.2) | 0 | |
| Geographic location, N (%) | 0.184 | ||
| Urban | 451 (85.1) | 40 (75.5) | |
| Suburban | 53 (10.0) | 9 (17.0) | |
| Rural | 26 (4.9) | 4 (7.6) | |
| Ownership type, N (%) | <0.001 | ||
| For profit | 129 (24.3) | 1 (1.9) | |
| Nonprofit | 355 (66.9) | 44 (83.0) | |
| Government | 47 (8.9) | 8 (15.1) | |
| Multihospital affiliation, N (%) | 0.032 | ||
| Yes | 385 (72.5) | 31 (58.5) | |
| No | 146 (27.5) | 22 (41.5) | |
| Risk‐standardized readmission rate (per 100 patients)b | |||
| For patients with HF, Mean (SD) | 24.7 (0.06) | 25.1 (0.06) | 0.088a |
| For patients with AMI, Mean (SD) | 19.5 (0.06) | 19.6 (0.07) | 0.722a |
Hospital Strategies to Reduce Readmission Rates
Many hospitals were not implementing recommended strategies at the time of enrollment. Only 52.7% of STAAR hospitals and 53.4% of H2H hospitals had a quality improvement team devoted to reducing readmissions for patients with AMI (Table 2). Half or fewer hospitals in either initiative reported that they monitored the proportion of discharge summaries sent to the primary care physician or the percent of patients with follow‐up appointments within 7 days. Less than 20% of hospitals in either initiative were monitoring readmissions to another hospital (Table 2). Most hospitals in STAAR and in H2H did not have the pharmacists responsible for medication reconciliation, with most assigning nurses this task, and few employed a third‐party database regularly for checking historical fill and current refill information (Table 3). In both initiatives, a small minority of hospitals reported that patients were always discharged with a follow‐up appointment already made, and less than half of hospitals had assigned someone to follow up on test results that return after the patient was discharged (Table 4).
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Hospital has reducing preventable readmissions as a written objective | ||
| Strongly agree/agree | 478 (89.9%) | 53 (96.4%) |
| Not sure/disagree/strongly disagree | 54 (10.2%) | 2 (3.6%) |
| Hospital has a reliable process in place to identify patients with HF at the time they are admitted | 438 (82.6%) | 50 (90.9%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with HF | 462 (86.8%) | 49 (89.1%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with AMI | 284 (53.4%) | 29 (52.7%) |
| Hospital has a multidisciplinary team to manage the care of patients who are at high risk of readmission | 299 (56.4%) | 42 (76.4%)a |
| Hospital has partnered with the following to reduce readmission rates | ||
| Community homecare agencies and/or skilled nursing facilities | 358 (67.6%) | 48 (87.3%)a |
| Community physicians or physician groups | 262 (49.6%) | 42 (76.4%)a |
| Other local hospitals | 123 (23.3%) | 23 (41.8%)a |
| Hospital tracks the following for quality improvement efforts: | ||
| Timeliness of discharge summary | 373 (70.6%) | 40 (72.7%) |
| Proportion of discharge summaries sent to primary physician | 121 (23.0%) | 17 (31.5%) |
| Percent of patients discharged with follow‐up appointment 7 days | 168 (31.9%) | 27 (50.0%) |
| Accuracy of medication reconciliation | 385 (72.9%) | 36 (66.7%) |
| 30‐day readmission rate | 499 (94.5%) | 54 (98.2%) |
| Early (<7 day) readmission rate | 293 (55.5%) | 26 (48.2%)a |
| Proportion of patients readmitted to another hospital | 61 (11.6%) | 9 (16.7%) |
| Has a designated person or group to review unplanned readmissions that occur within 30 days of the original discharge | 338 (63.9%) | 43 (78.2%) |
| Estimates risk of readmission in a formal way and uses it in clinical care during patient hospitalization | 118 (22.3%) | 22 (40.0%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Who is responsible for medication reconciliation at discharge? | ||
| Nurse | ||
| Never | 53 (10.0%) | 12 (22.2%)b |
| Sometimes | 51 (9.6%) | 13 (24.1%) |
| Usually | 49 (9.3%) | 5 (9.3%) |
| Always | 376 (71.1%) | 24 (44.4%) |
| Pharmacist | ||
| Never | 309 (58.5%) | 30 (55.6%) |
| Sometimes | 163 (30.9%) | 21 (38.9%) |
| Usually | 21 (4.0%) | 1 (1.9%) |
| Always | 35 (6.6%) | 2 (3.7%) |
| Responsibility is not formally assigned | ||
| Never | 453 (86.1%) | 41 (77.4%) |
| Sometimes | 23 (4.4%) | 6 (11.3%) |
| Usually | 21 (4.0%) | 4 (7.6%) |
| Always | 29 (5.5%) | 2 (3.8%) |
| Tools in place to facilitate medication reconciliationc | ||
| Paper‐based standardization form | 290 (54.5%) | 31 (56.4%) |
| Electronic medical record/Web‐based form | 392 (73.7%) | 38 (69.1%) |
| How often does each of the following occur as part of the medication reconciliation process at your hospital? | ||
| Emergency medicine staff obtains medication history | ||
| Never | 3 (0.6%) | 0 |
| Sometimes | 39 (7.4%) | 5 (9.1%) |
| Usually | 152 (28.7%) | 20 (36.4%) |
| Always | 336 (63.4%) | 30 (54.6%) |
| Admitting medical team obtains medication history | ||
| Never | 8 (1.5%) | 1 (1.8%) |
| Sometimes | 33 (6.2%) | 6 (10.9%) |
| Usually | 97 (18.3%) | 15 (27.3%) |
| Always | 392 (74.0%) | 33 (60.0%) |
| Pharmacist or pharmacy technician obtains medication history | ||
| Never | 244 (46.1%) | 19 (34.6%) |
| Sometimes | 160 (30.3%) | 16 (29.1%) |
| Usually | 47 (8.9%) | 10 (18.2%) |
| Always | 78 (14.7%) | 10 (18.2%) |
| Contact is made with outside pharmacies | ||
| Never | 76 (14.4%) | 3 (5.5%) |
| Sometimes | 366 (69.3%) | 42 (76.4%) |
| Usually | 69 (13.1%) | 6 (10.9%) |
| Always | 17 (3.2%) | 4 (7.3%) |
| Contact is made with primary physician | ||
| Never | 27 (5.1%) | 2 (3.6%) |
| Sometimes | 280 (52.9%) | 30 (54.6%) |
| Usually | 148 (28.0%) | 18 (32.7%) |
| Always | 74 (14.0%) | 5 (9.1%) |
| Outpatient and inpatient prescription records are linked electronically | ||
| Never | 324 (61.4%) | 28 (50.9%) |
| Sometimes | 91 (17.2%) | 14 (25.5%) |
| Usually | 61 (11.6%) | 8 (14.6%) |
| Always | 52 (9.9%) | 5 (9.1%) |
| Third‐party prescription database that provides historical fill and refill information (eg, Health Care Systems) | ||
| Never | 441 (83.5%) | 37 (67.3%) |
| Sometimes | 54 (10.2%) | 10 (18.2%) |
| Usually | 14 (2.7%) | 4 (7.3%) |
| Always | 19 (3.6%) | 4 (7.3%) |
| All patients (or their caregivers) receive at the time of discharge information about the purpose of each medication, which medications are new, which medications have changed in dose or frequency, and/or which medications are to be stopped | 407 (76.9%) | 35 (63.6%) |
| Hospital promotes use of teach‐back techniques (having the patient teach new information back to educator) | 371 (69.9%) | 48 (87.3%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| For all patients | ||
| All patients (or their caregivers) receive the following in written form at the time of discharge: | ||
| Discharge instructions | 485 (91.3%) | 45 (81.8%) |
| Names, doses, and frequency of all discharge medications | 463 (87.4%) | 42 (76.4%) |
| Educational information about heart failure, when relevant | 385 (72.5%) | 37 (67.3%) |
| Symptoms that prompt an immediate call to a physician or return to hospital | 352 (66.4%) | 33 (60.0%) |
| Educational information about AMI | 348 (65.5%) | 36 (66.7%) |
| Any type of emergency plana | 312 (58.8%) | 26 (47.3%) |
| Action plan for heart failure patients for managing changes in condition | 282 (53.1%) | 28 (50.9%) |
| Personal health record | 139 (26.3%) | 23 (41.8%) |
| Discharge summary | 104 (19.6%) | 12 (21.8%) |
| Patients are discharged from the hospital with an outpatient follow‐up appointment already arranged | ||
| Never | 20 (3.8%) | 1 (1.8%) |
| Sometimes | 222 (41.9%) | 26 (47.3%) |
| Usually | 233 (44.0%) | 26 (47.3%) |
| Always | 55 (10.4%) | 2 (3.6%) |
| Patients with home health services are provided direct contact information for a specific inpatient physician in case of questions | 249 (47.1%) | 35 (63.6%) |
| Process is in place to ensure outpatient physicians are alerted to the patient's discharge within 48 hours of discharge | 199 (37.6%) | 37 (67.3%)b |
| Proportion of patients for whom a paper or electronic discharge summary is sent directly to the patient's primary physician | ||
| None | 43 (8.1%) | 3 (5.5%) |
| Some | 153 (28.9%) | 14 (25.5%) |
| Most | 200 (37.8%) | 18 (32.7%) |
| All | 133 (25.1%) | 20 (36.4%) |
| Patient's discharge summary typically completed and available for viewing | ||
| Upon discharge | 42 (8.0%) | 5 (9.1%) |
| Within 48 hours of discharge | 222 (42.1%) | 33 (60.0%) |
| Within 7 days | 94 (17.8%) | 10 (18.2%) |
| Within 30 days | 157 (29.7%) | 7 (12.7%) |
| There are no explicit goals or policies defining a time‐frame for completing the discharge summary | 13 (2.5%) | 0 |
| Someone in the hospital is assigned to follow up on test results that return after the patient is discharged | 191 (36.2%) | 27 (49.1%) |
| Patients are regularly called after discharge to either follow up on postdischarge needs or to provide additional education | 334 (63.0%) | 38 (69.1%) |
| Home visits are arranged for all or most patients after discharge | 114 (21.5%) | 9 (16.4%) |
| After discharge, patients: | ||
| Receive telemonitoring | ||
| None | 241 (45.5%) | 12 (21.8%)a |
| Some | 265 (50.0%) | 41 (74.6%) |
| Most | 23 (4.3%) | 1 (1.8%) |
| All | 1 (0.2%) | 1 (1.8%) |
| Receive referrals to cardiac rehabilitation | ||
| None | 27 (5.1%) | 4 (7.4%)b |
| Some | 190 (36.0%) | 28 (51.9%) |
| Most | 203 (38.5%) | 17 (31.5%) |
| All | 108 (20.5%) | 5 (9.3%) |
| Are enrolled in chronic disease management programs | ||
| None | 161 (30.4%) | 13 (23.6%) |
| Some | 321 (60.7%) | 34 (61.8%) |
| Most | 41 (7.8%) | 7 (12.7%) |
| All | 6 (1.1%) | 1 (1.8%) |
| For patients transferred to skilled nursing facilities | ||
| Nurse‐to‐nurse report is always conducted prior to transfer | 326 (61.5%) | 22 (40.0%)a |
| Information always provided to the facility upon discharge | ||
| Completed discharge summary | 252 (47.6%) | 27 (49.1%) |
| Reconciled medication list | 436 (82.3%) | 46 (83.6%) |
| Medication administration record | 352 (66.4%) | 38 (69.1%) |
| Direct contact number of inpatient treating physician | 180 (34.0%) | 29 (52.7%)b |
Differences in the use of strategies by STAAR versus H2H hospitals were significant (P<0.01) in unadjusted analysis for several strategies that were attenuated and nonsignificant after adjustment for census region and ownership type (Tables 24). STAAR compared with H2H hospitals were more likely to have: (1) used a multidisciplinary team to care for patients at high risk of readmission, (2) partnered with community homecare agencies and/or skilled nursing facilities, (3) partnered with community physicians or physician groups, (4) partnered with other local hospitals to reduce preventable readmissions, (5) estimated risk of readmission in a formal way and used it in clinical care, (6) used teach‐back techniques, and (7) used telemonitoring. In contrast, H2H hospitals were more likely than STAAR hospitals to have monitored 7‐day readmission rates and to have conducted nurse‐to‐nurse report usually or always prior to discharge to nursing home facilities.
In multivariable analysis, STAAR and H2H hospitals differed significantly (P<0.01) for 4 additional strategies. STAAR hospitals were more likely to have (1) ensured outpatient physicians were alerted within 48 hours of patient discharge, and (2) provided skilled nursing facilities the direct contact number of the inpatient treating physician for patients transferred. H2H hospitals were more likely to have (1) assigned responsibility for medication reconciliation to nurses, and (2) referred discharged patients to cardiac rehabilitation services.
DISCUSSION
We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.
Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.
The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.
Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.
The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.
In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.
Disclosures
This work was funded by the Commonwealth Fund and the Donaghue Foundation. Dr. Krumholz is supported by grant U01 HL105270‐03 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute in Bethesda, Maryland. Dr. Horwitz is supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Horwitz is also a Pepper Scholar with support from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA). Dr. Krumholz discloses that he is the recipient of a research grant from Medtronic, Inc. through Yale University and is chair of a cardiac scientific advisory board for UnitedHealth.
With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]
In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.
Although previous research has examined strategies used by hospitals enrolled in H2H,10 we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.
Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.
METHODS
Study Design and Sample
We conducted a national Web‐based survey of all hospitals that had enrolled in H2H and/or STAAR from May 2009 through June 2010 (n=658 hospitals); the survey was conducted from November 1, 2010 through June 30, 2011 and completed by 599 hospitals (response rate of 91%) (see the survey tool in the Supporting Information, Appendix, in the online version of this article). To initiate contact with each hospital, we emailed the primary liaison person for the initiative at the hospital (n=594 hospitals enrolled in the H2H campaign and n=64 hospitals from Massachusetts, Michigan, and Washington enrolled in STAAR). Respondents were instructed to coordinate with other relevant staff to complete a single survey reflecting the hospital's response. Of the total 658 hospitals, 599 completed the survey, for a response rate of 91%. A total of 532 of these 599 hospitals were enrolled in H2H, 55 hospitals were enrolled in STAAR, and 12 hospitals were enrolled in both STAAR and H2H. We excluded the 12 hospitals that were enrolled in both campaigns from our analysis. All research procedures were approved by the institutional review board at the Yale School of Medicine.
Measures
We examined hospital strategies in 3 areas: quality improvement resources and performance monitoring, medication management, and discharge and follow‐up procedures. In addition, consistent with our earlier work,[10] we summarized strategies using an index of 10 specific strategies across the 3 domains. The first domain (quality improvement resources and performance monitoring) includes having a quality improvement team for reducing readmissions for heart failure, or for acute myocardial infarction, or for both; monitoring the percent of patients with follow‐up appointments within 7 days of discharge; and monitoring 30‐day readmission rates. The second domain (medication management) includes providing patient education about the purpose of each medication and any alterations to the medication list, having a pharmacist primarily responsible for conducting medication reconciliation at discharge, and having a pharmacy technician primarily responsible for obtaining medication history as part of medication reconciliation process. The third domain (discharge and follow‐up procedures) includes discharge processes in which patients or their caregivers receive an emergency plan, patients usually or always leave the hospital with an outpatient follow‐up appointment already arranged, a process is in place to ensure the outpatient physicians are alerted to the patient's discharge status within 48 hours of discharge, and patients are called after discharge to follow up on postdischarge needs or to provide additional patient education. The summary score ranged from 0 to 10, and its items are supported by a number of studies,[3, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] although definitive evidence on their effectiveness is lacking.
We also examined hospital characteristics including the number of staffed hospital beds, teaching status (hospital that is a member of Council of Teaching Hospitals [COTH], non‐COTH teaching hospital with residency approved by the Accreditation Council for Graduate Medical Education, or nonteaching hospital), multihospital affiliation (yes or no), and ownership (for profit, nonprofit, or government) using data from the Annual Survey of the American Hospital Association from 2009. We determined census regions from the US Census Bureau and urban/suburban/rural location from the 2003 Urban Influence Codes. Hospital 30‐day risk‐standardized readmission rates (RSRRs) were derived from the most recent year of data (July 2010 to June 2011) collected by the Centers for Medicare and Medicaid Services (CMS). RSRRs were calculated using the statistical model as specified by the CMS for public reporting of 30‐day RSRRs.[29, 30]
Data Analysis
We used standard frequency analysis to describe the sample of hospitals, the prevalence of each hospital strategy, and the distribution of summary variables, for both H2H and the STAAR hospitals. We examined the statistical significance of differences between the reported use of strategies to reduce readmissions in H2H versus STARR hospitals using logistic and linear regression, adjusted for hospital characteristics that differed significantly between the 2 groups in the bivariate analyses (ownership type and census region). We adjusted for hospital characteristics to isolate the independent association between the initiative (H2H or STAAR) and hospital strategies being employed. This was important given the significant differences in types of hospitals (by ownership and census region) in the H2H versus STAAR initiatives and reported variation of strategies used by hospital characteristics. Because hospitals completed the questionnaire at different times during the survey period, we adjusted for month of survey completion, but this variable was nonsignificant and therefore eliminated from the final model. We employed P<0.01 as our significance level to adjust for multiple comparisons conducted. This research was funded by the Commonwealth Fund, which had no influence on the methodology, findings, or interpretation. All analyses were conducted in SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
Characteristics of Hospital Sample
Of the 587 hospitals in our sample, 55 hospitals (9%) were enrolled in STAAR and 532 hospitals (91%) were enrolled in H2H. The roles reported by respondents varied, and many respondents reported having more than 1 role; nearly 60% were from quality management departments, 24% were from cardiology departments, 24% had other clinical roles, 17% were from case management or care coordination, and 7% reported working in nonclinical roles. Hospital characteristics are reported in Table 1.
| Characteristic | H2H, N=532 | STAAR, N=55 | 2 P Value |
|---|---|---|---|
| |||
| Teaching status, N (%) | 0.185 | ||
| COTH teaching | 70 (13.2) | 12 (22.2) | |
| Non‐COTH teaching | 105 (19.7) | 9 (16.7) | |
| Nonteaching | 357 (67.1) | 33 (61.1) | |
| Number of staffed beds, N (%) | 0.598 | ||
| <200 beds | 180 (34.2) | 22 (42.3) | |
| 200399 beds | 199 (37.8) | 19 (36.5) | |
| 400599 beds | 90 (17.1) | 6 (11.5) | |
| 600+ beds | 58 (11.0) | 5 (9.6) | |
| Mean (SD) | 315 (218) | 254 (206) | 0.056a |
| Census region, N (%) | <0.001 | ||
| New England | 21 (4.0) | 14 (26.4) | |
| Middle Atlantic | 58 (10.9) | 0 | |
| East North Central | 95 (17.9) | 27 (50.9) | |
| West North Central | 45 (8.5) | 0 | |
| South Atlantic | 122 (23.0) | 0 | |
| East South Central | 52 (9.8) | 0 | |
| West South Central | 54 (10.2) | 0 | |
| Mountain | 33 (6.2) | 0 | |
| Pacific | 50 (9.4) | 12 (22.6) | |
| Puerto Rico | 1 (0.2) | 0 | |
| Geographic location, N (%) | 0.184 | ||
| Urban | 451 (85.1) | 40 (75.5) | |
| Suburban | 53 (10.0) | 9 (17.0) | |
| Rural | 26 (4.9) | 4 (7.6) | |
| Ownership type, N (%) | <0.001 | ||
| For profit | 129 (24.3) | 1 (1.9) | |
| Nonprofit | 355 (66.9) | 44 (83.0) | |
| Government | 47 (8.9) | 8 (15.1) | |
| Multihospital affiliation, N (%) | 0.032 | ||
| Yes | 385 (72.5) | 31 (58.5) | |
| No | 146 (27.5) | 22 (41.5) | |
| Risk‐standardized readmission rate (per 100 patients)b | |||
| For patients with HF, Mean (SD) | 24.7 (0.06) | 25.1 (0.06) | 0.088a |
| For patients with AMI, Mean (SD) | 19.5 (0.06) | 19.6 (0.07) | 0.722a |
Hospital Strategies to Reduce Readmission Rates
Many hospitals were not implementing recommended strategies at the time of enrollment. Only 52.7% of STAAR hospitals and 53.4% of H2H hospitals had a quality improvement team devoted to reducing readmissions for patients with AMI (Table 2). Half or fewer hospitals in either initiative reported that they monitored the proportion of discharge summaries sent to the primary care physician or the percent of patients with follow‐up appointments within 7 days. Less than 20% of hospitals in either initiative were monitoring readmissions to another hospital (Table 2). Most hospitals in STAAR and in H2H did not have the pharmacists responsible for medication reconciliation, with most assigning nurses this task, and few employed a third‐party database regularly for checking historical fill and current refill information (Table 3). In both initiatives, a small minority of hospitals reported that patients were always discharged with a follow‐up appointment already made, and less than half of hospitals had assigned someone to follow up on test results that return after the patient was discharged (Table 4).
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Hospital has reducing preventable readmissions as a written objective | ||
| Strongly agree/agree | 478 (89.9%) | 53 (96.4%) |
| Not sure/disagree/strongly disagree | 54 (10.2%) | 2 (3.6%) |
| Hospital has a reliable process in place to identify patients with HF at the time they are admitted | 438 (82.6%) | 50 (90.9%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with HF | 462 (86.8%) | 49 (89.1%) |
| Hospital has quality improvement teams devoted to reducing preventable readmissions for patients with AMI | 284 (53.4%) | 29 (52.7%) |
| Hospital has a multidisciplinary team to manage the care of patients who are at high risk of readmission | 299 (56.4%) | 42 (76.4%)a |
| Hospital has partnered with the following to reduce readmission rates | ||
| Community homecare agencies and/or skilled nursing facilities | 358 (67.6%) | 48 (87.3%)a |
| Community physicians or physician groups | 262 (49.6%) | 42 (76.4%)a |
| Other local hospitals | 123 (23.3%) | 23 (41.8%)a |
| Hospital tracks the following for quality improvement efforts: | ||
| Timeliness of discharge summary | 373 (70.6%) | 40 (72.7%) |
| Proportion of discharge summaries sent to primary physician | 121 (23.0%) | 17 (31.5%) |
| Percent of patients discharged with follow‐up appointment 7 days | 168 (31.9%) | 27 (50.0%) |
| Accuracy of medication reconciliation | 385 (72.9%) | 36 (66.7%) |
| 30‐day readmission rate | 499 (94.5%) | 54 (98.2%) |
| Early (<7 day) readmission rate | 293 (55.5%) | 26 (48.2%)a |
| Proportion of patients readmitted to another hospital | 61 (11.6%) | 9 (16.7%) |
| Has a designated person or group to review unplanned readmissions that occur within 30 days of the original discharge | 338 (63.9%) | 43 (78.2%) |
| Estimates risk of readmission in a formal way and uses it in clinical care during patient hospitalization | 118 (22.3%) | 22 (40.0%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| Who is responsible for medication reconciliation at discharge? | ||
| Nurse | ||
| Never | 53 (10.0%) | 12 (22.2%)b |
| Sometimes | 51 (9.6%) | 13 (24.1%) |
| Usually | 49 (9.3%) | 5 (9.3%) |
| Always | 376 (71.1%) | 24 (44.4%) |
| Pharmacist | ||
| Never | 309 (58.5%) | 30 (55.6%) |
| Sometimes | 163 (30.9%) | 21 (38.9%) |
| Usually | 21 (4.0%) | 1 (1.9%) |
| Always | 35 (6.6%) | 2 (3.7%) |
| Responsibility is not formally assigned | ||
| Never | 453 (86.1%) | 41 (77.4%) |
| Sometimes | 23 (4.4%) | 6 (11.3%) |
| Usually | 21 (4.0%) | 4 (7.6%) |
| Always | 29 (5.5%) | 2 (3.8%) |
| Tools in place to facilitate medication reconciliationc | ||
| Paper‐based standardization form | 290 (54.5%) | 31 (56.4%) |
| Electronic medical record/Web‐based form | 392 (73.7%) | 38 (69.1%) |
| How often does each of the following occur as part of the medication reconciliation process at your hospital? | ||
| Emergency medicine staff obtains medication history | ||
| Never | 3 (0.6%) | 0 |
| Sometimes | 39 (7.4%) | 5 (9.1%) |
| Usually | 152 (28.7%) | 20 (36.4%) |
| Always | 336 (63.4%) | 30 (54.6%) |
| Admitting medical team obtains medication history | ||
| Never | 8 (1.5%) | 1 (1.8%) |
| Sometimes | 33 (6.2%) | 6 (10.9%) |
| Usually | 97 (18.3%) | 15 (27.3%) |
| Always | 392 (74.0%) | 33 (60.0%) |
| Pharmacist or pharmacy technician obtains medication history | ||
| Never | 244 (46.1%) | 19 (34.6%) |
| Sometimes | 160 (30.3%) | 16 (29.1%) |
| Usually | 47 (8.9%) | 10 (18.2%) |
| Always | 78 (14.7%) | 10 (18.2%) |
| Contact is made with outside pharmacies | ||
| Never | 76 (14.4%) | 3 (5.5%) |
| Sometimes | 366 (69.3%) | 42 (76.4%) |
| Usually | 69 (13.1%) | 6 (10.9%) |
| Always | 17 (3.2%) | 4 (7.3%) |
| Contact is made with primary physician | ||
| Never | 27 (5.1%) | 2 (3.6%) |
| Sometimes | 280 (52.9%) | 30 (54.6%) |
| Usually | 148 (28.0%) | 18 (32.7%) |
| Always | 74 (14.0%) | 5 (9.1%) |
| Outpatient and inpatient prescription records are linked electronically | ||
| Never | 324 (61.4%) | 28 (50.9%) |
| Sometimes | 91 (17.2%) | 14 (25.5%) |
| Usually | 61 (11.6%) | 8 (14.6%) |
| Always | 52 (9.9%) | 5 (9.1%) |
| Third‐party prescription database that provides historical fill and refill information (eg, Health Care Systems) | ||
| Never | 441 (83.5%) | 37 (67.3%) |
| Sometimes | 54 (10.2%) | 10 (18.2%) |
| Usually | 14 (2.7%) | 4 (7.3%) |
| Always | 19 (3.6%) | 4 (7.3%) |
| All patients (or their caregivers) receive at the time of discharge information about the purpose of each medication, which medications are new, which medications have changed in dose or frequency, and/or which medications are to be stopped | 407 (76.9%) | 35 (63.6%) |
| Hospital promotes use of teach‐back techniques (having the patient teach new information back to educator) | 371 (69.9%) | 48 (87.3%)a |
| H2H, N=532 | STAAR, N=55 | |
|---|---|---|
| ||
| For all patients | ||
| All patients (or their caregivers) receive the following in written form at the time of discharge: | ||
| Discharge instructions | 485 (91.3%) | 45 (81.8%) |
| Names, doses, and frequency of all discharge medications | 463 (87.4%) | 42 (76.4%) |
| Educational information about heart failure, when relevant | 385 (72.5%) | 37 (67.3%) |
| Symptoms that prompt an immediate call to a physician or return to hospital | 352 (66.4%) | 33 (60.0%) |
| Educational information about AMI | 348 (65.5%) | 36 (66.7%) |
| Any type of emergency plana | 312 (58.8%) | 26 (47.3%) |
| Action plan for heart failure patients for managing changes in condition | 282 (53.1%) | 28 (50.9%) |
| Personal health record | 139 (26.3%) | 23 (41.8%) |
| Discharge summary | 104 (19.6%) | 12 (21.8%) |
| Patients are discharged from the hospital with an outpatient follow‐up appointment already arranged | ||
| Never | 20 (3.8%) | 1 (1.8%) |
| Sometimes | 222 (41.9%) | 26 (47.3%) |
| Usually | 233 (44.0%) | 26 (47.3%) |
| Always | 55 (10.4%) | 2 (3.6%) |
| Patients with home health services are provided direct contact information for a specific inpatient physician in case of questions | 249 (47.1%) | 35 (63.6%) |
| Process is in place to ensure outpatient physicians are alerted to the patient's discharge within 48 hours of discharge | 199 (37.6%) | 37 (67.3%)b |
| Proportion of patients for whom a paper or electronic discharge summary is sent directly to the patient's primary physician | ||
| None | 43 (8.1%) | 3 (5.5%) |
| Some | 153 (28.9%) | 14 (25.5%) |
| Most | 200 (37.8%) | 18 (32.7%) |
| All | 133 (25.1%) | 20 (36.4%) |
| Patient's discharge summary typically completed and available for viewing | ||
| Upon discharge | 42 (8.0%) | 5 (9.1%) |
| Within 48 hours of discharge | 222 (42.1%) | 33 (60.0%) |
| Within 7 days | 94 (17.8%) | 10 (18.2%) |
| Within 30 days | 157 (29.7%) | 7 (12.7%) |
| There are no explicit goals or policies defining a time‐frame for completing the discharge summary | 13 (2.5%) | 0 |
| Someone in the hospital is assigned to follow up on test results that return after the patient is discharged | 191 (36.2%) | 27 (49.1%) |
| Patients are regularly called after discharge to either follow up on postdischarge needs or to provide additional education | 334 (63.0%) | 38 (69.1%) |
| Home visits are arranged for all or most patients after discharge | 114 (21.5%) | 9 (16.4%) |
| After discharge, patients: | ||
| Receive telemonitoring | ||
| None | 241 (45.5%) | 12 (21.8%)a |
| Some | 265 (50.0%) | 41 (74.6%) |
| Most | 23 (4.3%) | 1 (1.8%) |
| All | 1 (0.2%) | 1 (1.8%) |
| Receive referrals to cardiac rehabilitation | ||
| None | 27 (5.1%) | 4 (7.4%)b |
| Some | 190 (36.0%) | 28 (51.9%) |
| Most | 203 (38.5%) | 17 (31.5%) |
| All | 108 (20.5%) | 5 (9.3%) |
| Are enrolled in chronic disease management programs | ||
| None | 161 (30.4%) | 13 (23.6%) |
| Some | 321 (60.7%) | 34 (61.8%) |
| Most | 41 (7.8%) | 7 (12.7%) |
| All | 6 (1.1%) | 1 (1.8%) |
| For patients transferred to skilled nursing facilities | ||
| Nurse‐to‐nurse report is always conducted prior to transfer | 326 (61.5%) | 22 (40.0%)a |
| Information always provided to the facility upon discharge | ||
| Completed discharge summary | 252 (47.6%) | 27 (49.1%) |
| Reconciled medication list | 436 (82.3%) | 46 (83.6%) |
| Medication administration record | 352 (66.4%) | 38 (69.1%) |
| Direct contact number of inpatient treating physician | 180 (34.0%) | 29 (52.7%)b |
Differences in the use of strategies by STAAR versus H2H hospitals were significant (P<0.01) in unadjusted analysis for several strategies that were attenuated and nonsignificant after adjustment for census region and ownership type (Tables 24). STAAR compared with H2H hospitals were more likely to have: (1) used a multidisciplinary team to care for patients at high risk of readmission, (2) partnered with community homecare agencies and/or skilled nursing facilities, (3) partnered with community physicians or physician groups, (4) partnered with other local hospitals to reduce preventable readmissions, (5) estimated risk of readmission in a formal way and used it in clinical care, (6) used teach‐back techniques, and (7) used telemonitoring. In contrast, H2H hospitals were more likely than STAAR hospitals to have monitored 7‐day readmission rates and to have conducted nurse‐to‐nurse report usually or always prior to discharge to nursing home facilities.
In multivariable analysis, STAAR and H2H hospitals differed significantly (P<0.01) for 4 additional strategies. STAAR hospitals were more likely to have (1) ensured outpatient physicians were alerted within 48 hours of patient discharge, and (2) provided skilled nursing facilities the direct contact number of the inpatient treating physician for patients transferred. H2H hospitals were more likely to have (1) assigned responsibility for medication reconciliation to nurses, and (2) referred discharged patients to cardiac rehabilitation services.
DISCUSSION
We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.
Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.
The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.
Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.
The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.
In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.
Disclosures
This work was funded by the Commonwealth Fund and the Donaghue Foundation. Dr. Krumholz is supported by grant U01 HL105270‐03 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute in Bethesda, Maryland. Dr. Horwitz is supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Horwitz is also a Pepper Scholar with support from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA). Dr. Krumholz discloses that he is the recipient of a research grant from Medtronic, Inc. through Yale University and is chair of a cardiac scientific advisory board for UnitedHealth.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360:1418–1428.
- , , , , . “Learning by doing”—resident perspectives on developing competency in high‐quality discharge care. J Gen Intern Med. 2012;27:1188–1194.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520–528.
- , , , . Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists. J Hosp Med. 2007;2:314–323.
- . The role of the hospitalist in quality improvement: systems for improving the care of patients with acute coronary syndrome. J Hosp Med. 2010;5(suppl 4):S1–S7.
- Society of Hospital Medicine. Project BOOST: Better Outcomes by Optimizing Safe Transitions Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed January 19, 2013.
- Society of Hospital Medicine. The BOOST Tools. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/html_CC/06Boost/07_Boost_Tools.cfm. Accessed January 19, 2013.
- , , , et al. Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists. J Hosp Med. 2006;1:354–360.
- Institute for Healthcare Improvement. Overview: STate action on avoidable rehospitalizations (STAAR) initiative. Available at: http://www.ihi.org/offerings/Initiatives/STAAR/Pages/default.aspx. Accessed February 20, 2010.
- , , , et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60:607–614.
- , , . Effectiveness and feasibility of pharmacist‐led admission medication reconciliation for geriatric patients. J Pharm Pract. 2012;25:136–141.
- , , , et al. Effect of admission medication reconciliation on adverse drug events from admission medication changes. Arch Intern Med. 2011;171:860–861.
- , , , . Potential risk of medication discrepancies and reconciliation errors at admission and discharge from an inpatient medical service. Ann Pharmacother. 2010;44:1747–1754.
- , . Measuring patient safety performance. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:59–102.
- , . Analyzing patient safety performance. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:103–118.
- , . Pharmacists' medication reconciliation‐related clinical interventions in a children's hospital. Jt Comm J Qual Patient Saf. 2009;35:278–282.
- , , , et al. Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: an analysis of medication reconciliation errors and risk factors at hospital admission. J Gen Intern Med. 2010;25:441–447.
- , , BS, . Pharmacist‐conducted medication reconciliation in an emergency department. Am J Health Syst Pharm. 2007;64:1720–1723.
- , , , et al. Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303:1716–1722.
- , , , et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178–187.
- , , , et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. 2002;39:83–89.
- . Using performance data to prioritize safety improvements. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:119–142.
- , . Medication reconciliation at an academic medical center: Implementation of a comprehensive program from admission to discharge. Emer Med J. 2010;27:911–915.
- , , , , . Medication reconciliation at an academic medical center: implementation of a comprehensive program from admission to discharge. Am J Health Syst Pharm. 2009;66:2126–2131.
- National Quality Forum (NQF). Safe practices for better healthcare—2010 update: A consensus report. 2010. Available at: http://www. qualityforum.org/Publications/2010/04/Safe_Practices_for_Better_Health care_%Ed%80%93_2010_Update.aspx. Accessed September 28, 2012.
- , , , et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166:565–571.
- , , , et al. Medication history reconciliation by clinical pharmacists in elderly inpatients admitted from home or a nursing home. Ann Pharmacother. 2010;44:1596–1603.
- , , . A review of the literature on heart failure and discharge education. Crit Care Nurs Q. 2011;34:235–245.
- , , , et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:29–37.
- , , , et al. An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4:243–252.
- , , , et al. Comprehensive quality of discharge summaries at an academic medical center [published online ahead of print [March 22, 2013]. J Hosp Med. doi: 10.1002/jhm.2021.
- , , , et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med. In press.
- , , , et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24:381–386.
- , , , et al. Patient‐physician communication at hospital discharge and patients' understanding of the postdischarge treatment plan. Arch Intern Med. 1997;157:1026–1030.
- , , , , , . Quality of care for acute myocardial infarction in rural and urban US hospitals. J Rural Health. 2004;20:99–108.
- , , , , . Regional variation in the treatment and outcomes of myocardial infarction: investigating New England's advantage. Am Heart J. 2003;146:242–249.
- , , , , . Outcomes of percutaneous coronary interventions performed at centers without and with onsite coronary artery bypass graft surgery. JAMA. 2004;292:1961–1968.
- , , , et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
- , , , et al. Recent national trends in readmission rates after heart failure hospitalization. Circ Heart Fail. 2010;3:97–103.
- , , , et al. National patterns of risk‐standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release. Circ Cardiovasc Qual Outcomes. 2010;3:459–467.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360:1418–1428.
- , , , , . “Learning by doing”—resident perspectives on developing competency in high‐quality discharge care. J Gen Intern Med. 2012;27:1188–1194.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520–528.
- , , , . Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists. J Hosp Med. 2007;2:314–323.
- . The role of the hospitalist in quality improvement: systems for improving the care of patients with acute coronary syndrome. J Hosp Med. 2010;5(suppl 4):S1–S7.
- Society of Hospital Medicine. Project BOOST: Better Outcomes by Optimizing Safe Transitions Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed January 19, 2013.
- Society of Hospital Medicine. The BOOST Tools. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/html_CC/06Boost/07_Boost_Tools.cfm. Accessed January 19, 2013.
- , , , et al. Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists. J Hosp Med. 2006;1:354–360.
- Institute for Healthcare Improvement. Overview: STate action on avoidable rehospitalizations (STAAR) initiative. Available at: http://www.ihi.org/offerings/Initiatives/STAAR/Pages/default.aspx. Accessed February 20, 2010.
- , , , et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60:607–614.
- , , . Effectiveness and feasibility of pharmacist‐led admission medication reconciliation for geriatric patients. J Pharm Pract. 2012;25:136–141.
- , , , et al. Effect of admission medication reconciliation on adverse drug events from admission medication changes. Arch Intern Med. 2011;171:860–861.
- , , , . Potential risk of medication discrepancies and reconciliation errors at admission and discharge from an inpatient medical service. Ann Pharmacother. 2010;44:1747–1754.
- , . Measuring patient safety performance. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:59–102.
- , . Analyzing patient safety performance. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:103–118.
- , . Pharmacists' medication reconciliation‐related clinical interventions in a children's hospital. Jt Comm J Qual Patient Saf. 2009;35:278–282.
- , , , et al. Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: an analysis of medication reconciliation errors and risk factors at hospital admission. J Gen Intern Med. 2010;25:441–447.
- , , BS, . Pharmacist‐conducted medication reconciliation in an emergency department. Am J Health Syst Pharm. 2007;64:1720–1723.
- , , , et al. Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303:1716–1722.
- , , , et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178–187.
- , , , et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. 2002;39:83–89.
- . Using performance data to prioritize safety improvements. In: Spath PL, ed. Error Reduction in Health Care: A Systems Approach to Improving Patient Safety. 2nd ed. Hoboken, NJ: Jossey‐Bass; 2010:119–142.
- , . Medication reconciliation at an academic medical center: Implementation of a comprehensive program from admission to discharge. Emer Med J. 2010;27:911–915.
- , , , , . Medication reconciliation at an academic medical center: implementation of a comprehensive program from admission to discharge. Am J Health Syst Pharm. 2009;66:2126–2131.
- National Quality Forum (NQF). Safe practices for better healthcare—2010 update: A consensus report. 2010. Available at: http://www. qualityforum.org/Publications/2010/04/Safe_Practices_for_Better_Health care_%Ed%80%93_2010_Update.aspx. Accessed September 28, 2012.
- , , , et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166:565–571.
- , , , et al. Medication history reconciliation by clinical pharmacists in elderly inpatients admitted from home or a nursing home. Ann Pharmacother. 2010;44:1596–1603.
- , , . A review of the literature on heart failure and discharge education. Crit Care Nurs Q. 2011;34:235–245.
- , , , et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:29–37.
- , , , et al. An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4:243–252.
- , , , et al. Comprehensive quality of discharge summaries at an academic medical center [published online ahead of print [March 22, 2013]. J Hosp Med. doi: 10.1002/jhm.2021.
- , , , et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med. In press.
- , , , et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24:381–386.
- , , , et al. Patient‐physician communication at hospital discharge and patients' understanding of the postdischarge treatment plan. Arch Intern Med. 1997;157:1026–1030.
- , , , , , . Quality of care for acute myocardial infarction in rural and urban US hospitals. J Rural Health. 2004;20:99–108.
- , , , , . Regional variation in the treatment and outcomes of myocardial infarction: investigating New England's advantage. Am Heart J. 2003;146:242–249.
- , , , , . Outcomes of percutaneous coronary interventions performed at centers without and with onsite coronary artery bypass graft surgery. JAMA. 2004;292:1961–1968.
- , , , et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
- , , , et al. Recent national trends in readmission rates after heart failure hospitalization. Circ Heart Fail. 2010;3:97–103.
- , , , et al. National patterns of risk‐standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release. Circ Cardiovasc Qual Outcomes. 2010;3:459–467.
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