Editorial

Overall rates of opioid use and chronic use for noncancer pain have increased markedly in the last 1 to 2 decades.[1, 2] Recognition of such rapidly increasing use has prompted a flurry of investigations examining the impact of what has been referred to as the opioid epidemic.[1, 3, 4] Patients receiving chronic opioid analgesics have previously been demonstrated to consume disproportionate shares of healthcare resources, including significantly more emergency room visits and days in the hospital.[5] In this issue of the Journal of Hospital Medicine, Liang and Turner[6] further demonstrate the impressive scope of healthcare resources consumed by this patient population, and extend these findings by examining the relationship between opioid dose and subsequent hospitalization in a large national cohort of middle‐aged health maintenance organization enrollees with noncancer pain. This is the first study to investigate the relationship in a general cohort of patients.

Perhaps the most striking finding of the study was an all‐cause hospitalization rate of 1120 per 10,000 person‐years among their cohort of opioid users. Considering that 5 to 8 million Americans use long‐term opioids,[7] this translates to about 500,000 to 900,000 admissions per year. The degree to which opioids themselves contribute to such hospitalizations (attributable risk) is uncertain, and it is likely that some of this risk can be explained by the idea that opioids are a marker for comorbidity, and that the conditions prompting opioid use independently increase risk of hospitalization. Studies examining more homogeneous patient populations could serve to shed light on this question. The issue of attributable risk notwithstanding, it is clear that this is a patient population that should have the attention of physicians, hospital administrators, and policy makers.

The main finding of their study is that the total opioid dose in any given 6‐month interval was more strongly associated with subsequent all‐cause hospitalization than the daily dose. This suggests that cumulative exposure is important, and possibly more important than the strength of any given prescription, at least when it comes to the outcome of hospitalization. That is not to say that the daily dose is unimportant, and the authors appropriately caution against such an interpretation. Daily dose matters, and to conclude otherwise would be incorrect for several reasons. First, among patients receiving high total doses of opioids, higher daily doses did seem to confer additional risk. Second, hospitalization is only 1 measure of risk, and multiple prior studies and a recent systematic review have concluded that higher opioid doses are strongly associated with adverse events, including overdose, abuse, addiction, motor vehicle accidents, and myocardial infarction.[8] Last, their finding that total dose more strongly predicts hospitalization than daily dose may reflect confounding by indication and underlying patient characteristics not captured in their analysis. Patients receiving a daily dose of 100 mg or more, but with a total dose of <1830 mg over 6 months, would necessarily have received opioids for a relatively brief period of time (<20 days). The indications forand patients receivingsuch short‐course, high‐dose therapy are likely to be vastly different from those for longer‐course, high‐dose therapy, in ways that could be meaningfully associated with hospitalization risk. Nonetheless, their results suggest that cumulative exposure is important as an additional metric by which to predict possible adverse consequences of opioid use.

That cumulative exposure and percent of time on opioids are associated with increased risk of subsequent hospitalization casts further doubt on the already questionable risk‐to‐benefit ratio of long‐term use of opioids for noncancer pain. A recent systematic review of the effectiveness and risks of long‐term opioid therapy for chronic pain found existing evidence insufficient to determine effectiveness for chronic pain and function, owing to lack of a single study evaluating long‐term outcomes in patients on opioid therapy versus no opioid therapy, and found evidence for a dose‐dependent risk for serious harms.[8] The authors conclude that the lack of scientific evidence on effectiveness of long‐term opioid therapy for chronic pain is in striking contrast to its widespread use in this setting. Studies examining the effect of long‐term opioid therapy on pain and function, and defining patient subgroups that may benefit from such therapy, are imperative and long overdue.

In the absence of data showing benefit, and in the face of a growing body of evidence demonstrating harm, we are obligated to reevaluate opioid prescribing for chronic noncancer pain. Until studies have evaluated the impact of opioid use on long‐term outcomes, physicians are missing a key piece of the risk‐benefit calculation, and prescribing must be done judiciously. Curbing the opioid epidemic will require initiatives of epidemic proportions, involving the entire spectrum of healthcare, from the primary care setting to the emergency department (where up to 25% of patients with chronic pain receive their opioids[7]), from researchers to policy makers, and ultimately from patient expectations to physician decision making.

Disclosures

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design, conduct, and reporting of the study. The author reports no conflicts of interest.

Overall rates of opioid use and chronic use for noncancer pain have increased markedly in the last 1 to 2 decades.[1, 2] Recognition of such rapidly increasing use has prompted a flurry of investigations examining the impact of what has been referred to as the opioid epidemic.[1, 3, 4] Patients receiving chronic opioid analgesics have previously been demonstrated to consume disproportionate shares of healthcare resources, including significantly more emergency room visits and days in the hospital.[5] In this issue of the Journal of Hospital Medicine, Liang and Turner[6] further demonstrate the impressive scope of healthcare resources consumed by this patient population, and extend these findings by examining the relationship between opioid dose and subsequent hospitalization in a large national cohort of middle‐aged health maintenance organization enrollees with noncancer pain. This is the first study to investigate the relationship in a general cohort of patients.

Perhaps the most striking finding of the study was an all‐cause hospitalization rate of 1120 per 10,000 person‐years among their cohort of opioid users. Considering that 5 to 8 million Americans use long‐term opioids,[7] this translates to about 500,000 to 900,000 admissions per year. The degree to which opioids themselves contribute to such hospitalizations (attributable risk) is uncertain, and it is likely that some of this risk can be explained by the idea that opioids are a marker for comorbidity, and that the conditions prompting opioid use independently increase risk of hospitalization. Studies examining more homogeneous patient populations could serve to shed light on this question. The issue of attributable risk notwithstanding, it is clear that this is a patient population that should have the attention of physicians, hospital administrators, and policy makers.

The main finding of their study is that the total opioid dose in any given 6‐month interval was more strongly associated with subsequent all‐cause hospitalization than the daily dose. This suggests that cumulative exposure is important, and possibly more important than the strength of any given prescription, at least when it comes to the outcome of hospitalization. That is not to say that the daily dose is unimportant, and the authors appropriately caution against such an interpretation. Daily dose matters, and to conclude otherwise would be incorrect for several reasons. First, among patients receiving high total doses of opioids, higher daily doses did seem to confer additional risk. Second, hospitalization is only 1 measure of risk, and multiple prior studies and a recent systematic review have concluded that higher opioid doses are strongly associated with adverse events, including overdose, abuse, addiction, motor vehicle accidents, and myocardial infarction.[8] Last, their finding that total dose more strongly predicts hospitalization than daily dose may reflect confounding by indication and underlying patient characteristics not captured in their analysis. Patients receiving a daily dose of 100 mg or more, but with a total dose of <1830 mg over 6 months, would necessarily have received opioids for a relatively brief period of time (<20 days). The indications forand patients receivingsuch short‐course, high‐dose therapy are likely to be vastly different from those for longer‐course, high‐dose therapy, in ways that could be meaningfully associated with hospitalization risk. Nonetheless, their results suggest that cumulative exposure is important as an additional metric by which to predict possible adverse consequences of opioid use.

That cumulative exposure and percent of time on opioids are associated with increased risk of subsequent hospitalization casts further doubt on the already questionable risk‐to‐benefit ratio of long‐term use of opioids for noncancer pain. A recent systematic review of the effectiveness and risks of long‐term opioid therapy for chronic pain found existing evidence insufficient to determine effectiveness for chronic pain and function, owing to lack of a single study evaluating long‐term outcomes in patients on opioid therapy versus no opioid therapy, and found evidence for a dose‐dependent risk for serious harms.[8] The authors conclude that the lack of scientific evidence on effectiveness of long‐term opioid therapy for chronic pain is in striking contrast to its widespread use in this setting. Studies examining the effect of long‐term opioid therapy on pain and function, and defining patient subgroups that may benefit from such therapy, are imperative and long overdue.

In the absence of data showing benefit, and in the face of a growing body of evidence demonstrating harm, we are obligated to reevaluate opioid prescribing for chronic noncancer pain. Until studies have evaluated the impact of opioid use on long‐term outcomes, physicians are missing a key piece of the risk‐benefit calculation, and prescribing must be done judiciously. Curbing the opioid epidemic will require initiatives of epidemic proportions, involving the entire spectrum of healthcare, from the primary care setting to the emergency department (where up to 25% of patients with chronic pain receive their opioids[7]), from researchers to policy makers, and ultimately from patient expectations to physician decision making.

Disclosures

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design, conduct, and reporting of the study. The author reports no conflicts of interest.

Overall rates of opioid use and chronic use for noncancer pain have increased markedly in the last 1 to 2 decades.[1, 2] Recognition of such rapidly increasing use has prompted a flurry of investigations examining the impact of what has been referred to as the opioid epidemic.[1, 3, 4] Patients receiving chronic opioid analgesics have previously been demonstrated to consume disproportionate shares of healthcare resources, including significantly more emergency room visits and days in the hospital.[5] In this issue of the Journal of Hospital Medicine, Liang and Turner[6] further demonstrate the impressive scope of healthcare resources consumed by this patient population, and extend these findings by examining the relationship between opioid dose and subsequent hospitalization in a large national cohort of middle‐aged health maintenance organization enrollees with noncancer pain. This is the first study to investigate the relationship in a general cohort of patients.

Perhaps the most striking finding of the study was an all‐cause hospitalization rate of 1120 per 10,000 person‐years among their cohort of opioid users. Considering that 5 to 8 million Americans use long‐term opioids,[7] this translates to about 500,000 to 900,000 admissions per year. The degree to which opioids themselves contribute to such hospitalizations (attributable risk) is uncertain, and it is likely that some of this risk can be explained by the idea that opioids are a marker for comorbidity, and that the conditions prompting opioid use independently increase risk of hospitalization. Studies examining more homogeneous patient populations could serve to shed light on this question. The issue of attributable risk notwithstanding, it is clear that this is a patient population that should have the attention of physicians, hospital administrators, and policy makers.

The main finding of their study is that the total opioid dose in any given 6‐month interval was more strongly associated with subsequent all‐cause hospitalization than the daily dose. This suggests that cumulative exposure is important, and possibly more important than the strength of any given prescription, at least when it comes to the outcome of hospitalization. That is not to say that the daily dose is unimportant, and the authors appropriately caution against such an interpretation. Daily dose matters, and to conclude otherwise would be incorrect for several reasons. First, among patients receiving high total doses of opioids, higher daily doses did seem to confer additional risk. Second, hospitalization is only 1 measure of risk, and multiple prior studies and a recent systematic review have concluded that higher opioid doses are strongly associated with adverse events, including overdose, abuse, addiction, motor vehicle accidents, and myocardial infarction.[8] Last, their finding that total dose more strongly predicts hospitalization than daily dose may reflect confounding by indication and underlying patient characteristics not captured in their analysis. Patients receiving a daily dose of 100 mg or more, but with a total dose of <1830 mg over 6 months, would necessarily have received opioids for a relatively brief period of time (<20 days). The indications forand patients receivingsuch short‐course, high‐dose therapy are likely to be vastly different from those for longer‐course, high‐dose therapy, in ways that could be meaningfully associated with hospitalization risk. Nonetheless, their results suggest that cumulative exposure is important as an additional metric by which to predict possible adverse consequences of opioid use.

That cumulative exposure and percent of time on opioids are associated with increased risk of subsequent hospitalization casts further doubt on the already questionable risk‐to‐benefit ratio of long‐term use of opioids for noncancer pain. A recent systematic review of the effectiveness and risks of long‐term opioid therapy for chronic pain found existing evidence insufficient to determine effectiveness for chronic pain and function, owing to lack of a single study evaluating long‐term outcomes in patients on opioid therapy versus no opioid therapy, and found evidence for a dose‐dependent risk for serious harms.[8] The authors conclude that the lack of scientific evidence on effectiveness of long‐term opioid therapy for chronic pain is in striking contrast to its widespread use in this setting. Studies examining the effect of long‐term opioid therapy on pain and function, and defining patient subgroups that may benefit from such therapy, are imperative and long overdue.

In the absence of data showing benefit, and in the face of a growing body of evidence demonstrating harm, we are obligated to reevaluate opioid prescribing for chronic noncancer pain. Until studies have evaluated the impact of opioid use on long‐term outcomes, physicians are missing a key piece of the risk‐benefit calculation, and prescribing must be done judiciously. Curbing the opioid epidemic will require initiatives of epidemic proportions, involving the entire spectrum of healthcare, from the primary care setting to the emergency department (where up to 25% of patients with chronic pain receive their opioids[7]), from researchers to policy makers, and ultimately from patient expectations to physician decision making.

Disclosures

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design, conduct, and reporting of the study. The author reports no conflicts of interest.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.

Despite 15 years of national and local investment in improving the safety of hospital care, patient safety remains a leading problem in both adult and pediatric hospitals. A 2010 study found that 180,000 Medicare beneficiaries likely die each year due to harm suffered as a result of medical care,[1] a death toll surpassed only by deaths due to cardiovascular disease and cancer. Even though initial efforts in the field have shown great promise for stemming the tide of healthcare‐associated infections,[2] surgical errors,[3] handoff failures,[4] and errors in the care of adults hospitalized for myocardial infarction and congestive heart failure,[5] much work remains to be done.[6] The root causes of many adverse events are poorly understood and unaddressed. Resultant tragedies remain all too common.

In the current issue of the Journal of Hospital Medicine, Bonafide and colleagues report the results of an innovative observational pilot study designed to assess the role of an inadequately addressed root cause of serious errors: alarm fatigue.[7] Alarm fatigue is the phenomenon of desensitization to alarms, particularly in the context of excessive false alarms. In a videotaped observational assessment of nurse response times to 5070 alarms on a pediatric ward and intensive care unit (ICU), the authors found that nurses responded significantly more slowly as the number of nonactionable alarms in the preceding 2 hours increased. Although a substantial majority of these alarms were technically valid (ie, representing true deviations of vital signs outside of the normal range rather than sensor or equipment problems), the vast majority required no action to be takenapproximately 7 out of 8 in the ICU and an astonishing 99 out of 100 on the ward.

As any hospitalist, intensivist, or nurse knows well, alarms are rampant throughout hospitals. It is impossible to walk down any hallway on a busy hospital wardnever mind an ICUwithout seeing a flashing light or 2 above a doorway, and hearing the incessant beeping of oxygen saturation and cardiovascular/respiratory monitors, a thousand bits of technology forever crying wolf. The problem, of course, is that sometimes there really is a wolf, but it is hard to take the risk seriously when the false alarms happen not just twice before a true threat materializes, as in Aesop's fable, but 7 times in the ICU, or worse, 99 times in the setting where most hospitalists practice. Moreover, even when the threat is real, in most cases it is caught in time one way or another, and no lasting harm results.

So why not simply shut off the unremitting noise? In 1987, outside of Baltimore, Amtrak experienced what at the time was the deadliest rail crash in its history after 1 of its passenger trains collided with a Conrail freight train. A major root cause of the crash was that the crew on the freight train had placed duct tape over an annoying automated signal alarm.[8, 9] Tragically, on this particular day, the suppressed alarm was all too relevant. Identifying the real alarm, however, can be nearly impossible when it sounds the same as 100 irritating sounds constantly emanating from the environment. It is the challenge of identifying the needle in the haystack, after you have developed an allergy to the hay.

What then to do? More research like that conducted by Bonafide and colleagues is needed to better understand how healthcare providers respond to the onslaught of alarms they encounter, and to inform refinement of these systems. Understanding how alarm fatigue plays out in the context of different clinical settings, with different workloads, varying levels of distraction, and different rates of true and false‐positive alarms will be critical. Furthermore, understanding how individuals' physiologic fatigue, circadian misalignment, mood, stress, and cognitive state may play into alarm response is likewise essential, if we are to design appropriate alarm systems that function effectively in the busy 24‐hour environment of healthcare. Ongoing work suggests that smart alarms, using algorithms that integrate data from multiple vital sign readings over time, may reduce the frequency of false alarms and better identify clinically significant events.[10] Replacing existing range‐limit monitors with these types of smart alarms has the potential to greatly improve both the sensitivity and specificity of hospital alarms, but further work in this area is needed.

Ultimately, if we can better separate out the signal, we will be better poised to respond to the true emergencies that arise that are currently obscured by the ever‐present noise. Better trust in the alarm systems we have would help all of us focus our energies on the problems that matter most. Doing so, we could better care for our patients, and better identify the system failures that cause them harm in our hospitals.

Disclosures: Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an Executive Council Member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety.

Despite 15 years of national and local investment in improving the safety of hospital care, patient safety remains a leading problem in both adult and pediatric hospitals. A 2010 study found that 180,000 Medicare beneficiaries likely die each year due to harm suffered as a result of medical care,[1] a death toll surpassed only by deaths due to cardiovascular disease and cancer. Even though initial efforts in the field have shown great promise for stemming the tide of healthcare‐associated infections,[2] surgical errors,[3] handoff failures,[4] and errors in the care of adults hospitalized for myocardial infarction and congestive heart failure,[5] much work remains to be done.[6] The root causes of many adverse events are poorly understood and unaddressed. Resultant tragedies remain all too common.

In the current issue of the Journal of Hospital Medicine, Bonafide and colleagues report the results of an innovative observational pilot study designed to assess the role of an inadequately addressed root cause of serious errors: alarm fatigue.[7] Alarm fatigue is the phenomenon of desensitization to alarms, particularly in the context of excessive false alarms. In a videotaped observational assessment of nurse response times to 5070 alarms on a pediatric ward and intensive care unit (ICU), the authors found that nurses responded significantly more slowly as the number of nonactionable alarms in the preceding 2 hours increased. Although a substantial majority of these alarms were technically valid (ie, representing true deviations of vital signs outside of the normal range rather than sensor or equipment problems), the vast majority required no action to be takenapproximately 7 out of 8 in the ICU and an astonishing 99 out of 100 on the ward.

As any hospitalist, intensivist, or nurse knows well, alarms are rampant throughout hospitals. It is impossible to walk down any hallway on a busy hospital wardnever mind an ICUwithout seeing a flashing light or 2 above a doorway, and hearing the incessant beeping of oxygen saturation and cardiovascular/respiratory monitors, a thousand bits of technology forever crying wolf. The problem, of course, is that sometimes there really is a wolf, but it is hard to take the risk seriously when the false alarms happen not just twice before a true threat materializes, as in Aesop's fable, but 7 times in the ICU, or worse, 99 times in the setting where most hospitalists practice. Moreover, even when the threat is real, in most cases it is caught in time one way or another, and no lasting harm results.

So why not simply shut off the unremitting noise? In 1987, outside of Baltimore, Amtrak experienced what at the time was the deadliest rail crash in its history after 1 of its passenger trains collided with a Conrail freight train. A major root cause of the crash was that the crew on the freight train had placed duct tape over an annoying automated signal alarm.[8, 9] Tragically, on this particular day, the suppressed alarm was all too relevant. Identifying the real alarm, however, can be nearly impossible when it sounds the same as 100 irritating sounds constantly emanating from the environment. It is the challenge of identifying the needle in the haystack, after you have developed an allergy to the hay.

What then to do? More research like that conducted by Bonafide and colleagues is needed to better understand how healthcare providers respond to the onslaught of alarms they encounter, and to inform refinement of these systems. Understanding how alarm fatigue plays out in the context of different clinical settings, with different workloads, varying levels of distraction, and different rates of true and false‐positive alarms will be critical. Furthermore, understanding how individuals' physiologic fatigue, circadian misalignment, mood, stress, and cognitive state may play into alarm response is likewise essential, if we are to design appropriate alarm systems that function effectively in the busy 24‐hour environment of healthcare. Ongoing work suggests that smart alarms, using algorithms that integrate data from multiple vital sign readings over time, may reduce the frequency of false alarms and better identify clinically significant events.[10] Replacing existing range‐limit monitors with these types of smart alarms has the potential to greatly improve both the sensitivity and specificity of hospital alarms, but further work in this area is needed.

Ultimately, if we can better separate out the signal, we will be better poised to respond to the true emergencies that arise that are currently obscured by the ever‐present noise. Better trust in the alarm systems we have would help all of us focus our energies on the problems that matter most. Doing so, we could better care for our patients, and better identify the system failures that cause them harm in our hospitals.

Disclosures: Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an Executive Council Member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety.

Despite 15 years of national and local investment in improving the safety of hospital care, patient safety remains a leading problem in both adult and pediatric hospitals. A 2010 study found that 180,000 Medicare beneficiaries likely die each year due to harm suffered as a result of medical care,[1] a death toll surpassed only by deaths due to cardiovascular disease and cancer. Even though initial efforts in the field have shown great promise for stemming the tide of healthcare‐associated infections,[2] surgical errors,[3] handoff failures,[4] and errors in the care of adults hospitalized for myocardial infarction and congestive heart failure,[5] much work remains to be done.[6] The root causes of many adverse events are poorly understood and unaddressed. Resultant tragedies remain all too common.

In the current issue of the Journal of Hospital Medicine, Bonafide and colleagues report the results of an innovative observational pilot study designed to assess the role of an inadequately addressed root cause of serious errors: alarm fatigue.[7] Alarm fatigue is the phenomenon of desensitization to alarms, particularly in the context of excessive false alarms. In a videotaped observational assessment of nurse response times to 5070 alarms on a pediatric ward and intensive care unit (ICU), the authors found that nurses responded significantly more slowly as the number of nonactionable alarms in the preceding 2 hours increased. Although a substantial majority of these alarms were technically valid (ie, representing true deviations of vital signs outside of the normal range rather than sensor or equipment problems), the vast majority required no action to be takenapproximately 7 out of 8 in the ICU and an astonishing 99 out of 100 on the ward.

As any hospitalist, intensivist, or nurse knows well, alarms are rampant throughout hospitals. It is impossible to walk down any hallway on a busy hospital wardnever mind an ICUwithout seeing a flashing light or 2 above a doorway, and hearing the incessant beeping of oxygen saturation and cardiovascular/respiratory monitors, a thousand bits of technology forever crying wolf. The problem, of course, is that sometimes there really is a wolf, but it is hard to take the risk seriously when the false alarms happen not just twice before a true threat materializes, as in Aesop's fable, but 7 times in the ICU, or worse, 99 times in the setting where most hospitalists practice. Moreover, even when the threat is real, in most cases it is caught in time one way or another, and no lasting harm results.

So why not simply shut off the unremitting noise? In 1987, outside of Baltimore, Amtrak experienced what at the time was the deadliest rail crash in its history after 1 of its passenger trains collided with a Conrail freight train. A major root cause of the crash was that the crew on the freight train had placed duct tape over an annoying automated signal alarm.[8, 9] Tragically, on this particular day, the suppressed alarm was all too relevant. Identifying the real alarm, however, can be nearly impossible when it sounds the same as 100 irritating sounds constantly emanating from the environment. It is the challenge of identifying the needle in the haystack, after you have developed an allergy to the hay.

What then to do? More research like that conducted by Bonafide and colleagues is needed to better understand how healthcare providers respond to the onslaught of alarms they encounter, and to inform refinement of these systems. Understanding how alarm fatigue plays out in the context of different clinical settings, with different workloads, varying levels of distraction, and different rates of true and false‐positive alarms will be critical. Furthermore, understanding how individuals' physiologic fatigue, circadian misalignment, mood, stress, and cognitive state may play into alarm response is likewise essential, if we are to design appropriate alarm systems that function effectively in the busy 24‐hour environment of healthcare. Ongoing work suggests that smart alarms, using algorithms that integrate data from multiple vital sign readings over time, may reduce the frequency of false alarms and better identify clinically significant events.[10] Replacing existing range‐limit monitors with these types of smart alarms has the potential to greatly improve both the sensitivity and specificity of hospital alarms, but further work in this area is needed.

Ultimately, if we can better separate out the signal, we will be better poised to respond to the true emergencies that arise that are currently obscured by the ever‐present noise. Better trust in the alarm systems we have would help all of us focus our energies on the problems that matter most. Doing so, we could better care for our patients, and better identify the system failures that cause them harm in our hospitals.

Disclosures: Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an Executive Council Member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety.

In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.

In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.

The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.

An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.

Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.

In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.

In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.

The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.

An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.

Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.

In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.

In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.

The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.

An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.

Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.

Functional impairment, such as difficulty with activities of daily living or limited mobility,[1] is common among hospitalized patients and correlated with important outcomes: approximately 50% of hospitalized Medicare seniors have some level of impairment that correlates with higher rates of readmission,[2] long‐term care placement,[3] and even death.[4]

Lack of consistent, accurate, and reliable data on functional mobility during hospitalization poses an important barrier for programs seeking to improve functional outcomes in hospitalized patients.[5, 6] More accurate mobility data could improve current hospital practices to diagnose mobility problems, target mobility interventions, and measure interventions' effectiveness. Although wearable mobility sensors (small, wireless accelerometers placed on patients' wrists, ankles, or waists) hold promise in overcoming these barriers and improving current practice, existing data are from small samples of focused populations and have not integrated sensor data into patient care.[7, 8]

In this issue of the Journal of Hospital Medicine, Sallis and colleagues used mobility sensors to study 777 hospitalized patients.[9] This article has several strengths that make it unique among the handful of articles in this area: it is the largest to date, the first to consider patients on both medical and surgical units, and the first to correlate sensor data with clinical assessments of mobility by providers (nurses). The authors found that, regardless of length of stay, patients averaged 1100 steps during the final 24 hours of their hospitalization. Older patients had slightly fewer steps on average (982 per 24 hours), but, taken collectively, these findings led the authors to postulate that 1000 steps per day might be a good normative value for discharge readiness in terms of patient mobility.

This idea of a normative value for steps taken by inpatients prior to discharge raises several interesting questions. First, could numbers of steps become a value that hospital providers routinely use to optimize care of hospitalized patients similar to other values such as blood pressure or blood sugar? Such a threshold could be used to define strategies that target tight mobility control for patients at high risk for decline, and others might be managed with a more traditional ad lib approach. Alternatively, perhaps physicians should focus more on improvement in mobility regardless of a population‐defined threshold. In this case, the measure would be progress toward a patient‐centered or patient‐defined goal. Second, it is important to note that Sallis and colleagues found that patients whose nurses documented their estimated mobility more frequently in the medical record also had substantially higher sensor step counts. This raises the question of whether more data from sensors can assist front‐line inpatient providers to more effectively engage patients in mobilizing to avoid functional deconditioning during hospitalization. Often we tell our patients to try to get out of bed todaygo for a walk around the unit, but we are rarely specific about how far they should walk, and patients do not get feedback on their daily progress toward a specific mobility goal. Perhaps data on the number of steps from mobility sensors could be shown to both patients and providers so as to encourage patients to reach their goal, whether that is the normative 1000 steps per day or slightly more or less.

This article also has limitations, which raise important questions for future research. First, patients in this study were ambulatory and relatively healthy (85% had Charlson scores 0 or 1) at the time of admission, making it difficult to determine whether the approach used or threshold defined are valid in higher‐risk populations, such as those with preexisting functional limitations. Second, lack of clinical outcomes data is another important limitation in this study, which is shared by many, but not all, inpatient sensor studies. For example, a recent study correlated discharge location (skilled nursing facility vs home) to levels of step mobility; however, the authors were unable to determine the degree to which their step measures were simply mirroring clinical decision making.[10] Another recent study demonstrated that decreased inpatient step counts are associated with early mortality; however, more proximal outcomes such as postdischarge function were not measured.[11] Moreover, future studies will need to assess whether mobility sensors can reliably predict postdischarge function, and even be used to improve mobility or reduce functional impairment in hospital populations that include sicker patients.

Ultimately, the results by Sallis et al. are a useful step in the right direction, but much more work is needed to determine the clinical utility of mobility sensors as part of larger efforts to harness the potential of mobile health (mHealth) efforts to improve care for hospitalized patients.[12] The future of mobility sensors in healthcare is likely about how well patients and providers can use them to successfully guide and support behavior change. This will require a strong health‐adopter focus in coaching patients to use mobility sensors and their mobile, patient‐facing applications.[13] Ultimately, the goal must be to embed these mHealth approaches into larger behavior management and health system redesign so that clinical goals such as improved function after hospital discharge are met.[14]

Functional impairment, such as difficulty with activities of daily living or limited mobility,[1] is common among hospitalized patients and correlated with important outcomes: approximately 50% of hospitalized Medicare seniors have some level of impairment that correlates with higher rates of readmission,[2] long‐term care placement,[3] and even death.[4]

Lack of consistent, accurate, and reliable data on functional mobility during hospitalization poses an important barrier for programs seeking to improve functional outcomes in hospitalized patients.[5, 6] More accurate mobility data could improve current hospital practices to diagnose mobility problems, target mobility interventions, and measure interventions' effectiveness. Although wearable mobility sensors (small, wireless accelerometers placed on patients' wrists, ankles, or waists) hold promise in overcoming these barriers and improving current practice, existing data are from small samples of focused populations and have not integrated sensor data into patient care.[7, 8]

In this issue of the Journal of Hospital Medicine, Sallis and colleagues used mobility sensors to study 777 hospitalized patients.[9] This article has several strengths that make it unique among the handful of articles in this area: it is the largest to date, the first to consider patients on both medical and surgical units, and the first to correlate sensor data with clinical assessments of mobility by providers (nurses). The authors found that, regardless of length of stay, patients averaged 1100 steps during the final 24 hours of their hospitalization. Older patients had slightly fewer steps on average (982 per 24 hours), but, taken collectively, these findings led the authors to postulate that 1000 steps per day might be a good normative value for discharge readiness in terms of patient mobility.

This idea of a normative value for steps taken by inpatients prior to discharge raises several interesting questions. First, could numbers of steps become a value that hospital providers routinely use to optimize care of hospitalized patients similar to other values such as blood pressure or blood sugar? Such a threshold could be used to define strategies that target tight mobility control for patients at high risk for decline, and others might be managed with a more traditional ad lib approach. Alternatively, perhaps physicians should focus more on improvement in mobility regardless of a population‐defined threshold. In this case, the measure would be progress toward a patient‐centered or patient‐defined goal. Second, it is important to note that Sallis and colleagues found that patients whose nurses documented their estimated mobility more frequently in the medical record also had substantially higher sensor step counts. This raises the question of whether more data from sensors can assist front‐line inpatient providers to more effectively engage patients in mobilizing to avoid functional deconditioning during hospitalization. Often we tell our patients to try to get out of bed todaygo for a walk around the unit, but we are rarely specific about how far they should walk, and patients do not get feedback on their daily progress toward a specific mobility goal. Perhaps data on the number of steps from mobility sensors could be shown to both patients and providers so as to encourage patients to reach their goal, whether that is the normative 1000 steps per day or slightly more or less.

This article also has limitations, which raise important questions for future research. First, patients in this study were ambulatory and relatively healthy (85% had Charlson scores 0 or 1) at the time of admission, making it difficult to determine whether the approach used or threshold defined are valid in higher‐risk populations, such as those with preexisting functional limitations. Second, lack of clinical outcomes data is another important limitation in this study, which is shared by many, but not all, inpatient sensor studies. For example, a recent study correlated discharge location (skilled nursing facility vs home) to levels of step mobility; however, the authors were unable to determine the degree to which their step measures were simply mirroring clinical decision making.[10] Another recent study demonstrated that decreased inpatient step counts are associated with early mortality; however, more proximal outcomes such as postdischarge function were not measured.[11] Moreover, future studies will need to assess whether mobility sensors can reliably predict postdischarge function, and even be used to improve mobility or reduce functional impairment in hospital populations that include sicker patients.

Ultimately, the results by Sallis et al. are a useful step in the right direction, but much more work is needed to determine the clinical utility of mobility sensors as part of larger efforts to harness the potential of mobile health (mHealth) efforts to improve care for hospitalized patients.[12] The future of mobility sensors in healthcare is likely about how well patients and providers can use them to successfully guide and support behavior change. This will require a strong health‐adopter focus in coaching patients to use mobility sensors and their mobile, patient‐facing applications.[13] Ultimately, the goal must be to embed these mHealth approaches into larger behavior management and health system redesign so that clinical goals such as improved function after hospital discharge are met.[14]

Functional impairment, such as difficulty with activities of daily living or limited mobility,[1] is common among hospitalized patients and correlated with important outcomes: approximately 50% of hospitalized Medicare seniors have some level of impairment that correlates with higher rates of readmission,[2] long‐term care placement,[3] and even death.[4]

Lack of consistent, accurate, and reliable data on functional mobility during hospitalization poses an important barrier for programs seeking to improve functional outcomes in hospitalized patients.[5, 6] More accurate mobility data could improve current hospital practices to diagnose mobility problems, target mobility interventions, and measure interventions' effectiveness. Although wearable mobility sensors (small, wireless accelerometers placed on patients' wrists, ankles, or waists) hold promise in overcoming these barriers and improving current practice, existing data are from small samples of focused populations and have not integrated sensor data into patient care.[7, 8]

In this issue of the Journal of Hospital Medicine, Sallis and colleagues used mobility sensors to study 777 hospitalized patients.[9] This article has several strengths that make it unique among the handful of articles in this area: it is the largest to date, the first to consider patients on both medical and surgical units, and the first to correlate sensor data with clinical assessments of mobility by providers (nurses). The authors found that, regardless of length of stay, patients averaged 1100 steps during the final 24 hours of their hospitalization. Older patients had slightly fewer steps on average (982 per 24 hours), but, taken collectively, these findings led the authors to postulate that 1000 steps per day might be a good normative value for discharge readiness in terms of patient mobility.

This idea of a normative value for steps taken by inpatients prior to discharge raises several interesting questions. First, could numbers of steps become a value that hospital providers routinely use to optimize care of hospitalized patients similar to other values such as blood pressure or blood sugar? Such a threshold could be used to define strategies that target tight mobility control for patients at high risk for decline, and others might be managed with a more traditional ad lib approach. Alternatively, perhaps physicians should focus more on improvement in mobility regardless of a population‐defined threshold. In this case, the measure would be progress toward a patient‐centered or patient‐defined goal. Second, it is important to note that Sallis and colleagues found that patients whose nurses documented their estimated mobility more frequently in the medical record also had substantially higher sensor step counts. This raises the question of whether more data from sensors can assist front‐line inpatient providers to more effectively engage patients in mobilizing to avoid functional deconditioning during hospitalization. Often we tell our patients to try to get out of bed todaygo for a walk around the unit, but we are rarely specific about how far they should walk, and patients do not get feedback on their daily progress toward a specific mobility goal. Perhaps data on the number of steps from mobility sensors could be shown to both patients and providers so as to encourage patients to reach their goal, whether that is the normative 1000 steps per day or slightly more or less.

This article also has limitations, which raise important questions for future research. First, patients in this study were ambulatory and relatively healthy (85% had Charlson scores 0 or 1) at the time of admission, making it difficult to determine whether the approach used or threshold defined are valid in higher‐risk populations, such as those with preexisting functional limitations. Second, lack of clinical outcomes data is another important limitation in this study, which is shared by many, but not all, inpatient sensor studies. For example, a recent study correlated discharge location (skilled nursing facility vs home) to levels of step mobility; however, the authors were unable to determine the degree to which their step measures were simply mirroring clinical decision making.[10] Another recent study demonstrated that decreased inpatient step counts are associated with early mortality; however, more proximal outcomes such as postdischarge function were not measured.[11] Moreover, future studies will need to assess whether mobility sensors can reliably predict postdischarge function, and even be used to improve mobility or reduce functional impairment in hospital populations that include sicker patients.

Ultimately, the results by Sallis et al. are a useful step in the right direction, but much more work is needed to determine the clinical utility of mobility sensors as part of larger efforts to harness the potential of mobile health (mHealth) efforts to improve care for hospitalized patients.[12] The future of mobility sensors in healthcare is likely about how well patients and providers can use them to successfully guide and support behavior change. This will require a strong health‐adopter focus in coaching patients to use mobility sensors and their mobile, patient‐facing applications.[13] Ultimately, the goal must be to embed these mHealth approaches into larger behavior management and health system redesign so that clinical goals such as improved function after hospital discharge are met.[14]

The theory behind rapid response teams (RRTs), namely to provide critical care resources to patients with clinical deterioration on the wards, is such common sense that failure to do so seems unethical. This idea, combined with evidence that many cardiac arrests on the wards are predictable and potentially preventable events, led to the proliferation of RRTs across the country and a Joint Commission mandate.[1] However, data from clinical trials have failed to consistently confirm the value of these teams, likely a product of the wide variability in implementation practices across institutions.[2]

In this issue of the Journal of Hospital Medicine, Davis and colleagues demonstrate improvements in both mortality and cardiac arrest rates outside the intensive care unit (ICU) following implementation of their rapid response system in 2 hospitals.[3] Although several other studies have shown similar results, what makes this implementation unique is the bundle approach that included proactive rounding by the charge nurse from each unit, annual focused training of team members and staff, and an integrated, continuous, quality‐improvement feedback loop. Bundles are common in successful quality‐improvement work, but can be challenging for deciphering which of the individual components are driving the results, leaving readers to venture an educated guess. In the current bundle, the novel use of the charge nurse has some significant appeal as a candidate primary driver of the impact, because it likely had 2 distinct actions: (1) proactive rounding and (2) promoting a culture change, both of which are well supported in the literature.^4,5

Several studies, including this one, have demonstrated a dose‐response association between the number of RRT activations and patient outcomes, with a low number of RRT activations deemed a major contributor to the neutral results of the large multicenter, randomized, controlled MERIT trial.[6, 7] Additionally, delays in treatment and transfer to the ICU for unstable patients are known to increase mortality.[8] One way to increase the number of patients seen by the RRT and decrease activation delays is by instituting proactive rounding by the team on high‐risk patients. This was the strategy employed in a landmark ward‐randomized trial by Priestley and colleagues, which demonstrated a significant improvement in mortality from proactive rounding on patients deemed to be at high risk of clinical deterioration as calculated by an early warning score or due to caregiver concern.⁴

Identification of at‐risk patients for proactive rounding can be accomplished with gestalt, as was done by the charge nurse in the current study, or using specific individual criteria such as recent discharge from an ICU. Alternatively, this can be accomplished using composite vital signbased risk scores, such as the Modified Early Warning Score (MEWS).[9] Recently, several newer algorithms that integrate vital signs, laboratory data, and demographics have been shown to outperform the MEWS.[10, 11] Such systems promise an exciting age of real‐time computer‐generated risk stratification, with the ability to automate and standardize the selection of patients for proactive rounding across institutions.

Interestingly, the selection of the charge nurse, rather than someone who did not reside on the unit, to conduct the surveillance rounds likely had another benefit: expediting and facilitating the culture change necessary for a successful implementation. The integration of the charge nurse into the RRT likely led to a local reinforcement of important cultural changes that were already happening at the institutional level. It is clear that culture change is essential in any quality improvement endeavor, and previous literature on RRTs supports this notion.[5]

Rapid response systems are complex and include the activation criteria, team composition and training, and an administrative component. A multifaceted, bundled approach is likely to be required for success. Furthermore, regardless of what risk stratification criteria are used, proactive rounding on high‐risk patients is likely to increase the yield. Utilizing the charge nurse in that effort is a creative use of a preexisting local resource and is worthy of future study.

Disclosures: Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080) and has received honoraria from CHEST for invited speaking engagements. Drs. Churpek and Edelson have a patent pending (ARCD.P0535US.P2) for risk stratification algorithms for hospitalized patients, and Dr. Edelson has an ownership interest in Quant HC (Chicago, IL), which seeks to commercialize those algorithms.

The theory behind rapid response teams (RRTs), namely to provide critical care resources to patients with clinical deterioration on the wards, is such common sense that failure to do so seems unethical. This idea, combined with evidence that many cardiac arrests on the wards are predictable and potentially preventable events, led to the proliferation of RRTs across the country and a Joint Commission mandate.[1] However, data from clinical trials have failed to consistently confirm the value of these teams, likely a product of the wide variability in implementation practices across institutions.[2]

In this issue of the Journal of Hospital Medicine, Davis and colleagues demonstrate improvements in both mortality and cardiac arrest rates outside the intensive care unit (ICU) following implementation of their rapid response system in 2 hospitals.[3] Although several other studies have shown similar results, what makes this implementation unique is the bundle approach that included proactive rounding by the charge nurse from each unit, annual focused training of team members and staff, and an integrated, continuous, quality‐improvement feedback loop. Bundles are common in successful quality‐improvement work, but can be challenging for deciphering which of the individual components are driving the results, leaving readers to venture an educated guess. In the current bundle, the novel use of the charge nurse has some significant appeal as a candidate primary driver of the impact, because it likely had 2 distinct actions: (1) proactive rounding and (2) promoting a culture change, both of which are well supported in the literature.^4,5

Several studies, including this one, have demonstrated a dose‐response association between the number of RRT activations and patient outcomes, with a low number of RRT activations deemed a major contributor to the neutral results of the large multicenter, randomized, controlled MERIT trial.[6, 7] Additionally, delays in treatment and transfer to the ICU for unstable patients are known to increase mortality.[8] One way to increase the number of patients seen by the RRT and decrease activation delays is by instituting proactive rounding by the team on high‐risk patients. This was the strategy employed in a landmark ward‐randomized trial by Priestley and colleagues, which demonstrated a significant improvement in mortality from proactive rounding on patients deemed to be at high risk of clinical deterioration as calculated by an early warning score or due to caregiver concern.⁴

Identification of at‐risk patients for proactive rounding can be accomplished with gestalt, as was done by the charge nurse in the current study, or using specific individual criteria such as recent discharge from an ICU. Alternatively, this can be accomplished using composite vital signbased risk scores, such as the Modified Early Warning Score (MEWS).[9] Recently, several newer algorithms that integrate vital signs, laboratory data, and demographics have been shown to outperform the MEWS.[10, 11] Such systems promise an exciting age of real‐time computer‐generated risk stratification, with the ability to automate and standardize the selection of patients for proactive rounding across institutions.

Interestingly, the selection of the charge nurse, rather than someone who did not reside on the unit, to conduct the surveillance rounds likely had another benefit: expediting and facilitating the culture change necessary for a successful implementation. The integration of the charge nurse into the RRT likely led to a local reinforcement of important cultural changes that were already happening at the institutional level. It is clear that culture change is essential in any quality improvement endeavor, and previous literature on RRTs supports this notion.[5]

Rapid response systems are complex and include the activation criteria, team composition and training, and an administrative component. A multifaceted, bundled approach is likely to be required for success. Furthermore, regardless of what risk stratification criteria are used, proactive rounding on high‐risk patients is likely to increase the yield. Utilizing the charge nurse in that effort is a creative use of a preexisting local resource and is worthy of future study.

Disclosures: Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080) and has received honoraria from CHEST for invited speaking engagements. Drs. Churpek and Edelson have a patent pending (ARCD.P0535US.P2) for risk stratification algorithms for hospitalized patients, and Dr. Edelson has an ownership interest in Quant HC (Chicago, IL), which seeks to commercialize those algorithms.

The theory behind rapid response teams (RRTs), namely to provide critical care resources to patients with clinical deterioration on the wards, is such common sense that failure to do so seems unethical. This idea, combined with evidence that many cardiac arrests on the wards are predictable and potentially preventable events, led to the proliferation of RRTs across the country and a Joint Commission mandate.[1] However, data from clinical trials have failed to consistently confirm the value of these teams, likely a product of the wide variability in implementation practices across institutions.[2]

In this issue of the Journal of Hospital Medicine, Davis and colleagues demonstrate improvements in both mortality and cardiac arrest rates outside the intensive care unit (ICU) following implementation of their rapid response system in 2 hospitals.[3] Although several other studies have shown similar results, what makes this implementation unique is the bundle approach that included proactive rounding by the charge nurse from each unit, annual focused training of team members and staff, and an integrated, continuous, quality‐improvement feedback loop. Bundles are common in successful quality‐improvement work, but can be challenging for deciphering which of the individual components are driving the results, leaving readers to venture an educated guess. In the current bundle, the novel use of the charge nurse has some significant appeal as a candidate primary driver of the impact, because it likely had 2 distinct actions: (1) proactive rounding and (2) promoting a culture change, both of which are well supported in the literature.^4,5

Several studies, including this one, have demonstrated a dose‐response association between the number of RRT activations and patient outcomes, with a low number of RRT activations deemed a major contributor to the neutral results of the large multicenter, randomized, controlled MERIT trial.[6, 7] Additionally, delays in treatment and transfer to the ICU for unstable patients are known to increase mortality.[8] One way to increase the number of patients seen by the RRT and decrease activation delays is by instituting proactive rounding by the team on high‐risk patients. This was the strategy employed in a landmark ward‐randomized trial by Priestley and colleagues, which demonstrated a significant improvement in mortality from proactive rounding on patients deemed to be at high risk of clinical deterioration as calculated by an early warning score or due to caregiver concern.⁴

Identification of at‐risk patients for proactive rounding can be accomplished with gestalt, as was done by the charge nurse in the current study, or using specific individual criteria such as recent discharge from an ICU. Alternatively, this can be accomplished using composite vital signbased risk scores, such as the Modified Early Warning Score (MEWS).[9] Recently, several newer algorithms that integrate vital signs, laboratory data, and demographics have been shown to outperform the MEWS.[10, 11] Such systems promise an exciting age of real‐time computer‐generated risk stratification, with the ability to automate and standardize the selection of patients for proactive rounding across institutions.

Interestingly, the selection of the charge nurse, rather than someone who did not reside on the unit, to conduct the surveillance rounds likely had another benefit: expediting and facilitating the culture change necessary for a successful implementation. The integration of the charge nurse into the RRT likely led to a local reinforcement of important cultural changes that were already happening at the institutional level. It is clear that culture change is essential in any quality improvement endeavor, and previous literature on RRTs supports this notion.[5]

Rapid response systems are complex and include the activation criteria, team composition and training, and an administrative component. A multifaceted, bundled approach is likely to be required for success. Furthermore, regardless of what risk stratification criteria are used, proactive rounding on high‐risk patients is likely to increase the yield. Utilizing the charge nurse in that effort is a creative use of a preexisting local resource and is worthy of future study.

Disclosures: Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080) and has received honoraria from CHEST for invited speaking engagements. Drs. Churpek and Edelson have a patent pending (ARCD.P0535US.P2) for risk stratification algorithms for hospitalized patients, and Dr. Edelson has an ownership interest in Quant HC (Chicago, IL), which seeks to commercialize those algorithms.

In this issue of the Journal of Hospital Medicine, the results of 2 inpatient handoff studies further shape our evolving understanding of in‐hospital care transitions. Schouten and colleagues,[1] report no difference in adverse outcomes when admissions were handed off to the primary team using face‐to‐face compared to nonface‐to‐face interactions. Meanwhile, Hanson and colleagues[2] report that a written handoff tool is used infrequently by covering interns.

Schouten et al.'s study attempted to isolate the impact of the verbal portion of the handoff between admitting and accepting team by evaluating whether early adverse outcomes differed between patients whose teams performed a face‐to‐face handoffs compared to those who did not. Their study was a retrospective chart review, and no additional process changes, training, or instruction regarding handoffs were implemented or measured. Handoffs occurred primarily between advanced practice providers, hospitalists, and a small number of resident physicians, so generalizability of this study to other institutions may be limited. No difference in adverse events was noted between admissions with face‐to‐face compared to those without face‐to‐face handoffs (2.6% vs 3.2%). Unfortunately, this study was likely underpowered to detect significant changes in adverse events, with a sample size of 805 total patients with a 3% baseline rate of adverse events (by our estimate, over 5000 patients would be needed in each group10,000 overallto detect a 30% relative difference in event rates). Further, this study did not examine other outcomes that could be impacted by the handoff process such as provider efficiency or patient experience.

Face‐to‐face handoffs, the gold standard for handoffs between providers, was 1 of the sign‐out approaches examined in a study by Graham and colleagues.[3] This study, in contrast to the Schouten et al. study, prospectively evaluated adverse events before and after implementation of face‐to‐face handoffs, with structured written sign‐out from the primary team to nighttime covering physicians. Prior to implementation, handoffs consisted of a double handoff involving an intermediary physician and unstructured written sign‐out. Although no statistically significant reduction in adverse events was found in the Graham et al. study, significant improvements were noted in physician satisfaction, documentation of key elements in handoffs, and reduced data omissions; importantly, a trend of fewer near misses was noted comparing the pre‐ and postintervention periods. Although the Schouten et al. and Graham et al. studies suggest questionable benefit of face‐to‐face handoffs, we would caution that limitations in sample size and methodological sensitivity to detect adverse events in both studies could explain the lack of association between face‐to‐face handoffs and reduced adverse events. Furthermore, the promising findings of fewer data omissions and near misses in the intervention group in the Graham et al. study suggest benefit from a multipronged approach to improving handoffs including both face‐to‐face interactions and a structured written component.

In this issue, Hanson and colleagues also evaluated the use of a handoff tool by cross‐covering interns in a convenience sample of overnight clinical interactions. Despite finding that standard written documentation was considered beneficial by nearly all respondents (94.3%), the interns reported that the handoff tool was used in only 27.7% of encounters. This pales in comparison to the use of the nurse or chart in 94.4% of cross‐coverage encounters. The authors speculate that a handoff tool, for many years the only timely source of information, may not be as useful when information can be easily accessed in an electronic health record. Yet, in a prior systematic review that included 6 studies of computerized handoff tools, Li and colleagues found that computerized handoff tools may improve physician efficiency, enhance the completeness of handoff information, and even potentially reduce adverse events.[4]

The Schouten et al. and Hanson et al. studies raise important questions for the fields of hospital medicine and patient safety. Is it time to do away with the written and verbal portions of the handoff process? Should the handoff of patients simply consist of transferring a list of patients to covering providers? We do not believe this is the correct course of action. Rather, we recommend a more evolutionary, not revolutionary, interpretation of these results, especially when considered as part of a broader story of in‐hospital transitions of care.

For example, a recently published evaluation of a resident handoff‐improvement program in 9 hospitals and 10,740 patient admissions by Starmer and collegues[5] focused on a handoff bundle, I‐PASS, which is a pneumonic for Illness severity, Patient summary, Action items, Situation awareness and contingency planning, Synthesis by receiver. The authors report a reduction in medical errors and preventable adverse events without significant increases in the duration of oral handoff per patient. The handoff in this study included both oral and written elements in the I‐PASS format. Implementation was multipronged, and the I‐PASS bundle included (1) use of the I‐PASS pneumonic to standardize handoffs; (2) resident physician training in handoffs and communication through a 2‐hour workshop, followed by a 1‐hour role‐playing and simulation session, and a computer module for practice; (3) faculty development and observation with use of direct‐observation tools to provide structured feedback to residents; (4) active surveillance for errors (rather than relying on self‐report); and (5) a sustainability campaign to promote continuation of culture change. The complexity and robust nature of the I‐PASS handoff bundle suggests that having multiple structured components included in a handoff program with active, rather than retrospective, evaluation might increase the likelihood of improved, sustained outcomes. In addition, one might also conclude from the Starmer et al. study that it takes commitment from all levels, including residents, faculty, and administration, to improve handoffs between teams for inpatient care.

We commend Schouten et al. and Hanson et al. on their contributions to the literature, but believe that the story of the in‐hospital handoff has yet to be fully written. Although results from these 2 articles may cause speculation about the value of oral and written handoffs, we believe that the balance of evidence favors the use of a multipronged approach that involves both structured oral and written handoffs to improve the value and efficiency of handoffs. In addition, findings from the I‐PASS study support dedicated handoff training for providers, evaluation of handoffs using structured tools, and active surveillance for medical errors. Future areas of work should include a systematic review of the inpatient handoff literature and further evaluation of precisely which specific intervention components (eg, structured content of handoffs, sensemaking content) or modes of delivery (eg, face‐to‐face vs other) are most likely to reduce medical errors and improve patient outcomes. As the hospital medicine movement continues to grow, handoffs will continue to be paramount. Establishing the safest method to complete handoffs to promote patient safety should be a common goal for hospitalists.

The handoff story is still in evolution; as hospitalists, we are poised to be its author.

In this issue of the Journal of Hospital Medicine, the results of 2 inpatient handoff studies further shape our evolving understanding of in‐hospital care transitions. Schouten and colleagues,[1] report no difference in adverse outcomes when admissions were handed off to the primary team using face‐to‐face compared to nonface‐to‐face interactions. Meanwhile, Hanson and colleagues[2] report that a written handoff tool is used infrequently by covering interns.

Schouten et al.'s study attempted to isolate the impact of the verbal portion of the handoff between admitting and accepting team by evaluating whether early adverse outcomes differed between patients whose teams performed a face‐to‐face handoffs compared to those who did not. Their study was a retrospective chart review, and no additional process changes, training, or instruction regarding handoffs were implemented or measured. Handoffs occurred primarily between advanced practice providers, hospitalists, and a small number of resident physicians, so generalizability of this study to other institutions may be limited. No difference in adverse events was noted between admissions with face‐to‐face compared to those without face‐to‐face handoffs (2.6% vs 3.2%). Unfortunately, this study was likely underpowered to detect significant changes in adverse events, with a sample size of 805 total patients with a 3% baseline rate of adverse events (by our estimate, over 5000 patients would be needed in each group10,000 overallto detect a 30% relative difference in event rates). Further, this study did not examine other outcomes that could be impacted by the handoff process such as provider efficiency or patient experience.

Face‐to‐face handoffs, the gold standard for handoffs between providers, was 1 of the sign‐out approaches examined in a study by Graham and colleagues.[3] This study, in contrast to the Schouten et al. study, prospectively evaluated adverse events before and after implementation of face‐to‐face handoffs, with structured written sign‐out from the primary team to nighttime covering physicians. Prior to implementation, handoffs consisted of a double handoff involving an intermediary physician and unstructured written sign‐out. Although no statistically significant reduction in adverse events was found in the Graham et al. study, significant improvements were noted in physician satisfaction, documentation of key elements in handoffs, and reduced data omissions; importantly, a trend of fewer near misses was noted comparing the pre‐ and postintervention periods. Although the Schouten et al. and Graham et al. studies suggest questionable benefit of face‐to‐face handoffs, we would caution that limitations in sample size and methodological sensitivity to detect adverse events in both studies could explain the lack of association between face‐to‐face handoffs and reduced adverse events. Furthermore, the promising findings of fewer data omissions and near misses in the intervention group in the Graham et al. study suggest benefit from a multipronged approach to improving handoffs including both face‐to‐face interactions and a structured written component.

In this issue, Hanson and colleagues also evaluated the use of a handoff tool by cross‐covering interns in a convenience sample of overnight clinical interactions. Despite finding that standard written documentation was considered beneficial by nearly all respondents (94.3%), the interns reported that the handoff tool was used in only 27.7% of encounters. This pales in comparison to the use of the nurse or chart in 94.4% of cross‐coverage encounters. The authors speculate that a handoff tool, for many years the only timely source of information, may not be as useful when information can be easily accessed in an electronic health record. Yet, in a prior systematic review that included 6 studies of computerized handoff tools, Li and colleagues found that computerized handoff tools may improve physician efficiency, enhance the completeness of handoff information, and even potentially reduce adverse events.[4]

The Schouten et al. and Hanson et al. studies raise important questions for the fields of hospital medicine and patient safety. Is it time to do away with the written and verbal portions of the handoff process? Should the handoff of patients simply consist of transferring a list of patients to covering providers? We do not believe this is the correct course of action. Rather, we recommend a more evolutionary, not revolutionary, interpretation of these results, especially when considered as part of a broader story of in‐hospital transitions of care.

For example, a recently published evaluation of a resident handoff‐improvement program in 9 hospitals and 10,740 patient admissions by Starmer and collegues[5] focused on a handoff bundle, I‐PASS, which is a pneumonic for Illness severity, Patient summary, Action items, Situation awareness and contingency planning, Synthesis by receiver. The authors report a reduction in medical errors and preventable adverse events without significant increases in the duration of oral handoff per patient. The handoff in this study included both oral and written elements in the I‐PASS format. Implementation was multipronged, and the I‐PASS bundle included (1) use of the I‐PASS pneumonic to standardize handoffs; (2) resident physician training in handoffs and communication through a 2‐hour workshop, followed by a 1‐hour role‐playing and simulation session, and a computer module for practice; (3) faculty development and observation with use of direct‐observation tools to provide structured feedback to residents; (4) active surveillance for errors (rather than relying on self‐report); and (5) a sustainability campaign to promote continuation of culture change. The complexity and robust nature of the I‐PASS handoff bundle suggests that having multiple structured components included in a handoff program with active, rather than retrospective, evaluation might increase the likelihood of improved, sustained outcomes. In addition, one might also conclude from the Starmer et al. study that it takes commitment from all levels, including residents, faculty, and administration, to improve handoffs between teams for inpatient care.

We commend Schouten et al. and Hanson et al. on their contributions to the literature, but believe that the story of the in‐hospital handoff has yet to be fully written. Although results from these 2 articles may cause speculation about the value of oral and written handoffs, we believe that the balance of evidence favors the use of a multipronged approach that involves both structured oral and written handoffs to improve the value and efficiency of handoffs. In addition, findings from the I‐PASS study support dedicated handoff training for providers, evaluation of handoffs using structured tools, and active surveillance for medical errors. Future areas of work should include a systematic review of the inpatient handoff literature and further evaluation of precisely which specific intervention components (eg, structured content of handoffs, sensemaking content) or modes of delivery (eg, face‐to‐face vs other) are most likely to reduce medical errors and improve patient outcomes. As the hospital medicine movement continues to grow, handoffs will continue to be paramount. Establishing the safest method to complete handoffs to promote patient safety should be a common goal for hospitalists.

The handoff story is still in evolution; as hospitalists, we are poised to be its author.

In this issue of the Journal of Hospital Medicine, the results of 2 inpatient handoff studies further shape our evolving understanding of in‐hospital care transitions. Schouten and colleagues,[1] report no difference in adverse outcomes when admissions were handed off to the primary team using face‐to‐face compared to nonface‐to‐face interactions. Meanwhile, Hanson and colleagues[2] report that a written handoff tool is used infrequently by covering interns.

Schouten et al.'s study attempted to isolate the impact of the verbal portion of the handoff between admitting and accepting team by evaluating whether early adverse outcomes differed between patients whose teams performed a face‐to‐face handoffs compared to those who did not. Their study was a retrospective chart review, and no additional process changes, training, or instruction regarding handoffs were implemented or measured. Handoffs occurred primarily between advanced practice providers, hospitalists, and a small number of resident physicians, so generalizability of this study to other institutions may be limited. No difference in adverse events was noted between admissions with face‐to‐face compared to those without face‐to‐face handoffs (2.6% vs 3.2%). Unfortunately, this study was likely underpowered to detect significant changes in adverse events, with a sample size of 805 total patients with a 3% baseline rate of adverse events (by our estimate, over 5000 patients would be needed in each group10,000 overallto detect a 30% relative difference in event rates). Further, this study did not examine other outcomes that could be impacted by the handoff process such as provider efficiency or patient experience.

Face‐to‐face handoffs, the gold standard for handoffs between providers, was 1 of the sign‐out approaches examined in a study by Graham and colleagues.[3] This study, in contrast to the Schouten et al. study, prospectively evaluated adverse events before and after implementation of face‐to‐face handoffs, with structured written sign‐out from the primary team to nighttime covering physicians. Prior to implementation, handoffs consisted of a double handoff involving an intermediary physician and unstructured written sign‐out. Although no statistically significant reduction in adverse events was found in the Graham et al. study, significant improvements were noted in physician satisfaction, documentation of key elements in handoffs, and reduced data omissions; importantly, a trend of fewer near misses was noted comparing the pre‐ and postintervention periods. Although the Schouten et al. and Graham et al. studies suggest questionable benefit of face‐to‐face handoffs, we would caution that limitations in sample size and methodological sensitivity to detect adverse events in both studies could explain the lack of association between face‐to‐face handoffs and reduced adverse events. Furthermore, the promising findings of fewer data omissions and near misses in the intervention group in the Graham et al. study suggest benefit from a multipronged approach to improving handoffs including both face‐to‐face interactions and a structured written component.

In this issue, Hanson and colleagues also evaluated the use of a handoff tool by cross‐covering interns in a convenience sample of overnight clinical interactions. Despite finding that standard written documentation was considered beneficial by nearly all respondents (94.3%), the interns reported that the handoff tool was used in only 27.7% of encounters. This pales in comparison to the use of the nurse or chart in 94.4% of cross‐coverage encounters. The authors speculate that a handoff tool, for many years the only timely source of information, may not be as useful when information can be easily accessed in an electronic health record. Yet, in a prior systematic review that included 6 studies of computerized handoff tools, Li and colleagues found that computerized handoff tools may improve physician efficiency, enhance the completeness of handoff information, and even potentially reduce adverse events.[4]

The Schouten et al. and Hanson et al. studies raise important questions for the fields of hospital medicine and patient safety. Is it time to do away with the written and verbal portions of the handoff process? Should the handoff of patients simply consist of transferring a list of patients to covering providers? We do not believe this is the correct course of action. Rather, we recommend a more evolutionary, not revolutionary, interpretation of these results, especially when considered as part of a broader story of in‐hospital transitions of care.

For example, a recently published evaluation of a resident handoff‐improvement program in 9 hospitals and 10,740 patient admissions by Starmer and collegues[5] focused on a handoff bundle, I‐PASS, which is a pneumonic for Illness severity, Patient summary, Action items, Situation awareness and contingency planning, Synthesis by receiver. The authors report a reduction in medical errors and preventable adverse events without significant increases in the duration of oral handoff per patient. The handoff in this study included both oral and written elements in the I‐PASS format. Implementation was multipronged, and the I‐PASS bundle included (1) use of the I‐PASS pneumonic to standardize handoffs; (2) resident physician training in handoffs and communication through a 2‐hour workshop, followed by a 1‐hour role‐playing and simulation session, and a computer module for practice; (3) faculty development and observation with use of direct‐observation tools to provide structured feedback to residents; (4) active surveillance for errors (rather than relying on self‐report); and (5) a sustainability campaign to promote continuation of culture change. The complexity and robust nature of the I‐PASS handoff bundle suggests that having multiple structured components included in a handoff program with active, rather than retrospective, evaluation might increase the likelihood of improved, sustained outcomes. In addition, one might also conclude from the Starmer et al. study that it takes commitment from all levels, including residents, faculty, and administration, to improve handoffs between teams for inpatient care.

We commend Schouten et al. and Hanson et al. on their contributions to the literature, but believe that the story of the in‐hospital handoff has yet to be fully written. Although results from these 2 articles may cause speculation about the value of oral and written handoffs, we believe that the balance of evidence favors the use of a multipronged approach that involves both structured oral and written handoffs to improve the value and efficiency of handoffs. In addition, findings from the I‐PASS study support dedicated handoff training for providers, evaluation of handoffs using structured tools, and active surveillance for medical errors. Future areas of work should include a systematic review of the inpatient handoff literature and further evaluation of precisely which specific intervention components (eg, structured content of handoffs, sensemaking content) or modes of delivery (eg, face‐to‐face vs other) are most likely to reduce medical errors and improve patient outcomes. As the hospital medicine movement continues to grow, handoffs will continue to be paramount. Establishing the safest method to complete handoffs to promote patient safety should be a common goal for hospitalists.

The handoff story is still in evolution; as hospitalists, we are poised to be its author.

Much has been published in the academic literature and lay press regarding rising healthcare costs.[1] As the nations' largest payer, the Centers for Medicaid and Medicare Services (CMS) have been aggressive in trying to decrease Medicare expenditures. Each year Medicare processes over 1 billion claims, submitted by over 1 million healthcare providers. Starting in 2005, demonstration projects supported by the CMS identified more than $1.03 billion in improper Medicare payments.[2] Subsequently, section 1893(h) of the Affordable Care Act authorized expansion of the Recovery Audit Program nationwide by January 2010. Facilitated by third‐party vendors paid on a contingency fee basis, known as the Recovery Audit Contractors (RACs), the stated objective of the program is to identify and correct improper payments, not only identify overpayments to healthcare providers and organization, but also underpayments, in addition to reporting common billing errors, trends, and other Medicare payment issues to CMS. [2] Although CMS does have a prepayment review program,[3] much of the reported RAC activities to date have been focused on postbill overpayment activities. In 2013 (the most recent reported annual activity period), CMS reported that collectively the RACs identified and corrected 1,532,249 claims for improper payments, collected $3.65 billion in overpayments, and identified $102.4 million in underpayments that were repaid to providers and suppliers.

Sheehy et al., present the collective experience of 3 large academic medical centers with RAC audit activity.[4] They found that from 2010 to 2013, there has been a 3‐fold increase in RAC‐related activities. The RACs are selected by CMS via a competitive bidding process and are contractually incentivize via a contingency fee. This means that they receive a portion of the funds that they recover (anywhere from 9%12% depending on the contract). If the RAC's claim is overturned on appeal, the RAC must repay the contingency fee, but does not face an economic penalty. This creates a potential incentive for RACs to be overly aggressive in pursuing potential overpayments from hospitals and providers.

The institutions in this study disputed 91% of allegations of overpayment. This dispute rate is notably higher than the 50% that was reported by a survey conducted by the American Hospital Association.[5] What is unknown is what the actual rate of overturned decisions based on appeal would be, as 49% of all contested claims from the study institutions were withdrawn and rebilled, and did not go through the complete appeals process. The authors cite the lengthy and presumably expensive process of adjudication as the reason for the decision to rebill the claims at the typically lower payment levels available under Medicare Part B. A 2012 report by the Office of the Inspector General (OIG) found that most (72%) of RAC‐denied hospital inpatient claims were overturned on appeal, in favor of the hospital by an administrative law judge (ALJ). This high rate of turnover has initiated a national discussion about the unbalanced financial incentives of the process per current design.

Since 2009, there has been a 10‐fold increase in the number of appeals waiting for a decision, with hearing delays reported to be as long as 32 months.[5, 6] The ALJ is required to issue a decision within 90 days of an appeal request. However, despite the huge volume of audits and secondary appeals generated by the RAC process, CMS has done little to expand the appeal infrastructure and the ALJ resources to keep pace with the incentivized RAC contractors.

The ALJ appeal backlog became so substantial that the Office of Medicare Hearings and Appeals published the following statement: As noted in a Federal Register Notice released by the Office of Medicare Hearings and Appeals (OMHA) in January 2014, the unprecedented growth in claim appeals continues to exceed the available adjudication resources to address [such] appeals.The CMS supports OMHA's efforts to bring efficiencies to the OMHA appeals process. Ultimately CMS offered hospitals a blanket 68% settlement for outstanding appeals to simply settle the backlogged cases.[7]

Finally, the authors note that an average of 5 full‐time equivalents (FTEs) was required by each institution to support the compliance‐related activities, which the authors claim is onerous and expensive. Their experience is consistent with other national reports that have found that 69% of surveyed hospitals report spending more than $40,000 per year, whereas 11% spend more than $100,000 annually.[5]

Ultimately, the authors conclude that reform is needed. Nationally many have agreed. As such, based on feedback, the CMS announced changes to the RAC program in December 2014[8] including: (1) reduction of the RAC look back period to 6 months (vs 3 years) from the date of service for payment adjustments, (2) RAC review period decreases to 30 days (vs 60 days), (3) addition of a 30‐day discussion period for claims, (4) the RAC will not receive a contingency fee until the second level review is completed, (5) broadened scope beyond inpatient claims (eg, review of outpatient claims), (6) more transparency regarding the appeals process, (7) new requirements for RACs to maintain a <10% overturn rate at the first‐level review (if not met, the RAC will be placed on a corrective action plan), and (8) RACs are now required to maintain an overall accuracy rate of 95%. In addition, CMS must publically report through an annual Report to Congress a Recovery Auditor accuracy rate for each Recovery Auditor.[9] There is no doubt that the current RAC program has generated significant savings for CMS. However, it has resulted in a notable cost and administrative burden to others including hospitals and provider groups. With the implementation of measures that hold RACs more accountable for the quality of their reviews, it is unclear if these new reform measures proposed by CMS will substantially improve the postpayment refinement process. Only with continued, but expensive, vigilance by providers and hospitals to ensure that claims are accurately processed as was described by the study institutions by Sheehy et al.,[4] will we know the potential value of the postpayment system.

Much has been published in the academic literature and lay press regarding rising healthcare costs.[1] As the nations' largest payer, the Centers for Medicaid and Medicare Services (CMS) have been aggressive in trying to decrease Medicare expenditures. Each year Medicare processes over 1 billion claims, submitted by over 1 million healthcare providers. Starting in 2005, demonstration projects supported by the CMS identified more than $1.03 billion in improper Medicare payments.[2] Subsequently, section 1893(h) of the Affordable Care Act authorized expansion of the Recovery Audit Program nationwide by January 2010. Facilitated by third‐party vendors paid on a contingency fee basis, known as the Recovery Audit Contractors (RACs), the stated objective of the program is to identify and correct improper payments, not only identify overpayments to healthcare providers and organization, but also underpayments, in addition to reporting common billing errors, trends, and other Medicare payment issues to CMS. [2] Although CMS does have a prepayment review program,[3] much of the reported RAC activities to date have been focused on postbill overpayment activities. In 2013 (the most recent reported annual activity period), CMS reported that collectively the RACs identified and corrected 1,532,249 claims for improper payments, collected $3.65 billion in overpayments, and identified $102.4 million in underpayments that were repaid to providers and suppliers.

Sheehy et al., present the collective experience of 3 large academic medical centers with RAC audit activity.[4] They found that from 2010 to 2013, there has been a 3‐fold increase in RAC‐related activities. The RACs are selected by CMS via a competitive bidding process and are contractually incentivize via a contingency fee. This means that they receive a portion of the funds that they recover (anywhere from 9%12% depending on the contract). If the RAC's claim is overturned on appeal, the RAC must repay the contingency fee, but does not face an economic penalty. This creates a potential incentive for RACs to be overly aggressive in pursuing potential overpayments from hospitals and providers.

The institutions in this study disputed 91% of allegations of overpayment. This dispute rate is notably higher than the 50% that was reported by a survey conducted by the American Hospital Association.[5] What is unknown is what the actual rate of overturned decisions based on appeal would be, as 49% of all contested claims from the study institutions were withdrawn and rebilled, and did not go through the complete appeals process. The authors cite the lengthy and presumably expensive process of adjudication as the reason for the decision to rebill the claims at the typically lower payment levels available under Medicare Part B. A 2012 report by the Office of the Inspector General (OIG) found that most (72%) of RAC‐denied hospital inpatient claims were overturned on appeal, in favor of the hospital by an administrative law judge (ALJ). This high rate of turnover has initiated a national discussion about the unbalanced financial incentives of the process per current design.

Since 2009, there has been a 10‐fold increase in the number of appeals waiting for a decision, with hearing delays reported to be as long as 32 months.[5, 6] The ALJ is required to issue a decision within 90 days of an appeal request. However, despite the huge volume of audits and secondary appeals generated by the RAC process, CMS has done little to expand the appeal infrastructure and the ALJ resources to keep pace with the incentivized RAC contractors.

The ALJ appeal backlog became so substantial that the Office of Medicare Hearings and Appeals published the following statement: As noted in a Federal Register Notice released by the Office of Medicare Hearings and Appeals (OMHA) in January 2014, the unprecedented growth in claim appeals continues to exceed the available adjudication resources to address [such] appeals.The CMS supports OMHA's efforts to bring efficiencies to the OMHA appeals process. Ultimately CMS offered hospitals a blanket 68% settlement for outstanding appeals to simply settle the backlogged cases.[7]

Finally, the authors note that an average of 5 full‐time equivalents (FTEs) was required by each institution to support the compliance‐related activities, which the authors claim is onerous and expensive. Their experience is consistent with other national reports that have found that 69% of surveyed hospitals report spending more than $40,000 per year, whereas 11% spend more than $100,000 annually.[5]

Ultimately, the authors conclude that reform is needed. Nationally many have agreed. As such, based on feedback, the CMS announced changes to the RAC program in December 2014[8] including: (1) reduction of the RAC look back period to 6 months (vs 3 years) from the date of service for payment adjustments, (2) RAC review period decreases to 30 days (vs 60 days), (3) addition of a 30‐day discussion period for claims, (4) the RAC will not receive a contingency fee until the second level review is completed, (5) broadened scope beyond inpatient claims (eg, review of outpatient claims), (6) more transparency regarding the appeals process, (7) new requirements for RACs to maintain a <10% overturn rate at the first‐level review (if not met, the RAC will be placed on a corrective action plan), and (8) RACs are now required to maintain an overall accuracy rate of 95%. In addition, CMS must publically report through an annual Report to Congress a Recovery Auditor accuracy rate for each Recovery Auditor.[9] There is no doubt that the current RAC program has generated significant savings for CMS. However, it has resulted in a notable cost and administrative burden to others including hospitals and provider groups. With the implementation of measures that hold RACs more accountable for the quality of their reviews, it is unclear if these new reform measures proposed by CMS will substantially improve the postpayment refinement process. Only with continued, but expensive, vigilance by providers and hospitals to ensure that claims are accurately processed as was described by the study institutions by Sheehy et al.,[4] will we know the potential value of the postpayment system.

Much has been published in the academic literature and lay press regarding rising healthcare costs.[1] As the nations' largest payer, the Centers for Medicaid and Medicare Services (CMS) have been aggressive in trying to decrease Medicare expenditures. Each year Medicare processes over 1 billion claims, submitted by over 1 million healthcare providers. Starting in 2005, demonstration projects supported by the CMS identified more than $1.03 billion in improper Medicare payments.[2] Subsequently, section 1893(h) of the Affordable Care Act authorized expansion of the Recovery Audit Program nationwide by January 2010. Facilitated by third‐party vendors paid on a contingency fee basis, known as the Recovery Audit Contractors (RACs), the stated objective of the program is to identify and correct improper payments, not only identify overpayments to healthcare providers and organization, but also underpayments, in addition to reporting common billing errors, trends, and other Medicare payment issues to CMS. [2] Although CMS does have a prepayment review program,[3] much of the reported RAC activities to date have been focused on postbill overpayment activities. In 2013 (the most recent reported annual activity period), CMS reported that collectively the RACs identified and corrected 1,532,249 claims for improper payments, collected $3.65 billion in overpayments, and identified $102.4 million in underpayments that were repaid to providers and suppliers.

Sheehy et al., present the collective experience of 3 large academic medical centers with RAC audit activity.[4] They found that from 2010 to 2013, there has been a 3‐fold increase in RAC‐related activities. The RACs are selected by CMS via a competitive bidding process and are contractually incentivize via a contingency fee. This means that they receive a portion of the funds that they recover (anywhere from 9%12% depending on the contract). If the RAC's claim is overturned on appeal, the RAC must repay the contingency fee, but does not face an economic penalty. This creates a potential incentive for RACs to be overly aggressive in pursuing potential overpayments from hospitals and providers.

The institutions in this study disputed 91% of allegations of overpayment. This dispute rate is notably higher than the 50% that was reported by a survey conducted by the American Hospital Association.[5] What is unknown is what the actual rate of overturned decisions based on appeal would be, as 49% of all contested claims from the study institutions were withdrawn and rebilled, and did not go through the complete appeals process. The authors cite the lengthy and presumably expensive process of adjudication as the reason for the decision to rebill the claims at the typically lower payment levels available under Medicare Part B. A 2012 report by the Office of the Inspector General (OIG) found that most (72%) of RAC‐denied hospital inpatient claims were overturned on appeal, in favor of the hospital by an administrative law judge (ALJ). This high rate of turnover has initiated a national discussion about the unbalanced financial incentives of the process per current design.

Since 2009, there has been a 10‐fold increase in the number of appeals waiting for a decision, with hearing delays reported to be as long as 32 months.[5, 6] The ALJ is required to issue a decision within 90 days of an appeal request. However, despite the huge volume of audits and secondary appeals generated by the RAC process, CMS has done little to expand the appeal infrastructure and the ALJ resources to keep pace with the incentivized RAC contractors.

The ALJ appeal backlog became so substantial that the Office of Medicare Hearings and Appeals published the following statement: As noted in a Federal Register Notice released by the Office of Medicare Hearings and Appeals (OMHA) in January 2014, the unprecedented growth in claim appeals continues to exceed the available adjudication resources to address [such] appeals.The CMS supports OMHA's efforts to bring efficiencies to the OMHA appeals process. Ultimately CMS offered hospitals a blanket 68% settlement for outstanding appeals to simply settle the backlogged cases.[7]

Finally, the authors note that an average of 5 full‐time equivalents (FTEs) was required by each institution to support the compliance‐related activities, which the authors claim is onerous and expensive. Their experience is consistent with other national reports that have found that 69% of surveyed hospitals report spending more than $40,000 per year, whereas 11% spend more than $100,000 annually.[5]

Ultimately, the authors conclude that reform is needed. Nationally many have agreed. As such, based on feedback, the CMS announced changes to the RAC program in December 2014[8] including: (1) reduction of the RAC look back period to 6 months (vs 3 years) from the date of service for payment adjustments, (2) RAC review period decreases to 30 days (vs 60 days), (3) addition of a 30‐day discussion period for claims, (4) the RAC will not receive a contingency fee until the second level review is completed, (5) broadened scope beyond inpatient claims (eg, review of outpatient claims), (6) more transparency regarding the appeals process, (7) new requirements for RACs to maintain a <10% overturn rate at the first‐level review (if not met, the RAC will be placed on a corrective action plan), and (8) RACs are now required to maintain an overall accuracy rate of 95%. In addition, CMS must publically report through an annual Report to Congress a Recovery Auditor accuracy rate for each Recovery Auditor.[9] There is no doubt that the current RAC program has generated significant savings for CMS. However, it has resulted in a notable cost and administrative burden to others including hospitals and provider groups. With the implementation of measures that hold RACs more accountable for the quality of their reviews, it is unclear if these new reform measures proposed by CMS will substantially improve the postpayment refinement process. Only with continued, but expensive, vigilance by providers and hospitals to ensure that claims are accurately processed as was described by the study institutions by Sheehy et al.,[4] will we know the potential value of the postpayment system.

The presence of hospitalists has been a major change in acute care in recent decades. The demographics of hospitalized patients also have changed, with a substantial increase in the proportion of patients aged 65 years and older to almost 50%. Older hospitalized patients represent a medically complex population, with multiple chronic conditions including cognitive impairment.[1] It is noteworthy that, in many US hospitals, the majority of older patients are now cared for by hospitalists without subspecialty training in geriatric medicine.[2] The convergence of these changes has led us to ask important questions about the best approach to caring for the growing population of hospitalized older patients.

The care of older hospitalized patients poses unique challenges both during and following a hospitalization event. This patient population tends to have multiple chronic conditions coupled with frequent healthcare utilization or transitions in care (eg, hospital to postacute care). In addition, geriatric syndromes are common among this group and may include: delirium, dementia, depression, functional impairment, falls, incontinence, pain, polypharmacy, and unintentional weight loss. It is also common for multiple geriatric syndromes to co‐occur (eg, falls and incontinence). The presence of one or more geriatric syndromes may complicate patient care and additionally impact outcomes, including hospitalization and mortality.[3, 4] An interdisciplinary geriatric team specifically diagnoses and treats these syndromes within the context of other presenting illnesses and comorbidities. Thus, a logical hypothesis would be that specialized geriatric consultation would improve outcomes of older hospitalized patients.

The study by Nazir et al.[5] in this issue of the Journal of Hospital Medicine explores this hypothesis, but generates more questions than answers. Briefly, the study examines a cohort of older hospitalized patients with cognitive impairment (CI). The authors compare rehospitalization and mortality outcomes among 176 patients who received geriatric consultation services (GCS) and 239 patients who received usual hospital care. Although the intervention group differed from the usual care group in meaningful ways outside of the intervention, the investigators did due diligence to adjust for these differences in their analysis. After adjustment, 30‐day and 1‐year mortality outcomes were comparable between groups, and the hazard for 30‐day readmissions was higher for the GCS group.

These findings stood contrary to the authors' hypothesis and what many would expect with subspecialty involvement during hospitalization. As the authors point out, however, we should interpret these findings cautiously due to a number of factors that may contribute to the seemingly limited effect of GCS in this study. First, it is important to note that this study occurred between 2006 and 2008. The emphasis on hospital readmissions as an important clinical outcome was increasing, although it had not reached the level that followed the 2009 publication by Jencks et al.[6] This emphasis further intensified following the inclusion of the Hospital Readmissions Reduction Program (HRRP) as part of the Affordable Care Act.[7] Thus, the implementation of the GCS in this university‐affiliated hospital may have reflected this pre‐HRRP period. For example, the team‐based rounds occurred only at the time of the initial consult. If a similar GCS were designed today in the post‐HRRP period, one could imagine more intense team‐based involvement occurring throughout the hospital stay, in particular near the time of discharge. In addition, recent studies underscore the importance of supporting transitions in care for older adults, who are often in need of postacute care, home health, and other services following hospitalization.[8] As noted by Nazir and colleagues, other interventions that have shown an impact on 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and outpatient setting. The authors also mentioned that a future component of preventing hospital readmissions was a stronger emphasis on advance care planning (ACP) discussions both during and following hospitalization. Neither of these key elements (eg, care transition personnel or proactive ACP discussions) was part of the GCS model evaluated in this study. Thus, it is unknown to what extent the higher 30‐day readmissions that occurred for the GCS group were consistent with patient/family goals of care. It is also unknown to what extent these readmissions were potentially unavoidable.

Perhaps even more importantly, this study is a reminder of the difference between efficacy and effectiveness; that is, does geriatric consultation work (efficacy) versus does a GCS as implemented at this specific hospital work (effectiveness)? The latter reflects not only aspects of what a geriatric interdisciplinary team may diagnose and recommend, but includes how patients are identified for consultation (referral process), the environment in which the consultation occurs (care coordination on unit or among team), and the fidelity to GCS recommendations. Without reported measures, it is unclear to what extent GCS achieved better recognition and treatment of geriatric syndromes, a reduction in polypharmacy, and optimal discharge planning. Theoretically, it is through the robust implementation of these components that better clinical outcomes would result. Even with a high degree of intervention implementation, 12‐month outcomes may be too far removed from the GCS intervention, especially for older patients with CI who are at high risk for decline.

Unfortunately, geriatric syndromes often go unrecognized, with high rates of polypharmacy at hospital discharge[9] and more than 50% of inpatients with unrecognized dementia,[10] delirium,[11] depression,[12] and nutritional risk.[13] Thus, our need for hospital geriatric care and expertise is greater than ever. This study highlights many of the challenges of the traditional consultative model of care and a need for innovative approaches to recognize and treat geriatric syndromes. It is likely that, given the complex nature of geriatric patients, efficacious consultative models will need to address multiple chronic conditions and extend beyond the hospital discharge period. However, based on available evidence, it is currently unclear what specific interventions are efficacious and what type of geriatric consultative model is required. No matter the method, hospitalists must recognize the unique challenges of this population and work to ensure safe hospitalization and care transitions.

The presence of hospitalists has been a major change in acute care in recent decades. The demographics of hospitalized patients also have changed, with a substantial increase in the proportion of patients aged 65 years and older to almost 50%. Older hospitalized patients represent a medically complex population, with multiple chronic conditions including cognitive impairment.[1] It is noteworthy that, in many US hospitals, the majority of older patients are now cared for by hospitalists without subspecialty training in geriatric medicine.[2] The convergence of these changes has led us to ask important questions about the best approach to caring for the growing population of hospitalized older patients.

The care of older hospitalized patients poses unique challenges both during and following a hospitalization event. This patient population tends to have multiple chronic conditions coupled with frequent healthcare utilization or transitions in care (eg, hospital to postacute care). In addition, geriatric syndromes are common among this group and may include: delirium, dementia, depression, functional impairment, falls, incontinence, pain, polypharmacy, and unintentional weight loss. It is also common for multiple geriatric syndromes to co‐occur (eg, falls and incontinence). The presence of one or more geriatric syndromes may complicate patient care and additionally impact outcomes, including hospitalization and mortality.[3, 4] An interdisciplinary geriatric team specifically diagnoses and treats these syndromes within the context of other presenting illnesses and comorbidities. Thus, a logical hypothesis would be that specialized geriatric consultation would improve outcomes of older hospitalized patients.

The study by Nazir et al.[5] in this issue of the Journal of Hospital Medicine explores this hypothesis, but generates more questions than answers. Briefly, the study examines a cohort of older hospitalized patients with cognitive impairment (CI). The authors compare rehospitalization and mortality outcomes among 176 patients who received geriatric consultation services (GCS) and 239 patients who received usual hospital care. Although the intervention group differed from the usual care group in meaningful ways outside of the intervention, the investigators did due diligence to adjust for these differences in their analysis. After adjustment, 30‐day and 1‐year mortality outcomes were comparable between groups, and the hazard for 30‐day readmissions was higher for the GCS group.

These findings stood contrary to the authors' hypothesis and what many would expect with subspecialty involvement during hospitalization. As the authors point out, however, we should interpret these findings cautiously due to a number of factors that may contribute to the seemingly limited effect of GCS in this study. First, it is important to note that this study occurred between 2006 and 2008. The emphasis on hospital readmissions as an important clinical outcome was increasing, although it had not reached the level that followed the 2009 publication by Jencks et al.[6] This emphasis further intensified following the inclusion of the Hospital Readmissions Reduction Program (HRRP) as part of the Affordable Care Act.[7] Thus, the implementation of the GCS in this university‐affiliated hospital may have reflected this pre‐HRRP period. For example, the team‐based rounds occurred only at the time of the initial consult. If a similar GCS were designed today in the post‐HRRP period, one could imagine more intense team‐based involvement occurring throughout the hospital stay, in particular near the time of discharge. In addition, recent studies underscore the importance of supporting transitions in care for older adults, who are often in need of postacute care, home health, and other services following hospitalization.[8] As noted by Nazir and colleagues, other interventions that have shown an impact on 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and outpatient setting. The authors also mentioned that a future component of preventing hospital readmissions was a stronger emphasis on advance care planning (ACP) discussions both during and following hospitalization. Neither of these key elements (eg, care transition personnel or proactive ACP discussions) was part of the GCS model evaluated in this study. Thus, it is unknown to what extent the higher 30‐day readmissions that occurred for the GCS group were consistent with patient/family goals of care. It is also unknown to what extent these readmissions were potentially unavoidable.

Perhaps even more importantly, this study is a reminder of the difference between efficacy and effectiveness; that is, does geriatric consultation work (efficacy) versus does a GCS as implemented at this specific hospital work (effectiveness)? The latter reflects not only aspects of what a geriatric interdisciplinary team may diagnose and recommend, but includes how patients are identified for consultation (referral process), the environment in which the consultation occurs (care coordination on unit or among team), and the fidelity to GCS recommendations. Without reported measures, it is unclear to what extent GCS achieved better recognition and treatment of geriatric syndromes, a reduction in polypharmacy, and optimal discharge planning. Theoretically, it is through the robust implementation of these components that better clinical outcomes would result. Even with a high degree of intervention implementation, 12‐month outcomes may be too far removed from the GCS intervention, especially for older patients with CI who are at high risk for decline.

Unfortunately, geriatric syndromes often go unrecognized, with high rates of polypharmacy at hospital discharge[9] and more than 50% of inpatients with unrecognized dementia,[10] delirium,[11] depression,[12] and nutritional risk.[13] Thus, our need for hospital geriatric care and expertise is greater than ever. This study highlights many of the challenges of the traditional consultative model of care and a need for innovative approaches to recognize and treat geriatric syndromes. It is likely that, given the complex nature of geriatric patients, efficacious consultative models will need to address multiple chronic conditions and extend beyond the hospital discharge period. However, based on available evidence, it is currently unclear what specific interventions are efficacious and what type of geriatric consultative model is required. No matter the method, hospitalists must recognize the unique challenges of this population and work to ensure safe hospitalization and care transitions.

The presence of hospitalists has been a major change in acute care in recent decades. The demographics of hospitalized patients also have changed, with a substantial increase in the proportion of patients aged 65 years and older to almost 50%. Older hospitalized patients represent a medically complex population, with multiple chronic conditions including cognitive impairment.[1] It is noteworthy that, in many US hospitals, the majority of older patients are now cared for by hospitalists without subspecialty training in geriatric medicine.[2] The convergence of these changes has led us to ask important questions about the best approach to caring for the growing population of hospitalized older patients.

The care of older hospitalized patients poses unique challenges both during and following a hospitalization event. This patient population tends to have multiple chronic conditions coupled with frequent healthcare utilization or transitions in care (eg, hospital to postacute care). In addition, geriatric syndromes are common among this group and may include: delirium, dementia, depression, functional impairment, falls, incontinence, pain, polypharmacy, and unintentional weight loss. It is also common for multiple geriatric syndromes to co‐occur (eg, falls and incontinence). The presence of one or more geriatric syndromes may complicate patient care and additionally impact outcomes, including hospitalization and mortality.[3, 4] An interdisciplinary geriatric team specifically diagnoses and treats these syndromes within the context of other presenting illnesses and comorbidities. Thus, a logical hypothesis would be that specialized geriatric consultation would improve outcomes of older hospitalized patients.

The study by Nazir et al.[5] in this issue of the Journal of Hospital Medicine explores this hypothesis, but generates more questions than answers. Briefly, the study examines a cohort of older hospitalized patients with cognitive impairment (CI). The authors compare rehospitalization and mortality outcomes among 176 patients who received geriatric consultation services (GCS) and 239 patients who received usual hospital care. Although the intervention group differed from the usual care group in meaningful ways outside of the intervention, the investigators did due diligence to adjust for these differences in their analysis. After adjustment, 30‐day and 1‐year mortality outcomes were comparable between groups, and the hazard for 30‐day readmissions was higher for the GCS group.

These findings stood contrary to the authors' hypothesis and what many would expect with subspecialty involvement during hospitalization. As the authors point out, however, we should interpret these findings cautiously due to a number of factors that may contribute to the seemingly limited effect of GCS in this study. First, it is important to note that this study occurred between 2006 and 2008. The emphasis on hospital readmissions as an important clinical outcome was increasing, although it had not reached the level that followed the 2009 publication by Jencks et al.[6] This emphasis further intensified following the inclusion of the Hospital Readmissions Reduction Program (HRRP) as part of the Affordable Care Act.[7] Thus, the implementation of the GCS in this university‐affiliated hospital may have reflected this pre‐HRRP period. For example, the team‐based rounds occurred only at the time of the initial consult. If a similar GCS were designed today in the post‐HRRP period, one could imagine more intense team‐based involvement occurring throughout the hospital stay, in particular near the time of discharge. In addition, recent studies underscore the importance of supporting transitions in care for older adults, who are often in need of postacute care, home health, and other services following hospitalization.[8] As noted by Nazir and colleagues, other interventions that have shown an impact on 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and outpatient setting. The authors also mentioned that a future component of preventing hospital readmissions was a stronger emphasis on advance care planning (ACP) discussions both during and following hospitalization. Neither of these key elements (eg, care transition personnel or proactive ACP discussions) was part of the GCS model evaluated in this study. Thus, it is unknown to what extent the higher 30‐day readmissions that occurred for the GCS group were consistent with patient/family goals of care. It is also unknown to what extent these readmissions were potentially unavoidable.

Perhaps even more importantly, this study is a reminder of the difference between efficacy and effectiveness; that is, does geriatric consultation work (efficacy) versus does a GCS as implemented at this specific hospital work (effectiveness)? The latter reflects not only aspects of what a geriatric interdisciplinary team may diagnose and recommend, but includes how patients are identified for consultation (referral process), the environment in which the consultation occurs (care coordination on unit or among team), and the fidelity to GCS recommendations. Without reported measures, it is unclear to what extent GCS achieved better recognition and treatment of geriatric syndromes, a reduction in polypharmacy, and optimal discharge planning. Theoretically, it is through the robust implementation of these components that better clinical outcomes would result. Even with a high degree of intervention implementation, 12‐month outcomes may be too far removed from the GCS intervention, especially for older patients with CI who are at high risk for decline.

Unfortunately, geriatric syndromes often go unrecognized, with high rates of polypharmacy at hospital discharge[9] and more than 50% of inpatients with unrecognized dementia,[10] delirium,[11] depression,[12] and nutritional risk.[13] Thus, our need for hospital geriatric care and expertise is greater than ever. This study highlights many of the challenges of the traditional consultative model of care and a need for innovative approaches to recognize and treat geriatric syndromes. It is likely that, given the complex nature of geriatric patients, efficacious consultative models will need to address multiple chronic conditions and extend beyond the hospital discharge period. However, based on available evidence, it is currently unclear what specific interventions are efficacious and what type of geriatric consultative model is required. No matter the method, hospitalists must recognize the unique challenges of this population and work to ensure safe hospitalization and care transitions.

The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]

In this issue of the Journal of Hospital Medicine, McAlister and colleagues¹⁰ ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.

Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]

These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.

Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.

PATIENT FACTORS

What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?

HOSPITAL ENVIRONMENT

How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?

CARE TRANSITIONS

What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?

SOCIAL AND ENVIRONMENTAL FACTORS

What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?

Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.

The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]

In this issue of the Journal of Hospital Medicine, McAlister and colleagues¹⁰ ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.

Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]

These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.

Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.

PATIENT FACTORS

What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?

HOSPITAL ENVIRONMENT

How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?

CARE TRANSITIONS

What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?

SOCIAL AND ENVIRONMENTAL FACTORS

What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?

Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.

The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]

In this issue of the Journal of Hospital Medicine, McAlister and colleagues¹⁰ ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.

Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]

These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.

Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.

PATIENT FACTORS

What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?

HOSPITAL ENVIRONMENT

How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?

CARE TRANSITIONS

What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?

SOCIAL AND ENVIRONMENTAL FACTORS

What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?

Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.