Brian J Miller, MD, MBA, MPH

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.

Still in its infancy, the Hospital Compare overall hospital quality star rating program introduced by the Centers for Medicare & Medicaid Services (CMS) has generated intense industry debate. Individual health systems are microcosms of the challenges of ratings and measurement design. Sibley Memorial Hospital, a member of Johns Hopkins Medicine, is a well-run, 288-bed, community hospital located in a wealthy section of northwest District of Columbia with a five-star rating. In contrast, its academic partner, the Johns Hopkins Hospital, a 1,162-bed hospital with a century-long history of innovation situated in an impoverished Baltimore, Maryland, neighborhood, received a three-star rating.

Hospital ratings are the product of an industry in transition: As care delivery has shifted from an individual provider-driven industry to an increasingly scaled systems enterprise, policymakers implemented regulatory standards targeting quality measurement. Subsequent to the National Academy of Medicine’s 1999 report To Err is Human, policy efforts brought public reporting of quality ratings to multiple market segments, including dialysis facilities (2001), nursing homes (2003), Medicare Advantage plans (2007), and physicians (2015). The hospital industry was no exception, and in 2016—with much controversy¹—CMS launched the hospital star ratings program.

CMS Star Ratings for hospitals are based on seven measure groups: mortality, safety, readmission, patient experience, effectiveness, timeliness, and efficient use of medical imaging. Both industry and researchers have decried the challenges of star ratings, noting that hospitals with a narrower scope of services are more likely to receive higher ratings.² Measure groupings may be further flawed as shown by recent work demonstrating that larger, safety net, or academic hospitals, as well as hospitals offering transplant services, have higher readmission rates,³ which may be caused by differences in patient complexity. Other research has demonstrated that overall quality ratings inappropriately pool all hospitals together, when it may be fairer to initially categorize hospitals and then score them.⁴

It is within this maelstrom of debate that, in this month’s issue of the Journal of Hospital Medicine, Shi and colleagues explore the relationship between hospital star ratings and the socioeconomic features of the surrounding communities.⁵ Conducting their analysis by linking multiple reputable government and industry sources, Shi and colleagues found that counties with higher education attainment and a lower proportion of dual Medicare-Medicaid–eligible populations had higher hospital star ratings. Furthermore, a county’s minority population percentage negatively correlated with hospital ratings. Validating the experience of many rural hospital executives—who frequently experience financial challenges—Shi and colleagues noted that rural hospitals were less likely to receive five-star ratings.

Do these findings reflect a true disparity and lack of access to high-quality hospitals, or are they artifactual—secondary to a flawed construct of hospital quality measurement? Many lower-ranking hospitals are urban academic centers frequently providing services not offered at their five-star community counterparts, such as neurosurgery, comprehensive cancer care, and organ transplants, while simultaneously serving as safety net hospitals, research institutions, trauma centers, and national referral centers.

Sociodemographics factor significantly in self-care management for hospital aftercare. Health literacy, access to primary and behavioral healthcare, and transportation all affect star indicators. Recent work⁶ demonstrated that comprehensive investments in transitional care strategies and the social determinants of health were ineffective at reducing readmissions, which suggests that high readmission rates for hospitals in impoverished areas are not only common, but also may not accurately reflect hospital quality and local investment.

Patient experience is also complicating, with research demonstrating that patient perceptions vary significantly by education, age, primary language, ethnicity, and overall health. For example, one-third of average-ranked hospitals would have rankings vary by at least 18 percentile points when evaluated by Spanish-speaking patients. Star ratings fail to capture and communicate this granularity.⁷

More concerning is that star ratings inherently assume that hospital performance is being compared across the same tasks, regardless of patient characteristics, local resources, or the scope of services provided, the latter of which may vary between hospitals. For example, communication may differ in both complexity and time intensity: Explaining an antibiotic to the uncomplicated patient with pneumonia differs from prescribing an antibiotic to a patient who is legally blind from optic neuritis, walks with a cane because of multiple sclerosis, and has 24 other prescription medications. Similar challenges exist for differences in local neighborhood resources and for facilities with differing service scope.

Although one strategy to handle these “disparities” in star ratings might be to risk-adjust for social determinants of health, patients may be better served by first rethinking how star ratings are constructed. Clustering hospitals by scope of services provided and geographic region prior to determining star ratings would provide consumers with meaningful information by helping patients compare and make choices among either local or regional hospitals; national quality rankings are unhelpful for patients.

Arguably one of the most complex and person-dependent service enterprises, care delivery presents unique challenges for evaluation of customer experience and medical quality. Hospital star ratings are no exception: We must rethink their construction so they can be more meaningful for both patients and physicians.

The authors would like to acknowledge Daniel J Brotman, MD, for his editorial advice and input.

Dr Miller reported consulting for the Federal Trade Commission and serving as a member of the Centers for Medicare & Medicaid Services Medicare Evidence Development Coverage Advisory Committee. Drs Siddiqui and Deutschendorf have nothing to disclose.

Still in its infancy, the Hospital Compare overall hospital quality star rating program introduced by the Centers for Medicare & Medicaid Services (CMS) has generated intense industry debate. Individual health systems are microcosms of the challenges of ratings and measurement design. Sibley Memorial Hospital, a member of Johns Hopkins Medicine, is a well-run, 288-bed, community hospital located in a wealthy section of northwest District of Columbia with a five-star rating. In contrast, its academic partner, the Johns Hopkins Hospital, a 1,162-bed hospital with a century-long history of innovation situated in an impoverished Baltimore, Maryland, neighborhood, received a three-star rating.

Hospital ratings are the product of an industry in transition: As care delivery has shifted from an individual provider-driven industry to an increasingly scaled systems enterprise, policymakers implemented regulatory standards targeting quality measurement. Subsequent to the National Academy of Medicine’s 1999 report To Err is Human, policy efforts brought public reporting of quality ratings to multiple market segments, including dialysis facilities (2001), nursing homes (2003), Medicare Advantage plans (2007), and physicians (2015). The hospital industry was no exception, and in 2016—with much controversy¹—CMS launched the hospital star ratings program.

CMS Star Ratings for hospitals are based on seven measure groups: mortality, safety, readmission, patient experience, effectiveness, timeliness, and efficient use of medical imaging. Both industry and researchers have decried the challenges of star ratings, noting that hospitals with a narrower scope of services are more likely to receive higher ratings.² Measure groupings may be further flawed as shown by recent work demonstrating that larger, safety net, or academic hospitals, as well as hospitals offering transplant services, have higher readmission rates,³ which may be caused by differences in patient complexity. Other research has demonstrated that overall quality ratings inappropriately pool all hospitals together, when it may be fairer to initially categorize hospitals and then score them.⁴

It is within this maelstrom of debate that, in this month’s issue of the Journal of Hospital Medicine, Shi and colleagues explore the relationship between hospital star ratings and the socioeconomic features of the surrounding communities.⁵ Conducting their analysis by linking multiple reputable government and industry sources, Shi and colleagues found that counties with higher education attainment and a lower proportion of dual Medicare-Medicaid–eligible populations had higher hospital star ratings. Furthermore, a county’s minority population percentage negatively correlated with hospital ratings. Validating the experience of many rural hospital executives—who frequently experience financial challenges—Shi and colleagues noted that rural hospitals were less likely to receive five-star ratings.

Do these findings reflect a true disparity and lack of access to high-quality hospitals, or are they artifactual—secondary to a flawed construct of hospital quality measurement? Many lower-ranking hospitals are urban academic centers frequently providing services not offered at their five-star community counterparts, such as neurosurgery, comprehensive cancer care, and organ transplants, while simultaneously serving as safety net hospitals, research institutions, trauma centers, and national referral centers.

Sociodemographics factor significantly in self-care management for hospital aftercare. Health literacy, access to primary and behavioral healthcare, and transportation all affect star indicators. Recent work⁶ demonstrated that comprehensive investments in transitional care strategies and the social determinants of health were ineffective at reducing readmissions, which suggests that high readmission rates for hospitals in impoverished areas are not only common, but also may not accurately reflect hospital quality and local investment.

Patient experience is also complicating, with research demonstrating that patient perceptions vary significantly by education, age, primary language, ethnicity, and overall health. For example, one-third of average-ranked hospitals would have rankings vary by at least 18 percentile points when evaluated by Spanish-speaking patients. Star ratings fail to capture and communicate this granularity.⁷

More concerning is that star ratings inherently assume that hospital performance is being compared across the same tasks, regardless of patient characteristics, local resources, or the scope of services provided, the latter of which may vary between hospitals. For example, communication may differ in both complexity and time intensity: Explaining an antibiotic to the uncomplicated patient with pneumonia differs from prescribing an antibiotic to a patient who is legally blind from optic neuritis, walks with a cane because of multiple sclerosis, and has 24 other prescription medications. Similar challenges exist for differences in local neighborhood resources and for facilities with differing service scope.

Although one strategy to handle these “disparities” in star ratings might be to risk-adjust for social determinants of health, patients may be better served by first rethinking how star ratings are constructed. Clustering hospitals by scope of services provided and geographic region prior to determining star ratings would provide consumers with meaningful information by helping patients compare and make choices among either local or regional hospitals; national quality rankings are unhelpful for patients.

Arguably one of the most complex and person-dependent service enterprises, care delivery presents unique challenges for evaluation of customer experience and medical quality. Hospital star ratings are no exception: We must rethink their construction so they can be more meaningful for both patients and physicians.

The authors would like to acknowledge Daniel J Brotman, MD, for his editorial advice and input.

Dr Miller reported consulting for the Federal Trade Commission and serving as a member of the Centers for Medicare & Medicaid Services Medicare Evidence Development Coverage Advisory Committee. Drs Siddiqui and Deutschendorf have nothing to disclose.

Still in its infancy, the Hospital Compare overall hospital quality star rating program introduced by the Centers for Medicare & Medicaid Services (CMS) has generated intense industry debate. Individual health systems are microcosms of the challenges of ratings and measurement design. Sibley Memorial Hospital, a member of Johns Hopkins Medicine, is a well-run, 288-bed, community hospital located in a wealthy section of northwest District of Columbia with a five-star rating. In contrast, its academic partner, the Johns Hopkins Hospital, a 1,162-bed hospital with a century-long history of innovation situated in an impoverished Baltimore, Maryland, neighborhood, received a three-star rating.

Hospital ratings are the product of an industry in transition: As care delivery has shifted from an individual provider-driven industry to an increasingly scaled systems enterprise, policymakers implemented regulatory standards targeting quality measurement. Subsequent to the National Academy of Medicine’s 1999 report To Err is Human, policy efforts brought public reporting of quality ratings to multiple market segments, including dialysis facilities (2001), nursing homes (2003), Medicare Advantage plans (2007), and physicians (2015). The hospital industry was no exception, and in 2016—with much controversy¹—CMS launched the hospital star ratings program.

CMS Star Ratings for hospitals are based on seven measure groups: mortality, safety, readmission, patient experience, effectiveness, timeliness, and efficient use of medical imaging. Both industry and researchers have decried the challenges of star ratings, noting that hospitals with a narrower scope of services are more likely to receive higher ratings.² Measure groupings may be further flawed as shown by recent work demonstrating that larger, safety net, or academic hospitals, as well as hospitals offering transplant services, have higher readmission rates,³ which may be caused by differences in patient complexity. Other research has demonstrated that overall quality ratings inappropriately pool all hospitals together, when it may be fairer to initially categorize hospitals and then score them.⁴

It is within this maelstrom of debate that, in this month’s issue of the Journal of Hospital Medicine, Shi and colleagues explore the relationship between hospital star ratings and the socioeconomic features of the surrounding communities.⁵ Conducting their analysis by linking multiple reputable government and industry sources, Shi and colleagues found that counties with higher education attainment and a lower proportion of dual Medicare-Medicaid–eligible populations had higher hospital star ratings. Furthermore, a county’s minority population percentage negatively correlated with hospital ratings. Validating the experience of many rural hospital executives—who frequently experience financial challenges—Shi and colleagues noted that rural hospitals were less likely to receive five-star ratings.

Do these findings reflect a true disparity and lack of access to high-quality hospitals, or are they artifactual—secondary to a flawed construct of hospital quality measurement? Many lower-ranking hospitals are urban academic centers frequently providing services not offered at their five-star community counterparts, such as neurosurgery, comprehensive cancer care, and organ transplants, while simultaneously serving as safety net hospitals, research institutions, trauma centers, and national referral centers.

Sociodemographics factor significantly in self-care management for hospital aftercare. Health literacy, access to primary and behavioral healthcare, and transportation all affect star indicators. Recent work⁶ demonstrated that comprehensive investments in transitional care strategies and the social determinants of health were ineffective at reducing readmissions, which suggests that high readmission rates for hospitals in impoverished areas are not only common, but also may not accurately reflect hospital quality and local investment.

Patient experience is also complicating, with research demonstrating that patient perceptions vary significantly by education, age, primary language, ethnicity, and overall health. For example, one-third of average-ranked hospitals would have rankings vary by at least 18 percentile points when evaluated by Spanish-speaking patients. Star ratings fail to capture and communicate this granularity.⁷

More concerning is that star ratings inherently assume that hospital performance is being compared across the same tasks, regardless of patient characteristics, local resources, or the scope of services provided, the latter of which may vary between hospitals. For example, communication may differ in both complexity and time intensity: Explaining an antibiotic to the uncomplicated patient with pneumonia differs from prescribing an antibiotic to a patient who is legally blind from optic neuritis, walks with a cane because of multiple sclerosis, and has 24 other prescription medications. Similar challenges exist for differences in local neighborhood resources and for facilities with differing service scope.

Although one strategy to handle these “disparities” in star ratings might be to risk-adjust for social determinants of health, patients may be better served by first rethinking how star ratings are constructed. Clustering hospitals by scope of services provided and geographic region prior to determining star ratings would provide consumers with meaningful information by helping patients compare and make choices among either local or regional hospitals; national quality rankings are unhelpful for patients.

Arguably one of the most complex and person-dependent service enterprises, care delivery presents unique challenges for evaluation of customer experience and medical quality. Hospital star ratings are no exception: We must rethink their construction so they can be more meaningful for both patients and physicians.

The authors would like to acknowledge Daniel J Brotman, MD, for his editorial advice and input.

Dr Miller reported consulting for the Federal Trade Commission and serving as a member of the Centers for Medicare & Medicaid Services Medicare Evidence Development Coverage Advisory Committee. Drs Siddiqui and Deutschendorf have nothing to disclose.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.