Importance:The US Centers for Medicare and Medicaid Services Hospital Readmissions Reduction Program penalizes hospitals with higher-than-expected risk-adjusted 30-day readmission rates (excess readmission ratio [ERR] > 1) after acute myocardial infarction (MI). However, the association of ERR with MI care processes and outcomes are not well established. Objective:To evaluate the association between ERR for MI with in-hospital process of care measures and 1-year clinical outcomes. Design, Setting, and Participants:Observational analysis of hospitalized patients with MI from National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines centers subject to the first cycle of the Hospital Readmissions Reduction Program between July 1, 2008, and June 30, 2011. Exposures:The ERR for MI (MI-ERR) in 2011. Main Outcomes and Measures:Adherence to process of care measures during index hospitalization in the overall study population and risk of the composite outcome of mortality or all-cause readmission within 1 year of discharge and its individual components among participants with available Centers for Medicare and Medicaid Services-linked data. Results:The median ages of patients in the MI-ERR greater than 1 and tertiles 1, 2, and 3 of the MI-ERR greater than 1 groups were 64, 63, 64, and 63 years, respectively. Among 380 hospitals that treated a total of 176 644 patients with MI during the study period, 43% had MI-ERR greater than 1. The proportions of patients of black race, those with heart failure signs at admission, and bleeding complications increased with higher MI-ERR. There was no significant association between adherence to MI performance measures and MI-ERR (adjusted odds ratio, 0.94; 95% CI, 0.81-1.08, per 0.1-unit increase in MI-ERR for overall defect-free care). Among the 51 453 patients with 1-year outcomes data available, higher MI-ERR was associated with higher adjusted risk of the composite outcome and all-cause readmission within 1 year of discharge. This association was largely driven by readmissions early after discharge and was not significant in landmark analyses beginning 30 days after discharge. The MI-ERR was not associated with risk for mortality within 1 year of discharge in the overall and 30-day landmark analyses. Conclusions and Relevance:During the first cycle of the Hospital Readmissions Reduction Program, participating hospitals' risk-adjusted 30-day readmission rates following MI were not associated with in-hospital quality of MI care or clinical outcomes occurring after the first 30 days after discharge.

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HomeCirculation: Cardiovascular Quality and OutcomesVol. 10, No. 5Social Risk Factors and Performance Under Medicare's Value-Based Purchasing Programs Free AccessBrief ReportPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessBrief ReportPDF/EPUBSocial Risk Factors and Performance Under Medicare's Value-Based Purchasing Programs Karen E. Joynt, MD, MPH, Rachael Zuckerman, PhD and Arnold M. Epstein, MD, MS Karen E. JoyntKaren E. Joynt From the Brigham and Women's Hospital, Boston, MA (K.E.J., A.M.E.); Harvard T.H. Chan School of Public Health, Boston, MA (K.E.J., A.M.E.); and United States Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, WA (K.E.J., R.Z.). , Rachael ZuckermanRachael Zuckerman From the Brigham and Women's Hospital, Boston, MA (K.E.J., A.M.E.); Harvard T.H. Chan School of Public Health, Boston, MA (K.E.J., A.M.E.); and United States Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, WA (K.E.J., R.Z.). and Arnold M. EpsteinArnold M. Epstein From the Brigham and Women's Hospital, Boston, MA (K.E.J., A.M.E.); Harvard T.H. Chan School of Public Health, Boston, MA (K.E.J., A.M.E.); and United States Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, WA (K.E.J., R.Z.). Originally published15 May 2017https://doi.org/10.1161/CIRCOUTCOMES.117.003587Circulation: Cardiovascular Quality and Outcomes. 2017;10:e003587Policy Statement TitleReport to Congress: Social Risk Factors and Performance Under Medicare's Value-based Purchasing Programs.1OrganizationUS Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation.Release DateDecember 21, 2016.Policy ContextValue-based purchasing (VBP), or pay-for-performance, comprises a growing portion of Medicare payment and the changes in physician payment enacted in the Medicare and Children's Health Insurance Program Reauthorization Act are likely to accelerate these trends even further.2 Simultaneously, there is growing consensus that social risk factors—such as income, race and ethnicity, and community environment—play a major role in health.3–5 Persistent and meaningful gaps exist in health and even in life expectancy based on these factors.6–9These 2 issues intersect in VBP. If beneficiaries with social risk factors have worse health outcomes because of factors beyond providers' control, providers could be unfairly disadvantaged under VBP. On the contrary, if beneficiaries with social risk factors have worse health outcomes because the providers serving them provide low-quality care, the financial incentives and accountability of VBP could be an important strategy for improving care and reducing disparities.In 2014, Congress passed the Improving Medicare Postacute Care Transformation Act (IMPACT),10 which required that the Office of the Assistant Secretary for Planning and Evaluation (ASPE) at the US Department of Health and Human Services complete an empirical report addressing the issue of social risk in Medicare's current VBP programs to assist Congress in further decision making on this issue. The report covered here is the first component of the required work, which was submitted to Congress in December 2016.The ASPE report may have particular salience to cardiovascular clinicians, who practice every day in an environment significantly impacted by VBP. Many current quality measures in inpatient and outpatient VBP programs are cardiovascular in nature; patients with acute myocardial infarction and heart failure are the focus of many of the outcome measures in these programs, including readmission and mortality rates.Major Findings/RecommendationsThe report analyzed dual enrollment in Medicare and Medicaid as a marker for poverty, residence in a low-income–zone improvement plan code tabulation area, Black race, Hispanic ethnicity, disability, and residence in a rural area. Medicare payment programs were included in the report if they incorporated quality or resource-use measures (Table).Table. Medicare Payment Programs Examined in the Assistant Secretary for Planning and Evaluation Report to Congress on Social Risk FactorsProgram1Hospital Readmissions Reduction Program2Hospital Value-Based Purchasing Program3Hospital-Acquired Condition Reduction Program4Medicare Advantage (Part C) Quality Star Rating Program*5Medicare Shared Savings Program6Physician Value-Based Payment Modifier Program†7End-Stage Renal Disease Quality Incentive Program8Skilled Nursing Facility Value-Based Purchasing Program‡9Home Health Value-Based Purchasing Program‡*Includes part D metrics where applicable.†This program will be replaced by the Merit-based Incentive Payment System in 2019.‡The skilled nursing facility value-based purchasing program and home health value-based purchasing program are too new to have program-level data yet available for analysis; thus, for the purpose of the report, only certain measures that may be used in these 2 programs were analyzed.The report outlines 2 broad findings. First, beneficiaries with social risk factors had poorer outcomes on many quality measures, including processes (eg, blood pressure screening), clinical outcomes (eg, cholesterol control or readmissions), safety (eg, infection rates), patient experience (eg, communication from doctors and nurses), and resource use (eg, spending per hospital admission episode). This was true even when comparing beneficiaries within the same hospital, health plan, accountable care organization, physician group, or facility. Dual enrollment was typically the most powerful predictor of poor performance among those social risk factors examined.The second finding was that providers that disproportionately cared for beneficiaries with social risk factors tended to perform worse on quality measures. Although a portion of the difference was related to the social risk factors of the patients they served, differences were evident even after adjusting for beneficiary social risk profile. As a result, providers serving these populations (safety-net hospitals, physician groups with a high proportion of low-income patients, etc) were more likely to face financial penalties across the 5 Medicare VBP programs in which penalties are currently assessed, including programs in the hospital, physician group, and dialysis facility settings. They were also less likely to receive bonuses in Medicare advantage.It was also reported that in every care setting, be it hospital, health plan, accountable care organization, physician group, or facility, there were some providers that served a high proportion of beneficiaries with social risk factors who achieved high levels of performance, suggesting that high performance for at-risk populations is feasible with the right strategies and supports.These findings suggest that both patient social risk factors and provider performance contribute to the worse outcomes seen for each. However, the analyses do not identify the underlying causes of such findings. Beneficiaries with social risk factors may have worse outcomes because of higher medical risk, worse functional status, cognitive limitations, challenges in adhering to medications or lifestyle recommendations, or bias. Providers serving these beneficiaries may have worse performance because of unmeasured differences in patient population, a mismatch between resources and clinical workloads, fewer community resources, or simply worse quality of care.The complexity of this issue suggests that a single fix will not be adequate to address the problem. Instead, a broad set of strategies and considerations will be required. The report outlines 3 main strategies for consideration:The first strategy is to measure and report quality for beneficiaries with social risk factors because what is not measured can not be optimally addressed. This would require enhancement of data collection to overcome issues of sample size and the development of statistical techniques for stratified reporting. Relatedly, the development of measures specifically focused on health equity could help highlight existing disparities and provide impetus to reduce them.The second strategy is to set high, fair quality standards for all beneficiaries. This does not imply that measures broadly should or should not be adjusted for social risk, but rather that each quality measure should be examined to determine whether adjustment for social risk factors is appropriate. The National Quality Forum is currently completing a broad analysis of new and existing measures in regard to this issue,11 and their findings will also significantly inform this debate. Additionally, all measures could be examined to determine whether differences in unmeasured medical risk, including things like frailty, functional status, disability, and disease severity, might explain some of the relationships between social risk and outcomes.The third strategy is to reward and support high quality for beneficiaries with social risk factors. This includes creating specific, targeted payment adjustments within VBP models to incent a focus on improving health for vulnerable populations. Such bonus opportunities could also offset any real or perceived disincentives under value-based payment models to providing care for these individuals. Providing technical support to providers who serve high-risk individuals, and developing innovative care solutions via demonstrations that focus on socially at-risk groups, may also have the potential to reduce disparities.DiscussionAs reported here and elsewhere,12 the ASPE report to Congress provided a comprehensive review of the interplay between Medicare payment systems and social risk. In addition to providing specific considerations, the report aimed to broaden the conversation about the interaction between VBP and social risk. Although many have advocated for simply adjusting quality measures for social risk, doing so too broadly risks masking disparities and fails to address the pervasive, persistent underlying issues at hand. While there may be cases in which adjustment is appropriate, the report also encourages stakeholders to consider strategies beyond measure adjustment to improve the quality of care provided to socially at-risk patients and to support providers caring for these beneficiaries. Here, in particular, there may be opportunities for further clinical innovation and research, as addressed below.Application to Cardiovascular Disease CareQuality ImprovementAs noted above, the findings of the ASPE report are relevant to cardiovascular disease because of the sheer number of quality measures related to cardiovascular disease that make up the backbone of many Medicare VBP programs. Acute myocardial infarction and heart failure, 2 main conditions in many VBP programs, have been the focus of many quality improvement efforts from the cardiovascular community,13 and such efforts will be even more important under VBP. Ideally, VBP could serve as a catalyst to innovation in achieving good cardiovascular outcomes for individuals with social risk factors, who have been convincingly shown to face significant disparities in these areas.14 Engagement of the cardiovascular community on ways to do so could be particularly helpful as policymakers seek to optimize these programs to achieve their stated goals while avoiding unintended consequences.ResearchThe ASPE report outlines several areas in which further research is needed, many of which the cardiovascular research community may be particularly well-poised to address. For example, the report identifies the need for ongoing evolution in methodology on risk-adjustment, which may include advances in emerging data sources (electronic health records and patient-reported outcomes) and emerging methods (machine learning and advanced statistical techniques). The cardiovascular community has led in these areas before and continues to produce cutting-edge research on these topics.Another area of research identified in the report is research designed to identify the best practices for improving health outcomes in individuals with social risk factors. Here, mixed methods research might play an important role in helping clinicians and policymakers understand how some providers are able to achieve excellent care and outcomes for their vulnerable populations. There are many promising interventions in these areas in cardiovascular care, for example, on reducing readmissions or improving control of cardiovascular risk factors, that need to be further understood, scaled, and disseminated to truly address health disparities in cardiovascular disease and more broadly.Sources of FundingThis study was funded by the US Department of Health and Human Services (employment).DisclosuresThe authors are current or former employees or contractors for the US Department of Health and Human Services.FootnotesCorrespondence to Karen E. Joynt, MD, MPH, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115. E-mail [email protected]References1. U.S. Department of Health & Human Services, Office of the Assistant Secretary for Planning and Evaluation. Report to Congress: Social Risk Factors and Performance Under Medicare's Value-Based Purchasing Programs. https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs. Washington, DC: 2016. Accessed May 3, 2017.Google Scholar2. Centers for Medicare and Medicaid Services. Quality Payment Program: Delivery System Reform, Medicare Payment Reform, & MACRA. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/Quality-Payment-Program.html. Accessed June 4, 2016.Google Scholar3. United States Department of Health and Human Services. Healthy People 2020: Social Determinants Of Health.https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. Accessed December 29, 2015.Google Scholar4. United States Department of Health and Human Services. Healthy People 2020: Disparities.http://www.healthypeople.gov/2020/about/foundation-health-measures/Disparities. Accessed December 29, 2015.Google Scholar5. Committee on Accounting for SES in Medicare Payment Programs. Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: National Academies of Sciences, Engineering, and Medicine; 2016.Google Scholar6. Singh GK, Siahpush M. Widening socioeconomic inequalities in US life expectancy, 1980-2000.Int J Epidemiol. 2006; 35:969–979. doi: 10.1093/ije/dyl083.CrossrefMedlineGoogle Scholar7. Committee on the Long-Run Macroeconomic Effects of the Aging U.S. Population-Phase II, Committee on Population, Division of Behavioral and Social Sciences and Education, Board on Mathematical Sciences and Their Applications, Division on Engineering and Physical Sciences.The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses. Washington, DC: National Academies of Sciences, Engineering, and Medicine; 2015.Google Scholar8. Kochanek KD, Anderson RN, Arias E. Leading causes of death contributing to decrease in life expectancy gap between black and white populations: United States, 1999–2013.NCHS Data Brief. 2015:1–8.Google Scholar9. Singh GK, Siahpush M. Widening rural-urban disparities in life expectancy, U.S., 1969-2009.Am J Prev Med. 2014; 46:e19–e29. doi: 10.1016/j.amepre.2013.10.017.CrossrefMedlineGoogle Scholar10. Improving Medicare Post-Acute Care Transformation Act of 2014.HR. 4994, 113th Cong.; (2014).Google Scholar11. National Quality Forum. SES trial period.National Quality Forum. http://www.qualityforum.org/SES_Trial_Period.aspx. Accessed November 19, 2015.Google Scholar12. Joynt KE, De Lew N, Sheingold SH, Conway PH, Goodrich K, Epstein AM. Should medicare value-based purchasing take social risk into account?N Engl J Med. 2017; 376:510–513. doi: 10.1056/NEJMp1616278.CrossrefMedlineGoogle Scholar13. Ellrodt AG, Fonarow GC, Schwamm LH, Albert N, Bhatt DL, Cannon CP, Hernandez AF, Hlatky MA, Luepker RV, Peterson PN, Reeves M, Smith EE. Synthesizing lessons learned from get with the guidelines: the value of disease-based registries in improving quality and outcomes.Circulation. 2013; 128:2447–2460. doi: 10.1161/01.cir.0000435779.48007.5c.LinkGoogle Scholar14. Clark AM, DesMeules M, Luo W, Duncan AS, Wielgosz A. Socioeconomic status and cardiovascular disease: risks and implications for care.Nat Rev Cardiol. 2009; 6:712–722. doi: 10.1038/nrcardio.2009.163.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Zhang Y, Kunnath N, Dimick J, Scott J, Diaz A and Ibrahim A (2022) Social Vulnerability And Outcomes For Access-Sensitive Surgical Conditions Among Medicare Beneficiaries, Health Affairs, 10.1377/hlthaff.2021.01615, 41:5, (671-679), Online publication date: 1-May-2022. Boulos P, Messenger J and Waldo S (2022) Readmission After ACS: Burden, Epidemiology, and Mitigation, Current Cardiology Reports, 10.1007/s11886-022-01702-8 Taylor K, Diaz A, Nuliyalu U, Ibrahim A and Nathan H (2022) Association of Dual Medicare and Medicaid Eligibility With Outcomes and Spending for Cancer Surgery in High-Quality Hospitals, JAMA Surgery, 10.1001/jamasurg.2021.7586, 157:4, (e217586) Aswani M and Roberts E (2022) Social risk adjustment in the hospital readmission reduction program: Pitfalls of peer grouping, measurement challenges, and potential solutions, Health Services Research, 10.1111/1475-6773.13969 Liu L, Gauri D and Jindal R (2021) The Role of Patient Satisfaction in Hospitals' Medicare Reimbursements, Journal of Public Policy & Marketing, 10.1177/0743915620984723, 40:4, (558-570), Online publication date: 1-Oct-2021. Clancy C, Goodrich K, Moody-Williams J, Dorsey Sheares K, O'Kane M, Cha S and Agrawal S (2021) Quality, Safety, and Standards Organizations COVID-19 Impact Assessment: Lessons Learned and Compelling Needs, NAM Perspectives, 10.31478/202107d Topmiller M, McCann J, Rankin J, Hoang H, Bolton J and Sripipatana A (2021) Exploring the association of social determinants of health and clinical quality measures and performance in HRSA-funded health centres, Family Medicine and Community Health, 10.1136/fmch-2020-000853, 9:3, (e000853), Online publication date: 1-Jul-2021. Thirukumaran C, Kim Y, Cai X, Ricciardi B, Li Y, Fiscella K, Mesfin A and Glance L (2021) Association of the Comprehensive Care for Joint Replacement Model With Disparities in the Use of Total Hip and Total Knee Replacement, JAMA Network Open, 10.1001/jamanetworkopen.2021.11858, 4:5, (e2111858) Nerenz D, Austin J, Deutscher D, Maddox K, Nuccio E, Teigland C, Weinhandl E and Glance L (2021) Adjusting Quality Measures For Social Risk Factors Can Promote Equity In Health Care, Health Affairs, 10.1377/hlthaff.2020.01764, 40:4, (637-644), Online publication date: 1-Apr-2021. Lin M, Burke R, Orav E, Friend T and Burke L (2020) Ambulatory Follow-up and Outcomes Among Medicare Beneficiaries After Emergency Department Discharge, JAMA Network Open, 10.1001/jamanetworkopen.2020.19878, 3:10, (e2019878) Khullar D, Schpero W, Bond A, Qian Y and Casalino L (2020) Association Between Patient Social Risk and Physician Performance Scores in the First Year of the Merit-based Incentive Payment System, JAMA, 10.1001/jama.2020.13129, 324:10, (975), Online publication date: 8-Sep-2020. Shashikumar S, Huang K, Konetzka R and Joynt Maddox K (2020) Claims-based Frailty Indices, Medical Care, 10.1097/MLR.0000000000001359, 58:9, (815-825), Online publication date: 1-Sep-2020. Shashikumar S, Luke A, Johnston K and Joynt Maddox K (2020) Assessment of HF Outcomes Using a Claims-Based Frailty Index, JACC: Heart Failure, 10.1016/j.jchf.2019.12.012, 8:6, (481-488), Online publication date: 1-Jun-2020. Agrawal S and Shrank W (2020) Clinical and Social Risk Adjustment — Reconsidering Distinctions, New England Journal of Medicine, 10.1056/NEJMp1913993, 382:17, (1581-1583), Online publication date: 23-Apr-2020. Krumholz H, Wang Y, Wang K, Lin Z, Bernheim S, Xu X, Desai N and Normand S (2019) Association of Hospital Payment Profiles With Variation in 30-Day Medicare Cost for Inpatients With Heart Failure or Pneumonia, JAMA Network Open, 10.1001/jamanetworkopen.2019.15604, 2:11, (e1915604) Khazanie P and Ho P (2019) Leveraging Value-Based Payment Models to Reduce Sex Differences in Care, Circulation: Cardiovascular Quality and Outcomes, 12:8, Online publication date: 1-Aug-2019. McCarthy C, Vaduganathan M, Patel K, Lalani H, Ayers C, Bhatt D, Januzzi J, de Lemos J, Yancy C, Fonarow G and Pandey A (2019) Association of the New Peer Group–Stratified Method With the Reclassification of Penalty Status in the Hospital Readmission Reduction Program, JAMA Network Open, 10.1001/jamanetworkopen.2019.2987, 2:4, (e192987) Joynt Maddox K, Reidhead M, Hu J, Kind A, Zaslavsky A, Nagasako E and Nerenz D (2019) Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program, Health Services Research, 10.1111/1475-6773.13133, 54:2, (327-336), Online publication date: 1-Apr-2019. Borden W and Nallamothu B (2018) Making Health Policy More Accessible, Circulation: Cardiovascular Quality and Outcomes, 11:12, Online publication date: 1-Dec-2018. May 2017Vol 10, Issue 5 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.117.003587PMID: 28506982 Originally publishedMay 15, 2017 PDF download Advertisement SubjectsEthics and PolicyHealth ServicesRace and Ethnicity

Rural beneficiaries make up nearly one quarter of the Medicare population, yet rural providers and patients face specific challenges with health and health care delivery that remain inadequately understood. Health disparities between rural and urban residents are widespread, barriers to health care in rural communities persist, and the rural health care workforce is limited. To better understand and track the relationship between rurality and performance under Medicare’s payment programs, researchers must be able to identify rural beneficiaries, providers, and hospitals. Although numerous definitions of rurality are applied across the Medicare program, empirical research is lacking comparing the different definitions of rurality and the impact of their application to quality, outcome, or costs. Definitions that recognize rurality as a graded concept, rather than a dichotomous one, hold promise. Understanding the strengths and limitations of different approaches to identifying rurality will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these approaches.

OBJECTIVESAlthough we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of these costs among providers or markets. This is important because it could help us to understand why some patients are higher-cost compared with others and enable us to develop interventions to reduce costs for these patients.STUDY DESIGNObservational study.METHODSWe used a 20% sample of Medicare fee-for-service claims data from 2011 and 2012, and defined high-cost patients as those in the top 10% of standardized costs. We then characterized high-concentration hospitals as those with the highest proportion of high-cost patient claims, and high-concentration markets as the Hospital Referral Regions (HRRs) with the highest proportion of high-cost patients. We compared the characteristics and outcomes of each.RESULTSHigh-concentration hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. These hospitals were more likely to be for-profit and major teaching hospitals, located in urban settings, and have higher readmission rates. High-concentration HRRs had 13% high-cost patients compared with 9.5% for the remaining 90% of HRRs. These HRRs had a smaller supply of total physicians, a greater supply of cardiologists, higher rates of emergency department visits, and significantly higher expenditures on care in the last 6 months of life.CONCLUSIONSHigh-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets.

HomeCirculationVol. 135, No. 21Insurance and Cardiovascular Health Free AccessArticle CommentaryPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessArticle CommentaryPDF/EPUBInsurance and Cardiovascular HealthTime for Evidence to Trump Politics Rishi K. Wadhera, MD, MPhil and Karen E. Joynt, MD, MPH Rishi K. WadheraRishi K. Wadhera From Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (R.K.W., K.J.); and Department of Health Policy and Management, Harvard School of Public Health, Boston, MA (K.J.). and Karen E. JoyntKaren E. Joynt From Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (R.K.W., K.J.); and Department of Health Policy and Management, Harvard School of Public Health, Boston, MA (K.J.). Originally published4 May 2017https://doi.org/10.1161/CIRCULATIONAHA.117.028618Circulation. 2017;135:1988–1990Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2017: Previous Version 1 The United States is entrenched in a fierce debate over healthcare reform. The Affordable Care Act (ACA) strove to reduce the number of uninsured individuals, and after its implementation, 20 million Americans gained insurance coverage. Ongoing shifts in health policy imperil these gains. As options for repealing, replacing, or revising the ACA are debated, we aim to outline what is known about the relationship between insurance coverage and cardiovascular care, the impact of the ACA on cardiovascular care, and areas where gaps in our knowledge remain. Understanding these relationships may help clinical leaders and policymakers better craft future policy initiatives.Insurance Coverage and Cardiovascular CareRoutine medical care is essential for appropriate risk factor screening and treatment. Insurance mediates access to health care, so it is not surprising that uninsured individuals are less likely to receive screening for hypertension, diabetes mellitus, and hypercholesterolemia. Even when diagnosed, treatment and control of cardiovascular risk factors is lower in the uninsured population.1 Perhaps, in part, because of these disparities, coronary artery disease is more prevalent in low-income populations, who are generally at greater risk of being uninsured. Furthermore, although rates of acute myocardial infarction decreased in the US population the decade before the ACA, the proportion of uninsured individuals hospitalized for acute myocardial infarction rose.In populations with established cardiovascular conditions, lack of insurance has been associated with poorer care quality and worse short-term outcomes. Uninsured patients with coronary artery disease are less likely to receive appropriate evidence-based therapies in the outpatient setting.2 Patients with acute myocardial infarction without insurance have been shown to receive less aggressive care and fewer invasive cardiac procedures, and they also have higher in-hospital mortality rates than privately insured individuals.3 The narrative is no different for uninsured individuals hospitalized for heart failure, who are less likely to receive guideline directed therapy and also experience worse in-hospital outcomes.4 This pattern also pervades other cardiovascular conditions, such as stroke and peripheral artery disease. Lack of insurance clearly encumbers the delivery of optimal cardiovascular care.Impact of the ACA on Cardiovascular CareIn 2010, 7.3 million Americans with cardiovascular disease were uninsured. Almost half of this population lacked coverage because of cost and collectively were less likely to have a usual place of health care or be able to afford prescription drugs. They also experienced higher out-of-pocket health costs compared with insured individuals. In the first year after the ACA's implementation, 7 million individuals at risk of or with cardiovascular disease gained insurance coverage.How did this shift in coverage impact healthcare utilization? Studies of insurance expansion in low-income populations, which primarily gained coverage through Medicaid enrollment, demonstrated improvements in access to primary care, specialty care, and prescription drugs. Outpatient utilization, preventative care, and self-reported care quality also improved after Medicaid and private insurance expansion, whereas reliance on emergency department services decreased. Simultaneously, catastrophic out-of-pocket medical costs also declined.5 In the ACA era, the acquisition of health insurance has addressed some gaps in access to care, improved healthcare utilization, and diminished financial strain.Areas Where Gaps RemainWhat is less well known but arguably as critical is whether and how acquiring health insurance actually translates into better long-term cardiovascular health. Although it seems intuitive that insurance coverage and health would be positively related, evidence for a longitudinal association between gaining coverage and experiencing sustained, long-term improvements in health is sparse (Figure). We need more concrete data that health insurance improves cardiovascular (and overall) health on individual and population levels, as well as a better understanding of the mechanisms by which this occurs. Recent large shifts in coverage, as part of the ACA, provide an opportunity to study such phenomena.Download figureDownload PowerPointFigure. Shifts in insurance coverage and short- and long-term outcomes.However, health insurance is not a panacea; to make real improvements in cardiovascular health, we as a healthcare community will need to recognize that many of the most important factors that influence health are outside hospital walls. Factors often linked with being uninsured, such as low socioeconomic status, limited educational attainment, and living in neighborhoods with high levels of deprivation, may impact health in significant ways that attenuate the long-term benefits of gaining coverage. Even with insurance, other barriers associated with race and ethnicity, citizenship status, disability, and geography may impede health advancement. However, acquiring insurance may mitigate the negative effects of some social determinants of health in meaningful ways, and this represents another key area for future research efforts.Providing Evidence to Guide PolicyRepealing the ACA could threaten insurance coverage for >23 million Americans. Although healthcare providers often invoke the idea that health insurance coverage is a "moral imperative," this perspective is not universally shared by national policymakers. Compelling evidence that insurance coverage improves long-term health, particularly for vulnerable populations, may push policymakers to spend more time debating how to, rather than whether to, expand insurance coverage.Cardiologists have long advocated for a practice environment in which robust data are actively translated into clinical practice, and we should adopt a similar stance for health policy. In a time of great uncertainty around health reform, we should acknowledge that evidence convincingly demonstrates that uninsurance is associated with adverse clinical outcomes. Simultaneously, we should work to provide additional evidence on the long-term impact of broadening insurance coverage on cardiovascular disease epidemiology, care quality and outcomes, and population health. It is critical that cardiologists be equipped with the best evidence base to engage, inform, and guide policymakers during periods of discussion and debate regarding national health policy.Sources of FundingDr Wadhera is partially supported by the Jerome H. Grossman, MD Fellowship in Healthcare Delivery Policy at the Harvard Kennedy School's Healthcare Policy Program. Dr Joynt receives research support from the National Heart, Lung, and Blood Institute (K23HL109177-03) and is a former employee of the US Department of Health and Human Services, where she continues work on a limited basis as a contractor.DisclosuresNone.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Circulation is available at http://circ.ahajournals.org.Correspondence to: Rishi K. Wadhera, MD, MPhil, Division of Cardiovascular Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115. E-mail [email protected]References1. McWilliams JM. Health consequences of uninsurance among adults in the United States: recent evidence and implications.Milbank Q. 2009; 87:443–494. doi: 10.1111/j.1468-0009.2009.00564.x.CrossrefMedlineGoogle Scholar2. Smolderen KG, Spertus JA, Tang F, Oetgen W, Borden WB, Ting HH, Chan PS. Treatment differences by health insurance among outpatients with coronary artery disease: insights from the national cardiovascular data registry.J Am Coll Cardiol. 2013; 61:1069–1075. doi: 10.1016/j.jacc.2012.11.058.CrossrefMedlineGoogle Scholar3. Canto JG, Rogers WJ, French WJ, Gore JM, Chandra NC, Barron HV. Payer status and the utilization of hospital resources in acute myocardial infarction: a report from the National Registry of Myocardial Infarction 2.Arch Intern Med. 2000; 160:817–823.CrossrefMedlineGoogle Scholar4. Kapoor JR, Kapoor R, Hellkamp AS, Hernandez AF, Heidenreich PA, Fonarow GC. Payment source, quality of care, and outcomes in patients hospitalized with heart failure.J Am Coll Cardiol. 2011; 58:1465–1471. doi: 10.1016/j.jacc.2011.06.034.CrossrefMedlineGoogle Scholar5. Sommers BD, Blendon RJ, Orav EJ, Epstein AM. Changes in utilization and health among low-income adults after Medicaid expansion or expanded private insurance.JAMA Intern Med. 2016; 176:1501–1509. doi: 10.1001/jamainternmed.2016.4419.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited ByKyalwazi A, Loccoh E, Brewer L, Ofili E, Xu J, Song Y, Joynt Maddox K, Yeh R and Wadhera R (2022) Disparities in Cardiovascular Mortality Between Black and White Adults in the United States, 1999 to 2019, Circulation, 146:3, (211-228), Online publication date: 19-Jul-2022. Warraich H, Kumar P, Nasir K, Joynt Maddox K and Wadhera R (2022) Political environment and mortality rates in the United States, 2001-19: population based cross sectional analysis, BMJ, 10.1136/bmj-2021-069308, (e069308) Essa M, Ghajar A, Delago A, Hammond-Haley M, Shalhoub J, Marshall D, Salciccioli J, Sugeng L, Philips B and Faridi K (2022) Demographic and State-Level Trends in Mortality Due to Ischemic Heart Disease in the United States from 1999 to 2019, The American Journal of Cardiology, 10.1016/j.amjcard.2022.02.016, 172, (1-6), Online publication date: 1-Jun-2022. Oseran A, Sun T and Wadhera R (2022) Health Care Access and Management of Cardiovascular Risk Factors Among Working-Age Adults With Low Income by State Medicaid Expansion Status, JAMA Cardiology, 10.1001/jamacardio.2022.1282 Slavin S, Khera R, Zafar S, Nasir K and Warraich H (2021) Financial burden, distress, and toxicity in cardiovascular disease, American Heart Journal, 10.1016/j.ahj.2021.04.011, 238, (75-84), Online publication date: 1-Aug-2021. Glance L, Thirukumaran C, Shippey E, Lustik S, Dick A and Deo S (2020) Impact of medicaid expansion on disparities in revascularization in patients hospitalized with acute myocardial infarction, PLOS ONE, 10.1371/journal.pone.0243385, 15:12, (e0243385) Wadhera R, Joynt Maddox K, Fonarow G, Zhao X, Heidenreich P, DeVore A, Matsouaka R, Hernandez A, Yancy C and Bhatt D (2018) Association of the Affordable Care Act's Medicaid Expansion With Care Quality and Outcomes for Low-Income Patients Hospitalized With Heart Failure, Circulation: Cardiovascular Quality and Outcomes, 11:7, Online publication date: 1-Jul-2018. Wadhera R (2017) Cardiovascular Medicine Amid Evolving Health Policy, Journal of the American College of Cardiology, 10.1016/j.jacc.2017.09.017, 70:17, (2201-2204), Online publication date: 1-Oct-2017. May 23, 2017Vol 135, Issue 21 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.117.028618PMID: 28473447 Originally publishedMay 4, 2017 KeywordsinsuranceAffordable Care Actcardiovascularoutcomeshealth policyPDF download Advertisement

Background: Public reporting of PCI outcomes is associated with risk-averse use of PCI and inferior outcomes. Contemporary data about how public reporting impacts interventional cardiologists’ (IC)...

The United States is entrenched in a fierce debate over healthcare reform. The Affordable Care Act (ACA) strove to reduce the number of uninsured individuals, and after its implementation, 20 million Americans gained insurance coverage. Ongoing shifts in health policy imperil these gains. As options for repealing, replacing, or revising the ACA are debated, we aim to outline what is known about the relationship between insurance coverage and cardiovascular care, the impact of the ACA on cardiovascular care, and areas where gaps in our knowledge remain. Understanding these relationships may help clinical leaders and policymakers better craft future policy initiatives.

Introduction: There is increasing policy focus on reducing costs of care for acute myocardial infarction (AMI). However, the degree to which hospital-level variation in 30-day spending during and i...

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Cardiology HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Cardiology journal

A new report shows that Medicare beneficiaries with social risk factors had worse outcomes than other beneficiaries on many quality measures, and providers that disproportionately served such beneficiaries tended to have worse performance on quality measures.

OBJECTIVES:To examine trends in hospital post-acute utilization indicators and to determine whether improvement in these indicators is associated with attesting to meaningful use (MU). DATA SOURCES:Medicare claims-based, repeated measures on 30-day hospital-wide all-cause readmission and emergency department (ED) utilization rates for 160 short-stay hospitals (2009-2012); Medicare EHR Incentive Program Payments files (2011-2012); and other hospital and market data. STUDY DESIGN:Interrupted time series with concurrent comparison group. PRINCIPAL FINDINGS:Propensity score-weighted multilevel models for change demonstrate that 30-day readmission rates (unadjusted) fell from 13.4 percent in 2009 to 12.1 percent in 2012. Similarly, 30-day ED utilization declined from 18.9 percent to 17.3 percent during the same period. However, MU and non-MU hospitals were indistinguishable vis-à-vis performance. Controlling for hospital and market characteristics, MU was unrelated to 30-day readmission. In contrast, 30-day ED utilization deteriorated. CONCLUSIONS:Hospitals with MU Stage 1 designation did not show significantly higher improvement on post-acute utilization compared to their counterparts without. To achieve gains in quality and safety, potentially associated with EHRs, and to advance care coordination and patient engagement, the regulators should strengthen accountability by linking comprehensive, outcomes-based performance measures to specific MU objectives.

Objective: US hospitals that care for vulnerable populations, “safety-net hospitals” (SNHs), are more likely to incur penalties under the Hospital Readmissions Reduction Program, which penalizes hospitals with higher-than-expected readmissions. Understanding whether SNHs face unique barriers to reducing readmissions or whether they underuse readmission-prevention strategies is important. Design: We surveyed leadership at 1600 US acute care hospitals, of whom 980 participated, between June 2013 and January 2014. Responses on 28 questions on readmission-related barriers and strategies were compared between SNHs and non-SNHs, adjusting for nonresponse and sampling strategy. We further compared responses between high-performing SNHs and low-performing SNHs. Results: We achieved a 62% response rate. SNHs were more likely to report patient-related barriers, including lack of transportation, homelessness, and language barriers compared with non-SNHs ( P -values<0.001). Despite reporting more barriers, SNHs were less likely to use e-tools to share discharge summaries (70.1% vs. 73.7%, P <0.04) or verbally communicate (31.5% vs. 39.8%, P <0.001) with outpatient providers, track readmissions by race/ethnicity (23.9% vs. 28.6%, P <0.001), or enroll patients in postdischarge programs (13.3% vs. 17.2%, P <0.001). SNHs were also less likely to use discharge coordinators, pharmacists, and postdischarge programs. When we examined the use of strategies within SNHs, we found trends to suggest that high-performing SNHs were more likely to use several readmission strategies. Conclusions: Despite reporting more barriers to reducing readmissions, SNHs were less likely to use readmission-reduction strategies. This combination of higher barriers and lower use of strategies may explain why SNHs have higher rates of readmissions and penalties under the Hospital Readmissions Reduction Program.

Disparities by economic status are observed in the health status and health outcomes of Medicare beneficiaries. For health services and health policy researchers, one barrier to addressing these disparities is the ability to use Medicare data to ascertain information about an individual's income level or poverty, because Medicare administrative data contains limited information about individual economic status. Information gleaned from other sources-such as the Medicaid and Supplemental Security Income programs-can be used in some cases to approximate the income of Medicare beneficiaries. However, such information is limited in its availability and applicability to all beneficiaries. Neighborhood-level measures of income can be used to infer individual-level income, but level of neighborhood aggregation impacts accuracy and usability of the data. Community-level composite measures of economic status have been shown to be associated with health and health outcomes of Medicare beneficiaries and may capture neighborhood effects that are separate from individual effects, but are not readily available in Medicare data and do not serve to replace information about individual economic status. There is no single best method of obtaining income data from Medicare files, but understanding strengths and limitations of different approaches to identifying economic status will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using measures of income.

Article, see p 1897 Public reporting of outcomes for percutaneous coronary intervention (PCI) has been taking place since New York initiated such a program in the 1990s. Pennsylvania followed suit in 2000, and Massachusetts shortly thereafter.1 Over this time, public reporting has been the subject of a great deal of controversy, with supporters arguing that public reporting drives critical improvements in care, and detractors arguing that it drives risk aversion and the denial of procedures to patients who may stand to benefit from receiving PCI.2 In this issue of Circulation , Waldo et al3 put one specific element of public reporting under a microscope, with somewhat surprising results. The authors examine what happened to hospitals that were found to be outliers in mortality rates as part of the public reporting programs in Massachusetts and New York. This was a not-uncommon phenomenon, with 31 hospitals (36%) identified as outliers over the study period. Contrary to expectations, Waldo et al report that hospitals that were identified as negative outliers during the study period did not limit the PCIs they subsequently performed, demonstrating similar growth in PCI rates as nonoutlier hospitals. In addition, and perhaps even more important, hospitals identified as outliers demonstrated a significant reduction in mortality rates following identification as outliers, a benefit that was concentrated in patients receiving PCI. Taken together, these findings suggest that being identified as an outlier was associated with improvements in care quality. To be clear, the present study does not answer the question of whether public reporting is associated with better outcomes than not having public reporting, nor whether …

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Cardiology HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Cardiology journal

OBJECTIVES: To determine the opinions of US hospital leadership on the Hospital Readmissions Reduction Program (HRRP), a national mandatory penalty-for-performance program.STUDY DESIGN: We developed a survey about federal readmission policies. We used a stratified sampling design to oversample hospitals in the highest and lowest quintile of performance on readmissions, and hospitals serving a high proportion of minority patients.METHODS: We surveyed leadership at 1600 US acute care hospitals that were subject to the HRRP, and achieved a 62% response rate. Results were stratified by the size of the HRRP penalty that hospitals received in 2013, and adjusted for nonresponse and sampling strategy.RESULTS: Compared with 36.1% for public reporting of readmission rates and 23.7% for public reporting of discharge processes, 65.8% of respondents reported that the HRRP had a "great impact" on efforts to reduce readmissions. The most common critique of the HRRP penalty was that it did not adequately account for differences in socioeconomic status between hospitals (75.8% "agree" or "agree strongly"); other concerns included that the penalties were "much too large" (67.7%), and hospitals' inability to impact patient adherence (64.1%). These sentiments were each more common in leaders of hospitals with higher HRRP penalties.CONCLUSIONS: The HRRP has had a major impact on hospital leaders' efforts to reduce readmission rates, which has implications for the design of future quality improvement programs. However, leaders are concerned about the size of the penalties, lack of adjustment for socioeconomic and clinical factors, and hospitals' inability to impact patient adherence and postacute care. These concerns may have implications as policy makers consider changes to the HRRP, as well as to other Medicare value-based payment programs that contain similar readmission metrics.

HomeCirculation: Cardiovascular Quality and OutcomesVol. 9, No. 5Assessing Hospital Performance for Emerging Technologies Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBAssessing Hospital Performance for Emerging TechnologiesEssential but Elusive Nihar R. Desai, MD, MPH and Karen E. Joynt, MD, MPH Nihar R. DesaiNihar R. Desai From the Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT (N.R.D.); Center for Outcomes Research and Evaluation, New Haven, CT (N.R.D.); Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA (K.E.J.); and Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA (K.E.J.). Search for more papers by this author and Karen E. JoyntKaren E. Joynt From the Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT (N.R.D.); Center for Outcomes Research and Evaluation, New Haven, CT (N.R.D.); Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA (K.E.J.); and Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA (K.E.J.). Search for more papers by this author Originally published1 Sep 2016https://doi.org/10.1161/CIRCOUTCOMES.116.003187Circulation: Cardiovascular Quality and Outcomes. 2016;9:498–500Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2016: Previous Version 1 We are in the midst of a technological revolution in cardiology. Over the past decade, new devices, tools, and techniques have proliferated, changing the face of cardiovascular care. The ability to relieve the hemodynamic burdens of a severely stenotic aortic valve without the need for cardiopulmonary bypass or a sternotomy with transcatheter aortic valve implantation has been heralded as an example of the best of modern medical science. Several large, randomized trials have established the efficacy and safety of transcatheter aortic valve replacement (TAVR) relative to medical therapy or surgical aortic valve replacement.1,2 This procedure has impacted hundreds of thousands of people with previously inoperable disease or with increased operative risk, offering both symptom relief and additional years of life. TAVR has spread rapidly, is now widely available across the United States, and the platform is iteratively improving to become safer and better. Although the experience in the United States is in its relative infancy, experience in Europe foreshadows continued expansion of TAVR to intermediate and low-risk patients with several randomized trials reported3 or currently underway.4Article, see p 560Concurrently, we are in the midst of an information revolution with growing demands for quality reporting, transparency, and performance assessment. The development and implementation of public reporting on Hospital Compare, value-based payment programs such as the Hospital Value Based Purchasing program, and alternative payment models such as accountable care organizations have grown out of Medicare and other payers’ desire to more closely align reimbursement with the quality of care delivered and the outcomes achieved.5 At the same time, healthcare consumers are increasingly willing and able to seek and interpret quality information, with Hospital Compare being joined by US News and World Report, Leapfrog, and Consumer Reports to deliver quality information to the public.The worlds of technological advancement and information advancement collide in the article by O’Brien et al6 and highlight both the importance and limitations of creating robust methods by which to judge hospital performance on emerging technologies. Postmarket surveillance of the real-world utilization, efficacy, and safety of novel technologies is essential. Hospitals performing TAVR are subject to a mandate to collect data on all patients who receive a Food and Drug Administration–approved transcatheter valve as a condition for Medicare reimbursement. The Society of Thoracic Surgeons—American College of Cardiology Transcatheter Valve Therapy Registry, which was developed in 2011 at the time of the first approval of TAVR for commercial use, not only satisfies this regulatory requirement but also provides participating hospitals benchmarked reports that can be used to support performance improvement initiatives. Consequently, developing risk models appropriate for judging hospital performance on TAVR have become a priority. Typically, hierarchical modeling that includes both patient-level covariates and a hospital-specific intercept are used for such purposes; these models can adjust for differences in case-mix and account for the clustering of patients within a site, and in doing so, provide an estimate of the hospital’s quality that can then be used for comparative performance assessment.The devil, however, is in the details. Both the opportunities and challenges of hierarchical modeling and hospital-level performance measurement are highlighted by O’Brien et al.6 To build a model suitable for assessing hospital performance, investigators included 22 248 TAVR procedures across 318 sites. The outcome of interest was in-hospital mortality, and the model included 40 covariates based on clinical relevance, data availability and quality, and variation across sites. Notably, frailty and patient-reported functional status, two powerful predictors of mortality after TAVR, were missing too frequently to be included.7,8 The mortality model demonstrated modest predictive capacity with a C statistic of 0.71 for the overall sample. The authors found >2-fold variation in the range of hospital risk-adjusted mortality rates (3.4%–7.7%) with an interquartile range of 4.8% to 5.4%. Still further, the authors report that a patient’s predicted odds of dying was 80% higher if they were treated by a hospital 1 SD above the mean when compared with a hospital 1 SD below the mean (odds ratio, 1.8; 95% confidence interval, 1.4–2.2). Such significant differences in outcomes, even after risk adjustment, illustrate the importance of such hospital comparisons, not only for performance improvement but also ultimately for public reporting, pay for performance, and consumer choice, as well as for the prompt detection of any safety signals as this procedure continues to spread more broadly.Or does it? O’Brien et al6 also report that the model only identified 1 hospital, of the 316 examined, as having a mortality rate that were statistically significantly different from expected6; certainly, this limits the model’s utility. If all hospitals are average, where are the opportunities for improvement? How can patients, providers, and payers make relevant decisions if the statistical methodology ascribes virtually all hospitals the same performance category?This study illuminates a central challenge—although hospital quality assessment may be essential, in many ways it remains elusive, particularly for emerging technologies where sample sizes are small and adverse events are relatively rare. Hierarchical modeling approaches such as the one used by O’Brien et al6 have many advantages and are used by The Centers for Medicare and Medicaid Services for public reporting on Hospital Compare for many conditions. Beyond the ability to adjust for differences in clinical characteristics of patients across hospitals and account for the clustered nature of the data within hospitals, this methodology can also account for differences in the number of observations across sites.9,10 Hospitals with a relatively small number of cases may have crude estimates of performance at the extremes (either worse or better performance). However, these may not represent the most accurate estimates of true performance. Although many statistical approaches simply exclude low-volume hospitals, this only creates a blind spot in the performance measurement system. Instead, hierarchical models can generate estimates of hospital variation in performance while also accounting for the uncertainty that comes with smaller volumes. Another important advantage from a public reporting standpoint is their conservatism—these models require a high degree of difference between observed and expected performance to achieve statistical significance and therefore are unlikely to accidentally penalize an average performer based on random variation in performance. This is perhaps wise for pay-for-performance applications, where financial stakes may be high.On the other hand, because these models are highly sensitive to volume and tend to characterize low-volume centers as average performers, clinically meaningful variation in performance may be obscured.11 For the goal of supporting quality improvement efforts and fostering patient decision making, this represents a significant limitation, particularly relevant for emerging technologies where there is often a significant learning curve and where the lowest-volume centers may be poor performers. Indeed, previous literature supports a relationship between volume and outcomes for many invasive procedures12; if such a relationship exists for TAVR, hierarchical models may not adequately stratify hospital performance by inappropriately crediting low-volume centers. This could hinder efforts at quality improvement and negatively impact transparency for consumer choice.Hospital performance assessment is critical, but challenging. As payers’ and consumers’ desire for performance information continues to grow, and as new technologies continue to emerge, these issues will only become more salient. As ablation techniques for atrial fibrillation, left atrial appendage occlusion, peripheral arterial interventions, mitral valve clipping, and other procedures continue to expand in frequency and complexity, the unique needs of patients, providers, and payers for rapid and rigorous measurement of hospital performance need to be considered and addressed. Cardiology is, and ought to remain, at the leading edge, grappling with these difficult issues as we continue to find new ways to improve our patients’ health and outcomes.Sources of FundingDr Desai was supported by grant K12 HS023000-01 from the Agency for Healthcare Research and Quality.DisclosuresDr Desai receives support from the Centers for Medicare and Medicaid Services to develop and maintain hospital performance measures that are used for public reporting and payment programs and research support from Johnson & Johnson, through Yale University, to develop methods of clinical trial data sharing. Dr Joynt currently serves as a senior advisor, US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to Karen E. Joynt, MD, MPH, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115. E-mail [email protected]References1. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, Tuzcu EM, Webb JG, Fontana GP, Makkar RR, Brown DL, Block PC, Guyton RA, Pichard AD, Bavaria JE, Herrmann HC, Douglas PS, Petersen JL, Akin JJ, Anderson WN, Wang D, Pocock S; PARTNER Trial Investigators. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery.N Engl J Med. 2010; 363:1597–1607. doi: 10.1056/NEJMoa1008232.CrossrefMedlineGoogle Scholar2. Adams DH, Popma JJ, Reardon MJ, Yakubov SJ, Coselli JS, Deeb GM, Gleason TG, Buchbinder M, Hermiller J, Kleiman NS, Chetcuti S, Heiser J, Merhi W, Zorn G, Tadros P, Robinson N, Petrossian G, Hughes GC, Harrison JK, Conte J, Maini B, Mumtaz M, Chenoweth S, Oh JK; U.S. CoreValve Clinical Investigators. Transcatheter aortic-valve replacement with a self-expanding prosthesis.N Engl J Med. 2014; 370:1790–1798. doi: 10.1056/NEJMoa1400590.CrossrefMedlineGoogle Scholar3. Leon MB, Smith CR, Mack MJ, Makkar RR, Svensson LG, Kodali SK, Thourani VH, Tuzcu EM, Miller DC, Herrmann HC, Doshi D, Cohen DJ, Pichard AD, Kapadia S, Dewey T, Babaliaros V, Szeto WY, Williams MR, Kereiakes D, Zajarias A, Greason KL, Whisenant BK, Hodson RW, Moses JW, Trento A, Brown DL, Fearon WF, Pibarot P, Hahn RT, Jaber WA, Anderson WN, Alu MC, Webb JG; PARTNER 2 Investigators. Transcatheter or surgical aortic-valve replacement in intermediate-risk patients.N Engl J Med. 2016; 374:1609–1620. doi: 10.1056/NEJMoa1514616.CrossrefMedlineGoogle Scholar4. Waksman R. TAVR for patients with low risk for mortality is here for investigation.Cardiovasc Revasc Med. 2016; 17:1–2. doi: 10.1016/j.carrev.2016.01.001.CrossrefMedlineGoogle Scholar5. Burwell SM. Setting value-based payment goals–HHS efforts to improve U.S. health care.N Engl J Med. 2015; 372:897–899. doi: 10.1056/NEJMp1500445.CrossrefMedlineGoogle Scholar6. O’Brien SM, Cohen DJ, Rumsfeld JS, Brennan JM, Shahian DM, Dai D, Holmes DR, Hakim RB, Thourani VH, Peterson ED, Edwards FH, for the STS/ACC TVT Registry. Variation in hospital risk–adjusted mortality rates following transcatheter aortic valve replacement in the United States: A report from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry.Circ Cardiovasc Qual Outcomes. 2016; 9:560–565. doi: 10.1161/CIRCOUTCOMES.116.002756.LinkGoogle Scholar7. Alfredsson J, Stebbins A, Brennan JM, Matsouaka R, Afilalo J, Peterson ED, Vemulapalli S, Rumsfeld JS, Shahian D, Mack MJ, Alexander KP. Gait speed predicts 30-day mortality after transcatheter aortic valve replacement: results from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry.Circulation. 2016; 133:1351–1359. doi: 10.1161/CIRCULATIONAHA.115.020279.LinkGoogle Scholar8. Arnold SV, Spertus JA, Vemulapalli S, Dai D, O’Brien SM, Baron SJ, Kirtane AJ, Mack MJ, Green P, Reynolds MR, Rumsfeld JS, Cohen DJ. Association of patient-reported health status with long-term mortality after transcatheter aortic valve replacement: report from the STS/ACC TVT registry.Circ Cardiovasc Interv. 2015; 8:645–653. doi: 10.1161/CIRCINTERVENTIONS.115.002875.Google Scholar9. Normand S-LT, Glickman ME, Gatsonis CA. Statistical methods for profiling providers of medical care: Issues and applications.J Am Stat Assoc. 1997; 92:803–814.CrossrefGoogle Scholar10. Krumholz HM, Brindis RG, Brush JE, Cohen DJ, Epstein AJ, Furie K, Howard G, Peterson ED, Rathore SS, Smith SC, Spertus JA, Wang Y, Normand SL; American Heart Association; Quality of Care and Outcomes Research Interdisciplinary Writing Group; Council on Epidemiology and Prevention; Stroke Council; American College of Cardiology Foundation. Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation.Circulation. 2006; 113:456–462. doi: 10.1161/CIRCULATIONAHA.105.170769.LinkGoogle Scholar11. Silber JH, Rosenbaum PR, Brachet TJ, Ross RN, Bressler LJ, Even-Shoshan O, Lorch SA, Volpp KG. The Hospital Compare mortality model and the volume-outcome relationship.Health Serv Res. 2010; 45(5 pt 1):1148–1167. doi: 10.1111/j.1475-6773.2010.01130.x.CrossrefMedlineGoogle Scholar12. Hannan EL, Wu C, Walford G, King SB, Holmes DR, Ambrose JA, Sharma S, Katz S, Clark LT, Jones RH. Volume-outcome relationships for percutaneous coronary interventions in the stent era.Circulation. 2005; 112:1171–1179. doi: 10.1161/CIRCULATIONAHA.104.528455.LinkGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Coylewright M and Suri R (2019) Focusing National Policy on All Patients with Severe Aortic Stenosis: A Paradigm Shift, Structural Heart, 10.1080/24748706.2019.1619956, 3:4, (280-283), Online publication date: 1-Jul-2019. Carroll J, Vemulapalli S, Dai D, Matsouaka R, Blackstone E, Edwards F, Masoudi F, Mack M, Peterson E, Holmes D, Rumsfeld J, Tuzcu E and Grover F (2017) Procedural Experience for Transcatheter Aortic Valve Replacement and Relation to Outcomes, Journal of the American College of Cardiology, 10.1016/j.jacc.2017.04.056, 70:1, (29-41), Online publication date: 1-Jul-2017. Leprêtre P, Goosey-Tolfrey V, Janssen T and Perret C (2016) Editorial: Rio, Tokyo Paralympic Games and Beyond: How to Prepare Athletes with Motor Disabilities for Peaking, Frontiers in Physiology, 10.3389/fphys.2016.00497, 7 September 2016Vol 9, Issue 5 Advertisement Article InformationMetrics © 2016 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.116.003187PMID: 27625406 Originally publishedSeptember 1, 2016 Keywordstranscatheter aortic valve replacementEditorialsmodels, statisticaloutcome measuresPDF download Advertisement SubjectsCatheter-Based Coronary and Valvular InterventionsQuality and Outcomes

Introduction: The Hospital Readmission Reduction Program (HRRP) penalizes hospitals with higher than expected risk-adjusted 30-day readmission rates (excess readmission ratio [ERR]>1) after acute m...