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Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
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1

Introduction

The Centers for Medicare & Medicaid Services (CMS) have been moving from volume-based, fee-for-service payment to value-based payment (VBP), which aims to improve health care quality, health outcomes, and patient care experiences, while also controlling costs. Since the passage of the Patient Protection and Affordable Care Act in 2010, CMS has implemented a variety of VBP strategies, including incentive programs and risk-based alternative payment models such as bundled (episode-based) payments and accountable care organizations (Burwell, 2015). Early evidence from these programs raised concerns about potential unintended consequences for health equity.1 Specifically, emerging evidence suggests that providers disproportionately serving patients with social risk factors for poor health outcomes (e.g., individuals with low socioeconomic position [SEP], racial and ethnic minorities, gender and sexual minorities, socially isolated persons, and individuals residing in disadvantaged neighborhoods) may be more likely to fare poorly on quality rankings and to receive financial penalties, and less likely to receive financial rewards (Berenson and Shih, 2012; Chien et al., 2007; Friedberg et al., 2010; Gilman et al., 2014, 2015; Joynt and Jha, 2013; Joynt and Rosenthal, 2012; Joynt et al., 2011; Karve et al., 2008; Ly et al., 2010; MedPAC, 2013; Mehta et al., 2008; Rajaram et al., 2015; Ryan, 2013; Shih et al., 2015; Sjoding and Cooke, 2014; Williams et al., 2014). However, an analysis of actual penalties incurred under the Hospital Readmissions Reduction Program for fiscal year 2013 reported that safety-net hospitals incurred only slightly higher penalties than non–safety-net hospitals (Sheingold et al., 2016).

The drivers of these disparities are poorly understood, and differences in interpretation have led to divergent concerns about the potential effect of VBP on health equity. Some suggest that underlying differences in patient characteristics (including clinical, behavioral, and social risk factors) that are out of the control of providers lead to differences in health outcomes (Jha and Zaslavsky, 2014; Joynt and Jha, 2013). In this view, because providers are being held financially accountable for differences in patient outcomes due to factors beyond their control and because providers disproportionately serving socially at-risk populations are historically less well funded than providers caring for the general population, VBP programs may be taking away resources from providers who need them most (Chien et al., 2007; Ryan, 2013). Moreover,

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1 Health equity means that every person has the opportunity to attain his or her full health potential and no one is disadvantaged from achieving this potential because of social position or other socially determined circumstances. A health disparity refers to a difference in a health outcome or a health determinant between populations (CDC, 2015).

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
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because socially at-risk populations may require more resources to achieve the same outcomes as the general population, increasing the resource gap may in turn increase health disparities (Bhalla and Kalkut, 2010; Ryan, 2013).

At the same time, others are concerned that differences in outcomes between providers serving socially at-risk populations and providers serving the general population reflect disparities in the provision of health care (Krumholz and Bernheim, 2014), because studies have shown that socially at-risk populations including racial and ethnic minorities, low-income persons, gender and sexual minorities, and other disadvantaged groups receive poorer quality health care, experience poorer health, and are more likely to receive care from lower-quality providers (Bach et al., 2004; Girotra et al., 2012; IOM, 2000, 2003, 2011; Jha et al., 2007, 2008, 2011; Popescu et al., 2009). In this view, VBP is a mechanism to hold those who provide lower-quality care accountable and to incentivize improvement (Bernheim, 2014). The reality of observed lower-quality care for socially at-risk populations is likely neither entirely beyond the control of payers and providers involved in their care nor entirely the result of lower capabilities or effort on the part of providers and payers. Thus, when considering the effect on health equity of VBP, there will always be an inherent tension between fairness to providers and improving health care and health outcomes for socially at-risk populations. This tension has led some to advocate for accounting for social risk factors in payment methods to promote fairness for providers, and spurred others to implement interventions to address social risk factors to improve health outcomes for socially at-risk populations. At the federal level, Congress passed the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014, which requires the Secretary of Health and Human Services to submit reports to Congress assessing the impact of and recommending methods to account for socioeconomic status on quality and resource use in Medicare. Additionally, CMS established the Accountable Health Communities initiative in 2016 to assess whether investing in interventions that address health-related social needs can improve health care utilization and costs among Medicare and Medicaid beneficiaries (Alley et al., 2016).

STATEMENT OF TASK

In an effort to better distinguish the drivers of variations in performance among providers disproportionately serving socially at-risk populations and to identify methods to account for social risk factors in Medicare payment programs, the Department of Health and Human Services acting through the Office of the Assistant Secretary of Planning and Evaluation (ASPE) contracted with the National Academies of Sciences, Engineering, and Medicine to convene an ad hoc committee to identify the best practices of high-performing hospitals, health plans, and other providers that serve disproportionately higher shares of socioeconomically disadvantaged populations and compare those best practices to practices of low-performing providers serving similar patient populations. The committee comprises expertise in health care quality, clinical medicine, health services research, health disparities, social determinants of health, risk adjustment, and Medicare programs (see Appendix B for biographical sketches). This report is the second in a series of five brief reports that aim to inform ASPE analyses that account for social risk factors in Medicare payment programs mandated through the IMPACT Act. In its first report (NASEM, 2016), the committee presented a conceptual framework and described the results of a literature search linking five social risk factors (SEP; race, ethnicity, and cultural context; gender; social relationships; and residential and community context) and health literacy

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×

to health-related measures of importance to Medicare payment and quality programs. Details of the statement of task and the sequence of reports can be found in Box 1-1. The committee will release reports every 3 months, addressing each item in the statement of task in turn. The statement of task requests committee recommendations only in the fourth report.

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×

COMMITTEE PROCESS AND APPROACH

The statement of task contains two key elements: identifying high- and low-performing hospitals, health plans, and other providers (hereafter referred to simply as providers) disproportionately serving socially at-risk populations and identifying best practices of the high-performing providers. The committee reviewed publicly reported performance of hospitals and health plans relevant to the Medicare population to attempt to identify high performers disproportionately serving socially at-risk populations—the Medicare Hospital Compare hospital data and the Medicare Advantage and Medicare Part D Star Ratings for health plan data (CMS, 2015; Medicare.gov, n.d.). The committee also reviewed the published literature examining the performance of providers disproportionately serving socially at-risk populations, including studies of variations in performance among these providers and comparisons to providers serving the general population.

To identify best practices of providers disproportionately serving socially at-risk populations, the committee considered both the published and grey literature. The published literature reviewed focused on targeted innovations, interventions, and other improvement strategies implemented by providers known to disproportionately serve socially at-risk populations—minority-serving institutions, safety-net hospitals, critical access hospitals, and community health centers. Because the committee expected that much of the literature on best practices would exist in the grey literature, it reached out to organizations known to conduct research or represent providers disproportionately serving socially at-risk populations (Alliance of Community Health Plans, America’s Essential Hospitals, America’s Health Insurance Plans, and The Commonwealth Fund) and asked for help identifying relevant case studies, especially those that are not within the peer-reviewed published literature. These organizations submitted 60 case studies for the committee’s consideration.

As will be described in detail in the next and final chapter, the committee identified key themes and commonalities in practices that were shown to improve health care quality and health outcomes for socially at-risk populations in specific provider settings and in specific community contexts.

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×

REFERENCES

Alley, D. E., C. N. Asomugha, P. H. Conway, and D. M. Sanghavi. 2016. Accountable health communities—addressing social needs through Medicare and Medicaid. New England Journal of Medicine 374(1):8–11.

Bach, P. B., H. H. Pham, D. Schrag, R. C. Tate, and J. L. Hargraves. 2004. Primary care physicians who treat blacks and whites. New England Journal of Medicine 351(6):575–584.

Berenson, J., and A. Shih. 2012. Higher readmissions at safety-net hospitals and potential policy solutions. Issue Brief (Commonwealth Fund) 34:1–16.

Bernheim, S. M. 2014. Measuring quality and enacting policy: Readmission rates and socioeconomic factors. Circulation: Cardiovascular Quality and Outcomes 7(3):350–352.

Bhalla, R., and G. Kalkut. 2010. Could medicare readmission policy exacerbate health care system inequity? Annals of Internal Medicine 152(2):114–117.

Burwell, S. M. 2015. Setting value-based payment goals—HHS efforts to improve U.S. health care. New England Journal of Medicine 372(10):897–899.

CDC (Centers for Disease Control and Prevention). 2015. Health equity. http://www.cdc.gov/chronicdisease/healthequity (accessed February 16, 2016).

Chien, A. T., M. H. Chin, A. M. Davis, and L. P. Casalino. 2007. Pay for performance, public reporting, and racial disparities in health care: How are programs being designed? Medical Care Research and Review 64(5 Suppl):283s–304s.

CMS (Centers for Medicare & Medicaid Services). 2015. Part C and D performance data. https://www.cms.gov/medicare/prescription-drugcoverage/prescriptiondrugcovgenin/performancedata.html (accessed February 5, 2016).

Friedberg, M. W., D. G. Safran, K. Coltin, M. Dresser, and E. C. Schneider. 2010. Paying for performance in primary care: Potential impact on practices and disparities. Health Affairs 29(5):926–932.

Gilman, M., E. K. Adams, J. M. Hockenberry, I. B. Wilson, A. S. Milstein, and E. R. Becker. 2014. California safety-net hospitals likely to be penalized by ACA value, readmission, and meaningful-use programs. Health Affairs 33(8):1314–1322.

Gilman, M., E. K. Adams, J. M. Hockenberry, A. S. Milstein, I. B. Wilson, and E. R. Becker. 2015. Safety-net hospitals more likely than other hospitals to fare poorly under Medicare’s value-based purchasing. Health Affairs 34(3):398–405.

Girotra, S., P. Cram, and I. Popescu. 2012. Patient satisfaction at America’s lowest performing hospitals. Circulation: Cardiovascular Quality and Outcomes 5(3):365–372.

IOM (Instiute of Medicine). 2000. America’s health care safety net: Intact but endangered. Washington, DC: National Academy Press.

IOM. 2003. Unequal treatment: Confronting racial and ethnic disparities in health care. Washington, DC: The National Academies Press.

IOM. 2011. The health of lesbian, gay, bisexual, and transgender people: Building a foundation for better understanding. Washington, DC: The National Academies Press

Jha, A. K., and A. M. Zaslavsky. 2014. Quality reporting that addresses disparities in health care. Journal of the American Medical Association 312(3):225–226.

Jha, A. K., E. J. Orav, Z. Li, and A. M. Epstein. 2007. Concentration and quality of hospitals that care for elderly black patients. Archives of Internal Medicine 167(11):1177–1182.

Jha, A. K., E. J. Orav, J. Zheng, and A. M. Epstein. 2008. The characteristics and performance of hospitals that care for elderly Hispanic Americans. Health Affairs 27(2):528–537.

Jha, A. K., E. J. Orav, and A. M. Epstein. 2011. Low-quality, high-cost hospitals, mainly in South, care for sharply higher shares of elderly black, Hispanic, and Medicaid patients. Health Affairs 30(10):1904–1911.

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×

Joynt, K. E., and A. K. Jha. 2013. A path forward on Medicare readmissions. New England Journal of Medicine 368(13):1175–1177.

Joynt, K. E., and M. B. Rosenthal. 2012. Hospital value-based purchasing: Will Medicare’s new policy exacerbate disparities? Circulation: Cardiovascular Quality and Outcomes 5(2):148–149.

Joynt, K. E., E. J. Orav, and A. K. Jha. 2011. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. Journal of the American Medical Association 305(7):675–681.

Karve, A. M., F. S. Ou, B. L. Lytle, and E. D. Peterson. 2008. Potential unintended financial consequences of pay-for-performance on the quality of care for minority patients. American Heart Journal 155(3):571–576.

Krumholz, H. M., and S. M. Bernheim. 2014. Considering the role of socioeconomic status in hospital outcomes measures. Annals of Internal Medicine 161(11):833–834.

Ly, D. P., L. Lopez, T. Isaac, and A. K. Jha. 2010. How do black-serving hospitals perform on patient safety indicators?: Implications for national public reporting and pay-for-performance. Medical Care 48(12):1133–1137.

Medicare.gov. n.d. The total performance score information. https://www.medicare.gov/hospitalcompare/data/total-performance-scores.html (accessed February 5, 2016).

MedPAC (Medicare Payment Advisory Commission). 2013. Chapter 4: Refining the hospital readmissions reduction program. In Report to the Congress: Medicare and the health care delivery system. Washington, DC: MedPAC.

Mehta, R. H., L. Liang, A. M. Karve, A. F. Hernandez, J. S. Rumsfeld, G. C. Fonarow, and E. D. Peterson. 2008. Association of patient case-mix adjustment, hospital process performance rankings, and eligibility for financial incentives. Journal of the American Medical Association 300(16):1897–1903.

NASEM (National Academies of Sciences, Engineering, and Medicine). 2016. Accounting for social risk factors in Medicare payment: Identifying social risk factors. Washington, DC: The National Academies Press.

Popescu, I., R. M. Werner, M. S. Vaughan-Sarrazin, and P. Cram. 2009. Characteristics and outcomes of America’s lowest-performing hospitals: An analysis of acute myocardial infarction hospital care in the United States. Circulation: Cardiovascular Quality and Outcomes 2(3):221–227.

Rajaram, R., J. W. Chung, C. V. Kinnier, C. Barnard, S. Mohanty, E. S. Pavey, M. C. McHugh, and K. Y. Bilimoria. 2015. Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services hospital-acquired condition reduction program. Journal of the American Medical Association 314(4):375–383.

Ryan, A. M. 2013. Will value-based purchasing increase disparities in care? New England Journal of Medicine 369(26):2472–2474.

Sheingold, S. H., R. Zuckerman, and A. Shartzer. 2016. Understanding Medicare hospital readmission rates and differing penalties between safety-net and other hospitals. Health Affairs 35(1):124–131.

Shih, T., A. M. Ryan, A. A. Gonzalez, and J. B. Dimick. 2015. Medicare’s hospital readmissions reduction program in surgery may disproportionately affect minority-serving hospitals. Annals of Surgery 261(6):1027–1031.

Sjoding, M. W., and C. R. Cooke. 2014. Readmission penalties for chronic obstructive pulmonary disease will further stress hospitals caring for vulnerable patient populations. American Journal of Respiratory and Critical Care Medicine 190(9):1072–1074.

Williams, K. A., Sr., A. A. Javed, M. S. Hamid, and A. M. Williams. 2014. Medicare readmission penalties in Detroit. New England Journal of Medicine 371(11):1077–1078.

Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
Page 11
Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
Page 12
Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
Page 13
Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
Page 14
Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
Page 15
Suggested Citation:"1 Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Systems Practices for the Care of Socially At-Risk Populations. Washington, DC: The National Academies Press. doi: 10.17226/21914.
×
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The Centers for Medicare & Medicaid Services (CMS) have been moving from volume-based, fee-for-service payment to value-based payment (VBP), which aims to improve health care quality, health outcomes, and patient care experiences, while also controlling costs. Since the passage of the Patient Protection and Affordable Care Act of 2010, CMS has implemented a variety of VBP strategies, including incentive programs and risk-based alternative payment models. Early evidence from these programs raised concerns about potential unintended consequences for health equity. Specifically, emerging evidence suggests that providers disproportionately serving patients with social risk factors for poor health outcomes (e.g., individuals with low socioeconomic position, racial and ethnic minorities, gender and sexual minorities, socially isolated persons, and individuals residing in disadvantaged neighborhoods) may be more likely to fare poorly on quality rankings and to receive financial penalties, and less likely to receive financial rewards.

The drivers of these disparities are poorly understood, and differences in interpretation have led to divergent concerns about the potential effect of VBP on health equity. Some suggest that underlying differences in patient characteristics that are out of the control of providers lead to differences in health outcomes. At the same time, others are concerned that differences in outcomes between providers serving socially at-risk populations and providers serving the general population reflect disparities in the provision of health care.

Systems Practices for the Care of Socially At-Risk Populations seeks to better distinguish the drivers of variations in performance among providers disproportionately serving socially at-risk populations and identifies methods to account for social risk factors in Medicare payment programs. This report identifies best practices of high-performing hospitals, health plans, and other providers that serve disproportionately higher shares of socioeconomically disadvantaged populations and compares those best practices of low-performing providers serving similar patient populations. It is the second in a series of five brief reports that aim to inform the Office of the Assistant Secretary of Planning and Evaluation (ASPE) analyses that account for social risk factors in Medicare payment programs mandated through the Improving Medicare Post-Acute Care Transformation (IMPACT) Act.

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