The Centers for Medicare & Medicaid Services (CMS) are steadily moving from paying for volume (fee-for-service payments) to paying for quality, outcomes, and costs (value-based payment, or VBP) in the traditional Medicare program. Since Congress enacted the Patient Protection and Affordable Care Act of 2010, CMS has implemented a variety of value-based payment models including quality incentives and risk-based, alternative payment models (APMs) (Burwell, 2015). Quality incentives such as pay-for-performance schemes link financial rewards and penalties to the quality and efficiency of care provided. APMs such as episode-based (bundled) payments and accountable care organizations hold health care providers accountable for both the quality and cost of the care they deliver. In this report both types of strategies are referred to broadly as value-based payment.
A growing body of research has demonstrated that social risk factors (defined in the committee’s first report as socioeconomic position; race/ethnicity and cultural context; gender; social relationships; and residential and community context) as well as health literacy may influence health outcomes as much as—or more than—medical care does (Deaton, 2016; McGinnis, 2016; NASEM, 2016a; Woolf and Purnell, 2016). These findings are a concern for policymakers and health care providers because Medicare beneficiaries with social risk factors for poor health care outcomes are disproportionately concentrated among a subset of health care providers (Bach et al., 2004; Jha et al., 2007, 2008). Clustering of socially at-risk patients is often found in a small subset of providers (e.g., safety-net hospitals, critical access hospitals, minority-serving institutions, community health centers) (NASEM, 2016b). Note, the term provider in this report refers to the reporting unit (or, provider setting) being evaluated—e.g., hospitals, health plans, provider groups, etc.
A wide range of stakeholders has raised concerns that current Medicare quality measures and payment programs that financially reward or penalize providers based on the health care outcomes of their patients and do not account for social risk factors may underestimate the quality of care for such providers. Patients with social risk factors may require more resources and more intensive care to achieve certain health outcomes compared to the resources and care needed to achieve those same outcomes in more advantaged patients (NASEM, 2016b). At the same time, because these providers are also more likely to care for patients who are uninsured or covered by Medicaid, they have historically been less well funded than providers caring for larger proportions of patients with commercial insurance that pay more generously for care. If providers disproportionately serving vulnerable populations are likely to have fewer resources to
begin with and care for patients who require more resources to achieve the same health care outcomes, these providers may be more likely to fare poorly on quality rankings (Chien et al., 2007; Joynt and Rosenthal, 2012; Ryan, 2013). Indeed, evidence suggests hospitals disproportionately serving socially at-risk populations (safety-net, minority-serving, and critical access hospitals) perform worse on average on performance indicators used in VBP compared to hospitals serving the general population (NASEM, 2016b). However, there is also evidence of substantial variation among these providers such that some achieve performance on par with top performers among all hospitals (NASEM, 2016b). Additionally, evidence among ambulatory care providers disproportionately serving socially at-risk population is more mixed, with many performing as well as or better than their providers serving the general population (NASEM, 2016b).
The poorer average performance among providers disproportionately serving socially at-risk populations combined with the fact that they have fewer resources has raised concerns that Medicare’s VBP programs may potentially increase disparities. For one, the disproportionate penalties among providers disproportionately serving socially at-risk populations can be perceived as penalizing providers for caring for socially at-risk populations and may reduce incentives to keep doing so. Additionally, if these providers are more likely to have lower average performance, they may also be less likely to receive rewards and more likely to be penalized under VBP schemes compared to providers serving the general population. In this way, VBP programs may be taking resources from the very organizations who need them most (Chien et al., 2007; Ryan, 2013). In so doing, quality in these providers may worsen (Grealy, 2014; Ryan, 2013) and the organizations could also fail, further reducing access to care for socially at-risk patients (Lipstein and Dunagan, 2014).
Proposals to improve VBP programs to address these unintended consequences on health disparities recommend accounting for differences in patient social risk factors when measuring quality and calculating payment, also referred as risk adjustment or payment adjustment. As defined in the committee’s first report and discussed in more detail in Chapter 4 of this report, although the committee conceives of risk adjustment and payment adjustment as two separate methods, risk adjustment can become a method of payment adjustment when risk adjusted measures are used as the basis of payment. This proposal extends the rationale for adjusting for differences in clinical risk factors across providers to ensure accurate measurement and fair comparisons by taking into account differences that are beyond the control of individual providers (currently performed for all Medicare quality measures and payment programs) to also include social risk factors that may be beyond the control of providers (Girotti et al., 2014; Jha and Zaslavsky, 2014; Joynt and Jha, 2013; Pollack, 2013; Renacci, 2014).
Critics of such accounting are concerned that some forms of adjusting payments or quality measures for social risk factors may reduce incentives for providers who care for disadvantaged patients to improve the quality of care they provide to these patients (Bernheim, 2014; Kertesz, 2014). Critics of accounting for social risk factors also argue that adjusting measures would obscure health disparities, making it more difficult to hold providers accountable for lower-quality care and would also accept and potentially institutionalize a lower standard of care for socially at-risk populations (Bernheim, 2014; Jha and Zaslavsky, 2014; Kertesz, 2014; Krumholz and Bernheim, 2014; O’Kane, 2015). If the goal of value-based payment models is to improve quality and control costs while simultaneously enhancing health care equity and improving outcomes for disadvantaged patients, careful attention must be paid to
the delicate balance between adjusting payments and quality measures and preserving incentives to improve the care these patients receive.
These concerns draw attention to possible harms that may be introduced after accounting for social risk factors that would not otherwise exist. However, new harms that may arise from accounting for social risk factors are best considered in relation to the possible advantages and disadvantages that already exist under the status quo. Evaluating the benefits and disadvantages of accounting for social risk factors thus requires evaluating the likely effect of new methodologies on existing disparities in quality and access to care, to understand whether accounting methods are likely to exacerbate or diminish these disparities.
In response to concerns about health equity and accuracy in reporting and to the Improving Medicare Post-Acute Care Transformation (IMPACT) Act approved by Congress in 2014, the Department of Health and Human Services acting through the Office of the Assistant Secretary for Planning and Evaluation (ASPE) contracted with the National Academies of Sciences, Engineering, and Medicine to convene an ad hoc committee to identify criteria for selecting social risk factors, specific social risk factors Medicare could use, and methods of accounting for those factors in Medicare quality measurement and payment applications. 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 third 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 the first report, the committee presented a conceptual framework and described the results of a literature search linking five social risk factors and health literacy to health-related measures of importance to Medicare quality measurement and payment programs. In the second report, the committee reviewed the performance of providers disproportionately serving socially at-risk populations, discussed drivers of variations in performance, and identified six community-informed and patient-centered systems practices that show promise to improve care for socially at-risk populations. Details of the statement of task and the sequence of reports can be found in Box 1-1. The committee will release reports every three months, addressing each item in the statement of task in turn. The statement of task requests committee recommendations only in the fourth report.
In their first report, the committee laid out a conceptual framework that captures the relationships among social risk factors and health literacy and health care-related outcomes and other performance measures. This report builds on the conceptual relationships and empirical associations between social risk factors and health literacy and quality measures and health care outcomes identified in the first report to provide guidance on which factors could be considered for Medicare accounting purposes, criteria to identify these factors, and methods to do so in ways that can improve care and promote greater health equity for socially at-risk patients. To that end,
the committee also aims to address issues that must be carefully considered to maintain or enhance incentives for providers to improve care for socially at-risk patients throughout the report while also promoting accuracy in reporting and compensating providers fairly. The committee’s goals in accounting for social risk factors in Medicare payment programs are:
- Reducing disparities in access, quality, and outcomes;
- Quality improvement and efficient care delivery for all patients;
- Fair and accurate public reporting; and
- Compensating providers fairly.
To achieve these goals, accounting for social risk factors should neither mask low-quality care or health disparities nor reward poor performance. Additionally, inclusion of social risk factors in quality measurement and payment should not disincentivize providers from finding strategies to overcome the influence of social risk factors on health care outcomes.
In Chapter 2, the committee identifies criteria for selecting social risk factors that could be incorporated into Medicare quality measurement and payment programs along with the rationale for and potential challenges of each criterion. In Chapter 3, the committee applies the criteria identified in Chapter 2 to the social risk factors and their respective indicators identified in the committee’s first report. The committee also identifies the rationale for including these factors and indicators based on the criteria, as well as their limitations relative to those criteria. Chapter 4 presents an overview of current and planned Medicare VBP programs and how they currently account for social risk factors (if at all) and describes alternative methods of accounting for social risk factors in these programs.
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.
Bernheim, S. M. 2014. Measuring quality and enacting policy: Readmission rates and socioeconomic factors. Circulation: Cardiovascular Quality and Outcomes 7(3):350-352.
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.
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.
Deaton, A. 2016. On death and money: History, facts, and explanations. JAMA.
Girotti, M. E., T. Shih, and J. B. Dimick. 2014. Health policy update: Rethinking hospital readmission as a surgical quality measure. JAMA Surgery 149(8):757-758.
Grealy, M. R. 2014. Measure under consideration (MUC) comments: Letter to the National Quality Forum: Healthcare leadership council, December 5, 2014. http://www.hlc.org/wp-content/uploads/2014/06/HLC_Early-Public-Comment-on-Measures-Under-Consideration.pdf (accessed October 30, 2015).
Jha, A. K., and A. M. Zaslavsky. 2014. Quality reporting that addresses disparities in health care. JAMA 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 (Millwood) 27(2):528-537.
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.
Kertesz, K. 2014. Center for Medicare Advocacy comments on the impact of dual eligibily on MA and Part D quality scores. http://www.medicareadvocacy.org/center-for-medicareadvocacy-comments-on-the-impact-of-dual-eligibility-on-ma-and-part-d-quality-scores/ (accessed October 30, 2015).
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.
Lipstein, S. H., and W. C. Dunagan. 2014. The risks of not adjusting performance measures for sociodemographic factors. Annals of Internal Medicine 161(8):594-596.
McGinnis, J. M. 2016. Income, life expectancy, and community health: Underscoring the opportunity. JAMA.
NASEM (The National Academies of Sciences, Engineering, and Medicine). 2016a. Accounting for social risk factors in medicare payment: Identifying social risk factors. Washington, DC: The National Academies Press.
NASEM. 2016b. Systems practices for the care of socially at-risk populations. Washington, DC: The National Academies Press.
O’Kane, M. 2015. Comment on the advance notice of methodological changes for calender year 2016 for Medicare Advantage call letter. https://www.ncqa.org/PublicPolicy/CommentLetters/MedicareAdvantage032015.aspx (accessed November 3, 2015).
Pollack, R. 2013. CMS-1599-p, Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and proposed fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; medicare program; proposed rule (vol. 78, no. 91): Letter to the cms administrator tavenner. http://www.aha.org/advocacy-issues/letter/2013/130620-cl-cms-1599p.pdf (accessed October 30, 2015).
Renacci, J. B. 2014. Letter to HHS Secretary Burwell and CMS Administrator Tavenner regarding the Medicare hospital readmissions reduction program. http://tinyurl.com/q6shyoc (accessed October 30, 2015).
Ryan, A. M. 2013. Will value-based purchasing increase disparities in care? New England Journal of Medicine 369(26):2472-2474.
Woolf, S. H., and J. Q. Purnell. 2016. The good life: Working together to promote opportunity and improve population health and well-being. JAMA.