Whether quality measures should account for differences between clinicians who disproportionately care for patients with social risk factors, such as low income, unstable housing, limited transportation, few social supports, remains controversial. Recently published in JAMA Health Forum, a new study from Yale School of Medicine demonstrates a systematic approach of weighing the pros and cons of adjustment for social risk factors in a measure of clinician quality.
Kasia Lipska, MD, MHS, associate professor of medicine (endocrinology), and a team of researchers confronted the issue of social risk factor adjustment during the development of an outcome quality measure for the Merit-Based Incentive Payment System (MIPS), which makes payment adjustments based on ambulatory care quality measures. The team conducted a study of over 4.5 million older Medicare fee-for-service beneficiaries with multiple chronic conditions. Beneficiaries were assigned to clinicians or clinician groups based on the pattern of their outpatient visits. The quality of care was assessed using a measure of risk-standardized acute unplanned admission rates, and researchers evaluated how various social risk factors (including low Agency for Healthcare Research and Quality Socioeconomic index, low physician-specialist density, and Medicare-Medicaid dual eligibility) affected the rates of these unplanned hospital admissions.
How can policy makers decide when to adjust quality measures for social risk factors? The team laid out a structured approach that included active engagement of stakeholders, an evaluation of conceptual and contextual factors, as well as empirical analyses of the association of social risk factors with measure scores. This structured approach led to a consensus to adjust this measure for two social risk factors, the low Agency for Healthcare Research and Quality Socioeconomic index and the low physician-specialist density, but not for Medicare-Medicaid dual eligibility status.
Overall, this final model demonstrates how quantitative analyses, input from stakeholders, and contextual factors can merge to play a role in decision making surrounding social risk factor adjustment.
In addition to Lipska, other authors include Faseeha K. Altaf, MPH; Andrea G. B. Barthel, MS; Erica S. Spatz, MD, MHS; Zhenqiu Lin, PhD; Jeph Herrin, PhD; Susannah M. Bernheim, MD, MHS; and Elizabeth E. Drye, MD.
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