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Quality Matters: Risk Adjustment

Sharon Post
December 23, 2014
At the November 2014 Illinois Medicaid Advisory Committee meeting, the Department of Healthcare and Family Services announced that it had suspended auto-enrollment for three managed care entities. This action has led to increased interest in the technical details of quality measurement and in risk adjustment and stratification in particular. Even within Medicaid beneficiaries, there are variations in clinical severity, race, ethnicity, education level and income. To the extent that these differences affect quality outcomes, they should be factored into measures used to hold Medicaid contractors accountable, but never to create double standards for more vulnerable individuals. The space between accountability and double standards is where the dry topic of risk adjustment becomes fraught and contestable.

Within a month of the MAC meeting, Medicare announced penalties against hospitals for healthcare-associated conditions, and concerns were raised that hospitals that served the poorest, sickest, or most remote patients were hit hardest by penalties. Once again, questions arose about how to modify quality measurement calculations to be fair and avoid unintended, harmful  consequences to safety net providers.  As public and private payers expand value-based purchasing, we can expect more calls for adjustments to quality measurements to account for differences in patient characteristics.

Quality measurement is crucial to guiding health policy decisions. It depends on accurate, timely data and careful validation of the calculation and use of each measure. While we depend on researchers with technical expertise to develop measures, collect and evaluate data, and interpret the results, anyone who has a stake in the health care system has an interest in getting quality measurement right. As an organization with such a stake in health reform, and without claiming deep technical expertise, we offer this policy brief on risk adjustment of quality measures, the first in a series on health care quality measurement.

The series of policy briefs will examine particular issues in quality measurement and their policy implications. While we believe firmly that measurement matters and that good data should drive policy decisions, we also understand that building evidence-bases and perfecting data analysis rarely rise to the same level of urgency as other topics, like securing revenue to fund Medicaid services and protecting enrollees from barriers to necessary care. With that in mind we aim to make these papers topical and timely, responding to events that arise in the State as they come up.