Reporting CAHPS and HEDIS Data by Race/Ethnicity for Medicare Beneficiaries

Public reporting on the quality of health plans and providers has become increasingly common as its advocates stress its potential to reduce costs and improve quality by informing consumer choice and motivating quality improvement initiatives (Berwick, James, and Coye 2003; Hibbard, Stockard, and Tusler 2003). Despite momentum in this area, evidence that public reports aid consumer decision making is mixed (Fung et al. 2008; Harris and Buntin 2008; Faber et al. 2009; Kolstad and Chernew 2009). For public reporting to support consumer decision making, consumers must be able to interpret and act on reported measures (Christianson et al. 2010; Hibbard, Greene, and Daniel 2010). To date, all public reporting of quality data has been based on care experienced by the average health plan enrollee or hospital patient. This practice makes sense if the experiences of the average enrollee or patient are applicable to all individuals using them for decision making. In practice, however, the best plans and providers for some groups may not be the best for all (Keenan et al. 2009; Elliott et al. 2011). Evidence that patient experience within a given plan or hospital may vary with patient characteristics has been particularly evident for racial/ethnic subgroups. For example, in an investigation of racial/ethnic differences in consumer assessments of commercial and Medicare managed care plans, Lurie et al. (2003) found substantial plan-to-plan variation in the gap between whites and other racial/ethnic groups in experience with, access to, and use of care. That is, whereas some plans showed no differences between whites and racial/ethnic minority groups on these variables, other plans showed large differences. Similarly, Trivedi et al. (2006) found substantial variation within as well as between Medicare plans in racial/ethnic disparities on each of four Healthcare Effectiveness Data and Information Set (HEDIS) outcome measures. More recently, Elliott et al. (2010) found that the relative standings of hospitals' patient experience scores were highly heterogeneous for patients of different race/ethnicity. Together, these findings suggest that a one-size-fits-all approach to public reporting based on the experiences of the average health plan enrollee or hospital patient might not be optimal for all purposes. Performance data stratified by race/ethnicity may have greater utility for consumers when they are selecting health plans and providers. Stratified performance reports could also raise awareness of racial/ethnic disparities in care and spur efforts to improve care for subgroups of patients who experience poorer access, treatment, and quality of care (Fiscella et al. 2000). Each of these goals is evident in the Medicare Improvements for Patients and Providers Act of 2008 (MIPPA), which proposed the collection of data for measuring, evaluating, and reporting on health disparities within the Medicare population. To the best of our knowledge, health care quality measures have not previously been publicly reported by race/ethnicity. Realizing the goals of stratified public reporting by race/ethnicity requires addressing some challenging issues (Elliott et al. 2010). One issue is how to achieve adequate precision of measurement without increasing sample size and accompanying costs. Although the sample sizes recommended in current performance reporting systems (e.g., the Consumer Assessment of Healthcare Providers and Systems [CAHPS] surveys) allow for accurate measurement of the average enrollee or patient's experiences by plan or hospital, they typically would not allow for accurate measurement of the experiences of specific racial/ethnic minority groups within plans/hospitals. This article describes how we dealt with this issue in an effort to produce and report racial/ethnic group-specific quality of care data for the MA and FFS Medicare beneficiary populations. In making recommendations about how to report these data, we also considered—although we did not investigate—challenges that consumers may face in interpreting and using racial/ethnic data on quality.