This expert perspective reviews how Medicaid programs in Connecticut, Massachusetts and Rhode Island have engaged with commercial payers, providers, patients, advocates and other parties to create and adhere to multi-payer aligned measure sets. It describes the benefits to Medicaid agencies of participating in aligned measure set efforts, as well as tips and resources for Medicaid agencies intersted in measure alignment.
Analyzing Health Disparities in Medicaid Managed Care
On Wednesday, February 24, State Health and Value Strategies hosted a webinar on analyzing health disparities in Medicaid managed care. Health disparities are a key indicator of health equity and understanding health care disparities is a critical component of informing systems changes to improve health care outcomes. Stratifying performance data by race, ethnicity, disability, gender identity, or sexual orientation can inform targeted interventions to reduce health care disparities; yet many states lack complete and reliable data to do so. During the webinar, experts from Bailit Health discussed how states can use performance rates and disparities analyses from Medicaid managed care programs in other states to determine where disparities are likely to exist in their own state and develop interventions. Attendees also heard from Dr. Lisa Albers at the California Department of Health Care Services about California’s experience analyzing Medi-CAL HEDIS data to identify health care disparities and establish performance improvement expectations for Medi-CAL plans.
Bailit Health has developed a tool, the Quality Measure Disparities Resource, to provide consolidated health disparities data on a number of quality measures from a selection of state Medicaid programs where these data are available. Given the limited availability of health disparities data as they relate to quality measures across states, this resource was created to help those states lacking their own state-specific stratified quality data to understand where there might be common disparities in quality measures.