Fighting health disparities: importance and impact for HEDIS

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Health disparities were among the many challenges brought to the fore during the COVID-19 pandemic. Recognizing the impact of COVID, the Centers for Disease Control and Prevention released a response outlining strategies to improve health outcomes for disproportionately affected populations.[1] However, health disparities and equity are not new issues. Mandated by Congress and supported by a U.S. Department of Health and Human Services interagency task force, the Agency for Healthcare Research and Quality (AHRQ) reported on progress and opportunities for improving the quality of health care and reducing health care disparities over the past 17 years. Their 2019 National Health Care Quality and Disparities Report encompasses reported patient care from 2016 to 2018 and shows that disparities still exist when comparing the experience of health care across races.[2]

Across all races reported against whites, the report shows 141 metrics where better care was received, 345 metrics where the same care was received, and 266 metrics where worse care was received.[3]

Note: The report also covers population disparities based on income stratification, insurance level, and residential demographics. We chose to extract race data to coincide with HEDIS measurement specifications.

NCQA’s response includes a phased approach

The National Committee for Quality Assurance (NCQA) has stated that high quality care is equitable care and there can be no quality without equity. They are committed to strengthening equity in all NCQA programs.[4] To help advance health equity, they “will play a vital role in providing the standards and data needed to identify disparities, enable positive change and measure results.”[5]

Specifically, the NCQA plans to enhance a variety of programs that cover health plan accreditation (HPA),[6] Distinction in Multicultural Health Care (MHC)[7] and the Healthcare Efficiency Data and Information Set (HEDIS®[8])[9] in these areas:

  • Identify and test standards that assess whether health plans have the structures and processes in place to help mitigate social risks and meet the health-related social needs of their members.

  • Develop and test performance measures for plans that assess whether members are screened for health-related and broader social needs, and whether and how their social needs are met.

  • Identify and test methods for measuring equity outcomes, such as developing benchmarks for equitable health outcomes for existing performance measures and examining approaches to using outcomes at the level community to evaluate and encourage the performance of the health plan.

The NCQA has developed a three-year phased approach to implementing racial and ethnic stratifications, with the stated goal of requiring all plans to measure performance by race and ethnicity using member data directly collected by the NCQA. measurement year (MY) 2024. [10] For MY 2022, the NCQA requires stratification by race and ethnicity for a subset of HEDIS metrics. Five metrics will be stratified into the 2022 marketing year, with a minimum of 15 metrics stratified by the 2024 marketing year.[11] The final measures cover the areas of Care Effectiveness, Access to Care and Utilization.[12]

Focusing on data remains vital

Data collected from members through surveys and assessments or from enrollment information provided by state Medicaid or federal agencies is considered direct data, the gold standard by NCQA standards. . Plans that have incomplete data through direct collection methods may use a reliable and valid method of indirect attribution of race and ethnicity.[13] Here are two examples of indirect data collection methods:

  • Indirect Bayesian Naming and Geocoding Method (BISG)

    • This method uses the member’s primary residence, geocoded to the census block group (FIPS 12) level, modified by ethnic surname lists and gender to infer race and ethnicity. [14],[15]

  • Geographic assignment to census tract unit

    • This method uses the member’s primary residence, geocoded to the census tract level (FIPS 11) to impute the predominant race and ethnicity of the neighborhood from the most recent microdata sample of the American Community Survey.[16],[17]

These race and ethnicity imputation methods have been validated for use at the population level (Note: this imputation would not be appropriate at the level of individual members) and will provide a bridge to give plans time to implement race and ethnicity collection processes. information from their members.

While the NCQA is taking promising steps to promote health equity, other organizations are also administering change. Get our report Driving Change in Health Equity: A Goal for the NCQA and Other Industry Stakeholders for a deeper dive into other industry efforts, including information on CMS’s health equity plan for Medicare and details on state initiatives that reflect the policy goals of NCQA and CMS.

Health care

HEDIS , CAQS , health equity

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