Health equity measurement

Health equity means that people’s individual needs—including those connected to age, race, national original, religion, disability, sexual orientation, gender identity, gender expression, socioeconomic status, and more—have a negligible influence on health outcomes. Equitable health care institutions identify which populations have the best outcomes and strive to help all patients reach that level, in part by addressing social determinants of health and biases in care delivery that adversely affect systematically excluded or marginalized groups.

Measuring health equity can help leaders identify where the biggest opportunities lie, set principled priorities, instill accountability, and measure progress.

Provider organizations, health plans, and life science companies have a unique role to play in measuring and addressing health disparities. To produce meaningful health equity measurements, you need an intentional strategy. Failing to have a principled data strategy can have detrimental impacts on your organization and those you serve:

• No analysis: If health care leaders don’t use data identify their biggest opportunities to improve health equity, you are likely to focus on disparities that have come across your personal radar. Your resulting action plan may then fail to address greater disparities that have not come to your attention.
• Poorly designed analyses: Conducting analyses with poorly defined metrics, targets, and cohorts may introduce biases into your priorities, which could inadvertently perpetuate inequities. The same outcome can arise if you use a data set without understanding its limitations and the impact those have on your analyses.
• Overly selective analyses: Overly targeted or over-scoped analyses (for example, focusing on a narrow set of demographic groups, or just one part of the care pathway) may provide evidence to support one-off projects that don’t address the areas of greatest need.
• Getting lost in the data: Conducting too many analyses, waiting for perfect data to draw insights, or analyzing data without a plan to prioritize among findings can all lead to “analysis paralysis” and inaction.

Organizations first need to outline and prioritize which questions you need to answer and which analyses you will need to find those answers. Then, collect demographic and social determinants of health (SDOH) data needed for those analyses, including demographic data on race, ethnicity, gender identity & sexual orientation, and language (REGAL). One data team we spoke with suggested not starting any health equity measurements until you’ve collected the relevant data for at least 50% of your population of interest, be that your patients, members, or employees.

REGAL data is a starting point, but broadening efforts to include characteristics such as disability status or geography will lead to a more robust demographic profile. You should also strive to gather qualitative insights because they often add context to disparities that may not be apparent when analyzing quantitative data alone.

Once the data are collected, begin to conduct analyses to uncover disparities by stratifying relevant key performance indicators by REGAL and SDOH data. When doing so, analyze data with an intersectional lens to ensure that you are identifying the most vulnerable populations. Your analyses will likely uncover several disparities; prioritize those that align with your organization’s values, goals, and strategy for a meaningful, sustainable impact.

Set measurable short- and long-term goals centered around the disparities your organization wants to prioritize. Short-term goals build momentum and serve as stepping-stones to long-term goals which aim for transformational change.

Lastly, remember transparency and accountability are key. Leveraging tools like dashboards to democratize data can help measure progress and prevent silos from forming between important stakeholders, both internally and externally.