Health equity is the state in which everyone has a fair and just opportunity to be as healthy as possible.
Data, compiled and analyzed with health equity at the top of the agenda, are important for identifying problems, allocating resources, and targeting interventions for those who most need them.
But data also have pitfalls and must be collected and interpreted cautiously. Too often, data are incomplete. People also don’t know that they are contributing to large datasets or how all their input is being used. And data are not inherently objective; the algorithms used to harness and analyze vast amounts of information can exacerbate existing biases.
Three Ways to Use Data to Drive Health Equity:
- Local Data is key for running an analysis on your population. The fallacy of using large datasets based on aggregated data not unique to your population will further exacerbate biases. Instead, use the data you have collected on your population e.g. CCLF files, and HRSA. It’s a smaller data set but will be more accurate and more actionable for your population.
- Identifying which health equity barriers are most prevalent and in what regions is an important first step in addressing health equity. By doing this you become more focused on specific health equity benefits that will be more impactful to your patient population.
- Focusing on Interventions is the fastest way to make your health equity benefits actionable. HCOs are often trying to reduce the utilization of healthcare services e.g., unplanned hospital admissions, readmissions, ed utilization. Identifying which health barriers are affecting patients who consume more healthcare services than they should is the quickest way to prove if your program is working and if you’re saving money.
Take action to achieve health equity by using Spatially Health’s platform. Our focus on local data and targeted interventions helps healthcare organizations identify and address health equity barriers more accurately and effectively. Request a demo today to learn more.
