Health data is critical for health care decision making and policy development. It can be used to determine a course of treatment, coverage eligibility, and even insurance premiums, but can be vulnerable to flaws like missing information or bias. The COVID-19 pandemic presents a notable example of the challenges and opportunities in leveraging health data. We relied on data to guide our pandemic response, however, research has shown that doing so may have perpetuated the inequities we witnessed. So, what happens if a decision-making tool learns from incomplete or biased data? Can it be reversed? This briefing provided a primer on health data, how we use it, and how we process it. Attendees learned the challenges of leveraging health data for decision-making and the implications for providers, payers, and patients during the COVID-19 pandemic. Panelists discussed how to ensure that data is translated into unbiased information and how we can catch, solve, and prevent flaws in data for more equitable care.
- Samantha Artiga, MHSA, Vice President and Director, Racial Equity and Health Policy Program, KFF
- Kadija Ferryman, Ph.D., Assistant Professor at John Hopkins Bloomberg School of Public Health
- Ziad Obermeyer, M.D., Blue Cross of California Distinguished Associate Professor, UC Berkeley School of Public Health
- Nadia J. Siddiqui, MPH, Chief Health Equity Officer, Texas Health Institute (moderator)
This was the final event in Part II of the Alliance for Health Policy’s 2021 Signature Series focused on health equity. See previous events in this series here.