Disparities in Missingness Produce Algorithmic Bias: Availability of Family Health History in Electronic Medical Records

• Activity Number: 10 - Recent Advances in Statistical Methods Applied to Racial Equity Research
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Melody Goodman*
• Companies: New York University
• Keywords: algorithm bias; racial disparities; family history; cancer; genetic counseling

Abstract

Prior research has shown that individuals from racial/ethnic minority groups have decreased access to and utilization of genetic services, and that these disparities cannot be attributed only to cost. Individual-level factors (e.g., awareness, knowledge, attitudes) have been identified, and the importance of health care system-level factors (e.g., insurance, access to specialists, language barriers) is increasingly being recognized. To identify patients eligible for genetic services, a critical piece of information is a detailed family history, one of the best predictors of cancer risks. However, family history information is often not adequately or routinely collected. Information about second-degree relatives and age at relatives’ disease diagnoses, required for risk stratification, are collected infrequently. The availability of family history information in the EHR can contribute to bias in algorithms. Some demographic groups are underrepresented in the pool identified by an EHR-based algorithm using structured data for identifying unaffected patients who qualify for cancer genetic services. We examine missingness by gender, race, ethnicity, and language.