Analysis of Electronic Residency Application Service (ERAS) Data Can Improve House Staff Diversity

Background: Training diverse house staff, including those who are underrepresented in medicine, is vital to provide high-quality patient care for the communities that we serve. In 2018, the Accreditation Council for Graduate Medical Education announced new common program requirements for systematic efforts to recruit and retain a diverse workforce. However, questions remain about how to implement such efforts.Materials and methods: Electronic Residency Application Service (ERAS) data from eight residency programs spanning two recruitment cycles (2017-2018, 2018-2019) was reviewed. The number of candidates at each stage in the process (applicant, invited to interview, interviewed, and matched) was examined by self-identified race or ethnicity. These data were presented to residency program directors at our Graduate Medical Education committee meeting before the next recruitment cycle. Data were analyzed following the 201920 residency match. Odds ratios and Pearson's chi-squared test were used to assess statistical significance.Results: A total of 10,445 and 10,982 medical students applied to our 8 core residency programs in 2017 and 2018, respectively. Medical students who applied and self-identified as Asian, Black or African American, and Hispanic or Latino or Spanish origin had lower odds of being invited to interview than those who self-identified as White. After data presentation, the odds of inviting Black or African American applicants to interview increased significantly. The odds of attending an interview once invited were the same across groups.Conclusions: Sharing ERAS data patterns with residency program directors was associated with a significant year over year change in interviewee diversity. Structured analysis of institutional ERAS data can provide insight into the resident selection process and may be a useful tool to improve house staff diversity. (c) 2020 Elsevier Inc. All rights reserved.