A novel algorithm to reduce bias and improve the quality and diversity of residency interviewees

Abstract

Abstract Objective Improve the quality and diversity of candidates invited for the Otolaryngology‐Head and Neck Surgery residency match by reducing geographical and inter‐rater bias with a novel geographic distribution algorithm. Methods Interview applicants were divided into geographic regions and assigned to reviewers. Each reviewer selected by force‐ranking a pre‐determined number of applicants to invite for interviews based on the percentage of applications received for each region. Our novel geographic distribution algorithm was then applied to maintain the geographic representation and underrepresented minority status of invited applicants to match the applicant pool. Results Analysis of previous interview selection methods demonstrated a statistically significant overrepresentation of local applicants invited for interviews. In 2022, 324 domestic applications were received for the otolaryngology match, which were divided into six geographic regions. There was no significant difference in USMLE scores between regions. The implementation of our distribution algorithm during applicant selection eliminated local overrepresentation in the invited pool of applicants and maintained the representation of underrepresented minority applicants. Following the match, reviewers indicated that implementation of the geographic distribution algorithm was simple and improved the quality and diversity of the group of interviewed applicants. Conclusion Traditional methods of scoring and inviting otolaryngology residency applicants can be confounded by regional and inter‐rater biases. Employing a geographic distribution algorithm improves the quality and diversity of invited applicants, eliminates bias, and maintains the representation of underrepresented minority applicants.

Keywords

• bias
• geographic
• otolaryngology‐head and neck surgery match
• underrepresented in medicine
• USMLE
• residency