Effect of Application Screening Methods on Racial and Ethnic Diversity in Otolaryngology

Racial and Ethnic Disparities in Otolaryngology Applicants and Residents: Recruiting a Diverse Workforce

OBJECTIVE

The purpose of this study is to examine the racial and ethnic representation of otolaryngology applicants and residents and determine if there have been any improvements in recruiting a diverse workforce over the past several years.

METHODS

A retrospective study was conducted on self-reported race and ethnicity data of otolaryngology applicants to United States residency programs from 2016 to 2022 from the Association of American Medical Colleges (AAMC) and residents from 2011 to 2022 from the Accreditation Council for Graduate Medical Education (ACGME). The changes in proportions of applicants and residents by race and ethnicity separately was compared as well as the proportion of applicants to residents. Descriptive analyses and chi-square tests were used to compare proportions of groups by race and ethnicity.

RESULTS

There was no statistically significant difference in the proportion of applicants by race or ethnicity from 2016-2017 to 2021-2022. There was a decrease in the proportion of White residents from 58.1% to 54.5% from 2011-2012 to 2018-2019. There were higher proportions of White residents than applicants and lower proportions of Black residents than applicants in the two cycles analyzed. In the most recent cycle analyzed, the proportion of Hispanic or Latino residents was higher than the proportion of Hispanic or Latino applicants.

CONCLUSION

While there may be improvements to increase representation of otolaryngology applicants and residents, there continues to be inequalities and a lack of diversity. Further initiatives are needed to ensure diversity in the field improves moving forward.

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

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.