CC BY-NC-ND 4.0 · Journal of Academic Ophthalmology 2021; 13(02): e175-e182
DOI: 10.1055/s-0041-1735951
Research Article

Inefficiencies in Residency Matching Associated with Gale–Shapley Algorithms

Yue Wu
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Parisa Taravati
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Ryan T. Yanagihara
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Courtney E. Francis
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Marian Blazes
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Cecilia S. Lee
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Aaron Y. Lee*
1   Department of Ophthalmology, University of Washington, Seattle, Washington
,
Russell N. Van Gelder*
1   Department of Ophthalmology, University of Washington, Seattle, Washington
› Author Affiliations
Funding A.Y.E. reports grants from National Eye Institute, Novartis, Regeneron Santen, Carl Zeiss Meditec, Microsoft, and NVIDIA. He reported personal fees from U.S. Food and Drug Administration, Genentech, Topcon, and Verana Health outside the submitted work. was supported by the Research to Prevent Blindness, University of Washington CoMotion Innovation Fund (NEI/NIH K23EY029246), and supported in part by the Mark J. Daily MD Research Fund Research Fund. C.S.L. reported grants from National Institute on Aging outside the submitted work.

Abstract

Objective This study aimed to investigate emerging trends and increasing costs in the National Residency Matching Program (NRMP) and San Francisco Residency and Fellowship Match Services (SF Match) associated with the current applicant/program Gale–Shapley-type matching algorithms.

Design A longitudinal observational study of behavioral trends in national residency matching systems with modeling of match results with alternative parameters.

Patients and Methods We analyzed publicly available data from the SF Match and NRMP websites from 1985 to 2020 for trends in the total number of applicants and available positions, as well the average number of applications and interviews per applicant for multiple specialties. To understand these trends and the algorithms' effect on the residency programs and applicants, we analyzed anonymized rank list and match data for ophthalmology from the SF Match between 2011 and 2019. Match results using current match parameters, as well as under conditions in which applicant and/or program rank lists were truncated with finalized rank lists, were analyzed.

Results Both the number of applications and length of programs' rank lists have increased steadily throughout residency programs, particularly those with competitive specialties. Capping student rank lists at seven programs, or less than 80% of the average 8.9 programs currently ranked, results in a 0.71% decrease in the total number of positions filled. Similarly, capping program rank lists at seven applicants per spot, or less than 60% of the average 11.5 applicants ranked per spot, results in a 5% decrease in the total number of positions filled.

Conclusion While the number of ophthalmology positions in the United States has increased only modestly, the number of applications under consideration has increased substantially over the past two decades. The current study suggests that both programs and applicants rank more choices than are required for a nearly complete and stable match, creating excess cost and work for both applicants and programs. “Stable-marriage” type algorithms induce applicants and programs to rank as many counterparties as possible to maximize individual chances of optimizing the match.

Note

A.Y.L has received honoraria from Topcon, Genentech, and Verana Health. A.Y.L. works with the U.S. Food and Drug Administration.


* These authors contributed equally and should be considered co-senior authors.


Supplementary Material



Publication History

Received: 04 January 2021

Accepted: 22 June 2021

Article published online:
22 November 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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