Login / Signup

Impact of COVID-19 on the Ophthalmology Residency Home-Institution Match Rate.

Robert T SwanMisha F SyedKimberly W CrowderAndrew G Lee
Published in: Journal of academic ophthalmology (2017) (2022)
Purpose  The aim of this study was to evaluate the ophthalmology residency match results to determine changes in the rate of home-institution matches during the coronavirus disease 2019 (COVID-19) pandemic. Methods  Aggregate deidentified summary match result data from 2017 to 2022 was obtained from the Association of University Professors of Ophthalmology and the San Francisco (SF) Match. A chi-squared test was performed to determine if the rate of candidate matching to the home residency program in ophthalmology was higher in the post-COVID-19 compared with pre-COVID-19 match years. A literature review using PubMed was performed of other medical subspecialty match rates to home institution during the same study period. Results  A chi-squared test for difference in proportions confirmed a significantly higher chance of matching to the home program for ophthalmology in the post-COVID-19, SF Match year of 2021 to 2022 compared with 2017 to 2020 ( p  = 0.001). Other medical specialties including otolaryngology, plastic surgery, and dermatology also showed similar increased home institution residency match rates during the same time period. Although neurosurgery and urology also had increased trend rates for home institution match rates, these results did not reach statistical significance. Conclusions  The ophthalmology home-institution residency SF Match rate was significantly increased during the COVID-19 pandemic year 2021 to 22. This mirrors a trend reported in other specialties including the otolaryngology, dermatology, and plastic surgery in the 2021 match. Additional study will be required to identify factors leading to this observation.
Keyphrases
  • coronavirus disease
  • healthcare
  • sars cov
  • artificial intelligence
  • cataract surgery
  • medical students
  • machine learning
  • quality improvement
  • big data
  • electronic health record
  • case report