Reducing Over-Interviewing in the Anesthesiology Residency Match.

Background The U.S. residency recruitment process is expensive and time-consuming because of application inflation and over-invitation. Objective Using interview and match data, we quantify the predicted effects if anesthesiology residency programs excluded interviews for applicants who are very unlikely to match. Methods We previously published the validity and accuracy of the logistic regression model based on data from interview scheduling software used by 32 U.S. anesthesiology residency programs and 1300 applicants from 2015-18. Data used were program region, applicant address, numbers of interviews of the interviewee, medical school US News and World Report (USNWR) rank, the difference between United States Medical Licensing Exam (USMLE) Step 1 and 2 Clinical Knowledge (CK) scores, and the historical average of USMLE scores of program residents. In the current study completed in 2020, the predicted probabilities and their variances were summed among interviewees for 30 deidentified programs. Results For anesthesiology, the median residency program could reduce their interviews by 16.9% (97.5% confidence interval 8.5%-24.1%) supposing they would not invite applicants if the 99% upper prediction limit for the probability of matching was less than 10.0%. The corresponding median savings would be 0.80 interviews per matched spot (0.34-1.33). In doing so, the median program would sustain a risk of 5.3% (97.5% confidence interval 2.3%-7.9%) of having at least one interviewee removed from their final rank-to-match list. Conclusion Using novel interview data and analyses, we demonstrate that residency programs can substantively reduce interviews with less effect on rank-to-match lists. The data-driven approach to manage marginal interviews allows program leadership to better weigh costs and benefits when composing their annual list of interviewees.