2025 Match Cycle: Decision Making with Data
- rupalvoramd
- Sep 4, 2024
- 3 min read
As the application (ERAS/ResidencyCAS/CentralApp) submission and transmission dates for applicants participating in the 2025 NRMP Main Residency Match quickly approaches, it is important to consider the vital role of data in individual decision making processes.
From individual applicant competitiveness for both one's specialty (+/- parallel specialty) and individual programs to factors such as the selection of geographic preferences, program fit identification, and program signal (+/- tiered) optimization, life determining decisions are being made. As both USMLE Step 1 and COMLEX Level 1 shifted to Pass/Fail, the objective measures in determining individual competitiveness have decreased by the removal of this previously significant metric and relatively early determinant of student competitiveness for certain specialties. As MS4/OMS4's have recently received or are receiving their 2CK/2CE scores, some have found themselves within the parameters and goals desired for their selected specialty. Others, however, are struggling with a disparate score that is not comparable to their previously determined aspirations and specialty expectations.
How can important decisions regarding how to best proceed now be made?
There is no clear answer, but data does exist that can help! Of note, objective metrics are not by any means definitive in ensuring match success. Having observed students who were "great on paper" with high board scores and an array of ECs encounter challenges with matching due to their interview performance, it is clear that there is not a magic calculation for match success; and both subjective and objective measures must be taken into account to gain a holistic picture of an applicant. However, the data compiled by organizations such as the American Association of Medical Colleges (AAMC) and the National Residency Matching Program (NRMP) can serve as tools to provide some insight into the likelihood of match success, especially based on certain identified parameters. For example, recent data released by the AAMC regarding the correlations between geographic prioritization and program signaling by specialty and specific programs translating into interview invitations is an excellent example of how data help an applicant make decisions with regard to selecting geographic regions and signaling. Similarly, the recent August releases of updated 2024 Charting Outcomes Data and the 2024 NRMP Program Director's Survey are additional examples of helpful available resources. Thus, in addition to utilizing AAMC's Residency Explorer, AAMC ERAS data, AAMC's Careers in Medicine, there are additional data repositories that can help guide students as they finalize their residency application decision making and future career plans.
Having served within AACOM and the AAMC in such efforts, it has been inspiring to see the continuing efforts put forth to assist in developing and ensuring transparency for all stakeholders: ranging from those serving within programs to medical schools to residency/fellowship applicants. It is with excitement and bated breath that I await medical education's evolution over time as both competency and data-based, with the added interplay of artificial intelligence. The hope is that all stakeholders (e.g. PDs/APDs, applicants) during each residency/fellowship cycle feel supported and have the tools to make their individual decisions to the best of their ability to ultimately ensure the optimal fit for both programs and newly incoming residents.
For now, sending wishes for all the best for applicants, schools, and programs during the current match cycle. And perhaps more importantly, wishing everyone peace and serenity during their individual decision making processes.
Take care,
Rupal S. Vora, M.D., MPH, FACP
MedStudentCoach LLC
Associate Clinical Professor, Creighton University School of Medicine, Internal Medicine
(Prior Assistant Dean Student Achievement, ATSU-SOMA)
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