The Big Data Gap: Asymmetric Information in the Ophthalmology Residency Match Process and the Argument for Transparent Residency Data

**Background:** The ophthalmology residency match has become increasingly competitive, with match rates dropping from 78% in 2020 to 69% in 2023, while the number of applicants rose 18% and available spots only increased by 3%. Applicants are submitting more applications (87% increase from 2008 to 2023), driven largely by fear of not matching, creating financial and administrative burdens. Despite efforts by SFMatch to improve transparency by publishing program data since 2020, there has been no formal evaluation of the completeness or usefulness of this data. This study aims to characterize gaps in the information available to applicants on the SFMatch site during the 2022-2023 match cycle.

**Methods:** The authors identified 121 U.S. ophthalmology residency programs using the 2022 SFMatch Ophthalmology Program Profile Information page. The website was accessed on August 15, 2022 (before application submission) and October 15, 2022 (after applications were released) to catalogue data availability. Program profiles were evaluated for location, accreditation status, intern year type, medical school association, class size, fellowship availability, research faculty, and surgical volume. Surgical volumes were catalogued verbatim. Additionally, the home webpages of all programs were reviewed for characteristics not provided on SFMatch, such as applicant considerations (e.g., USMLE score cutoffs), program characteristics (e.g., gender balance, research requirements), and benefits (e.g., salary, parental leave). Data were collected in Excel and analyzed using IBM SPSS.

**Key Results:** Of 121 programs, 98 (81%) provided data by August 15, 2022, and 112 (93%) by October 15, 2022. Among those providing data, 108 (96.4%) reported an association with a medical school. Average class size was 4.6 ± 2.7, and average number of research faculty was 6.0 ± 7.8. For surgical volumes, 111 (92%) programs provided cataract volume data (mean 195.2, range 0–365, SD 62.8). The next most commonly reported surgeries were oculoplastics (55%), glaucoma (45%), and strabismus (43%). In total, 23 different surgery types were reported, with inconsistent categorization (e.g., "vitrectomy" vs. "Retina/Vitreous"). On program home webpages, only 21.3% provided any resident surgical volume data. Regarding applicant considerations, 18% mentioned a holistic review process, 10.7% provided a STEP 1 score cutoff (with 9% explicitly stating no cutoff), and only 3.3% provided average STEP 1 scores. For program characteristics, 80.3% provided gender balance (mostly via resident profiles), 54.9% provided research requirements, and 82.0% provided rotation locations and time allocation. Benefits data were more common: salary (60.7%), medical/dental (59.8%), vacation (63.9%), but parental leave (32.0%) and retirement (32.8%) were less frequently reported.

**Clinical Implications:** The study reveals that the SFMatch database, while a step forward, suffers from significant data gaps and inconsistencies that undermine its utility for applicants. The lack of standardized reporting on surgical volumes and the absence of critical information such as USMLE score cutoffs, research requirements, and resident benefits force applicants to rely on word-of-mouth and personal connections. This perpetuates inequities, as students without a home residency program are 40% less likely to match. The authors argue that mandatory, standardized data disclosure by all programs participating in the match is necessary to level the playing field, reduce unnecessary applications, and improve the efficiency and equity of the residency selection process. Such transparency could also accelerate the adoption of beneficial resident policies across programs.