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The current system of summative multi-rater evaluations and standardized tests to determine readiness to graduate from critical care fellowships has limitations. We sought to pilot the use of data envelopment analysis (DEA) to assess what aspects of the fellowship program contribute the most to an individual fellow’s success. DEA is a nonparametric, operations research technique that uses linear programming to determine the technical efficiency of an entity based on its relative usage of resources in producing the outcome.Retrospective cohort study.Critical care fellows (n = 15) in an Accreditation Council for Graduate Medical Education (ACGME) accredited fellowship at a major academic medical center in the United States.After obtaining institutional review board approval for this retrospective study, we analyzed the data of 15 anesthesiology critical care fellows from academic years 2013–2015. The input-oriented DEA model develops a composite score for each fellow based on multiple inputs and outputs. The inputs included the didactic sessions attended, the ratio of clinical duty works hours to the procedures performed (work intensity index), and the outputs were the Multidisciplinary Critical Care Knowledge Assessment Program (MCCKAP) score and summative evaluations of fellows.A DEA efficiency score that ranged from 0 to 1 was generated for each of the fellows. Five fellows were rated as DEA efficient, and 10 fellows were characterized in the DEA inefficient group. The model was able to forecast the level of effort needed for each inefficient fellow, to achieve similar outputs as their best performing peers. The model also identified the work intensity index as the key element that characterized the best performers in our fellowship.DEA is a feasible method of objectively evaluating peer performance in a critical care fellowship beyond summative evaluations alone and can potentially be a powerful tool to guide individual performance during the fellowship.