• Jonathan P Wanderer, Thomas A Lasko, Joseph R Coco, Leslie C Fowler, Matthew D McEvoy, Xiaoke Feng, Matthew S Shotwell, Gen Li, Brian J Gelfand, Laurie L Novak, David A Owens, and Daniel V Fabbri.
• Department of Anesthesiology, Department of Biomedical Informatics, Vanderbilt University Medical Center, United States. Electronic address: jon.wanderer@vumc.org.
• J Clin Anesth. 2021 Feb 1; 68: 110114.

Study ObjectiveA challenge in reducing unwanted care variation is effectively managing the wide variety of performed surgical procedures. While an organization may perform thousands of types of cases, privacy and logistical constraints prevent review of previous cases to learn about prior practices. To bridge this gap, we developed a system for extracting key data from anesthesia records. Our objective was to determine whether usage of the system would improve case planning performance for anesthesia residents.DesignRandomized, cross-over trial.SettingVanderbilt University Medical Center.MeasurementsWe developed a web-based, data visualization tool for reviewing de-identified anesthesia records. First year anesthesia residents were recruited and performed simulated case planning tasks (e.g., selecting an anesthetic type) across six case scenarios using a randomized, cross-over design after a baseline assessment. An algorithm scored case planning performance based on care components selected by residents occurring frequently among prior anesthetics, which was scored on a 0-4 point scale. Linear mixed effects regression quantified the tool effect on the average performance score, adjusting for potential confounders.Main ResultsWe analyzed 516 survey questionnaires from 19 residents. The mean performance score was 2.55 ± SD 0.32. Utilization of the tool was associated with an average score improvement of 0.120 points (95% CI 0.060 to 0.179; p < 0.001). Additionally, a 0.055 point improvement due to the "learning effect" was observed from each assessment to the next (95% CI 0.034 to 0.077; p < 0.001). Assessment score was also significantly associated with specific case scenarios (p < 0.001).ConclusionsThis study demonstrated the feasibility of developing of a clinical data visualization system that aggregated key anesthetic information and found that the usage of tools modestly improved residents' performance in simulated case planning.Copyright © 2020 Elsevier Inc. All rights reserved.

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