Chen, Po-HaoLoehfelm, Thomas W.Kamer, Aaron P.Lemmon, Andrew B.Cook, Tessa S.Kohli, Marc D.2018-06-082018-06-082016-12Chen, P.-H., Loehfelm, T. W., Kamer, A. P., Lemmon, A. B., Cook, T. S., & Kohli, M. D. (2016). Toward Data-Driven Radiology Education—Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA). Journal of Digital Imaging, 29(6), 638–644. https://doi.org/10.1007/s10278-016-9872-20897-1889https://hdl.handle.net/1805/16436The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.en-USPublisher PolicyACGMEAnalyticsBig dataCase logDatabaseEducationRadiology trainingResidencyArticle