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J Digit Imaging (2016) 29:638644 DOI 10.1007/s10278-016-9872-2
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Web End = Toward Data-Driven Radiology EducationEarly Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)
Po-Hao Chen1 & Thomas W. Loehfelm2,3 & Aaron P. Kamer4 & Andrew B. Lemmon2 &
Tessa S. Cook1 & Marc D. Kohli3
Published online: 4 March 2016# Society for Imaging Informatics in Medicine 2016
Abstract The 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.
Keywords Radiology training . Residency . Case log . Education . Database . Big data . Analytics . ACGME
Background
Radiology education, like graduate medical education at large, is conducted primarily using an apprenticeship model. By independently interpreting an imaging study before reviewing with staff radiologists, residents gain experience and knowledge unique to that garnered from reading textbooks [1]. Therefore, the number of interpreted cases is an important surrogate for experience and competence during training.
Experience-based learning in clinical radiology training is important in national requirements as well as hospital credentialing processes. The Accreditation Council of Graduate Medical Education (ACGME) requires that radiology residency programs maintain case logs for 11 categories of examinations as markers for clinical exposure and sets minimum requirements in each category [2]. For example, the ACGME requires that a graduating fourth year radiology resident interpret at least 1900 chest radiographs during training. The Mammography Quality Standards Act...