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Acad Med. 2018 Mar 13. doi: 10.1097/ACM.0000000000002209. [Epub ahead of print]

Harnessing the Power of Big Data to Improve Graduate Medical Education: Big Idea or Bust?

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1
V.M. Arora is director, Graduate Medical Education Clinical Learning Environment Innovation, University of Chicago Medicine, Chicago, Illinois; ORCID: https://orcid.org/0000-0002-4745-7599.

Abstract

With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice.The timing is ripe for such a transformation. A recent National Academy of Medicine report called for reforms to how GME is delivered and financed. While many agree on the need to ensure GME meets our nation's health needs, there is little consensus on how to measure the performance of GME in meeting this goal. During a recent workshop at the National Academy of Medicine on GME outcomes and metrics in October 2017, a key theme emerged: big data holds great promise to inform GME performance at individual, institutional, and national levels. In this Invited Commentary, several examples are presented, such as using big data to inform clinical experience and provide clinically meaningful data to trainees, and using novel data sources, including ambient data, to better measure quality of GME training.

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