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Ruben Amarasingham, Anne-Marie J. Audet, David W. Bates, I. Glenn Cohen, Martin Entwistle, G.J. Escobar, Vincent Liu, Lynn Etheredge, Bernard Lo, Lucila Ohno-Machado, Sudha Ram, Suchi Saria, Lisa M. Schilling, Anand Shah, Walter F. Stewart, Ewout W. Steyerberg & Bin Xie, Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges, 4 eGEMs (Generating Evidence & Methods to improve patient outcomes) iss. 1, art. 3, Mar. 7, 2016.

Abstract: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner.