Machine-Learning Prediction and Reducing Overdoses with EHR Nudges (mPROVEN)

PI: Walid Gellad, MD, MPH
Funding Source: NIH/NIDA
July 2022 - April 2027

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This project proposes to reduce opioid overdoses and potentially unsafe opioid prescribing by combining a machine learning-based risk prediction model with a behavioral nudge in the electronic medical record. The team will develop an opioid overdose risk prediction algorithm with UPMC electronic medical record data, then design and pilot test a nudge intervention in the EHR, and conduct a randomized clinical trial of the algorithm plus nudge intervention in UPMC primary care practices, to see how the intervention affects important outcomes for opioid safety. This work is a continuation of Dr. Gellad’s NIH R01 “Using Machine Learning to Predict Problematic Prescription Opioid Use and Opioid Overdose.”