October 13, 2021 / 1:00-2:00 p.m. ET
Expeditions in Experiential AI
There has been recent interest by payers, health care systems, and researchers in the development of machine learning and artificial intelligence models that predict an individual’s probability of developing opioid use disorder. The scores generated by these algorithms can be used by physicians to tailor the prescribing of opioids for the treatment of pain, reducing or foregoing prescribing to individuals deemed to be at high risk, or increasing prescribing for patients deemed to be at low risk. This paper constructs a machine learning algorithm to predict opioid use disorder risk using commercially available claims data similar to those utilized in the development of proprietary opioid use disorder prediction algorithms. We study risk scores generated by the machine learning model in a setting with quasi-experimental variation in the likelihood that doctors prescribe opioids, generated by changes in the legal structure for monitoring physician prescribing.