Predicting high-risk opioid prescriptions before they are given

Proc Natl Acad Sci U S A. 2020 Jan 28;117(4):1917-1923. doi: 10.1073/pnas.1905355117. Epub 2020 Jan 14.


Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.

Keywords: administrative data; evidence-based policy; machine learning; opioids; predictive modeling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Algorithms*
  • Analgesics, Opioid / therapeutic use*
  • Drug Prescriptions / standards*
  • Female
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Opioid-Related Disorders / drug therapy*
  • Opioid-Related Disorders / epidemiology
  • Practice Patterns, Physicians' / standards*
  • Predictive Value of Tests
  • Prescription Drug Misuse / prevention & control*
  • Rhode Island / epidemiology
  • Risk Assessment / methods*


  • Analgesics, Opioid