文章基本信息

标题：Predicting high-risk opioid prescriptions before they are given
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作者：Justine S. Hastings ; Mark Howison ; Sarah E. Inman 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：4
页码：1917-1923
DOI：10.1073/pnas.1905355117
出版社：The National Academy of Sciences of the United States of America
摘要：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.
关键词：opioids ; evidence-based policy ; predictive modeling ; machine learning ; administrative data