Predicting high-risk opioid prescriptions before they are given.
Aged
Algorithms
Analgesics, Opioid
/ therapeutic use
Drug Prescriptions
/ standards
Female
Humans
Machine Learning
Male
Middle Aged
Opioid-Related Disorders
/ drug therapy
Practice Patterns, Physicians'
/ standards
Predictive Value of Tests
Prescription Drug Misuse
/ prevention & control
Rhode Island
/ epidemiology
Risk Assessment
/ methods
administrative data
evidence-based policy
machine learning
opioids
predictive modeling
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
28 01 2020
28 01 2020
Historique:
pubmed:
16
1
2020
medline:
12
5
2020
entrez:
16
1
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31937665
pii: 1905355117
doi: 10.1073/pnas.1905355117
pmc: PMC6994994
doi:
Substances chimiques
Analgesics, Opioid
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1917-1923Informations de copyright
Copyright © 2020 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
Competing interest statement: J.S.H. is a scholar on leave visiting Amazon Inc. during the 2018 to 2020 academic years, but is not working on projects that directly relate to the subject matter of this study in that role.
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