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Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

Canan C, Polinski JM, Alexander C, et al. Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review. J Am Med Inform Assoc. 2017;24(6):1204-1210. doi:10.1093/jamia/ocx066.

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November 8, 2017
Canan C, Polinski JM, Alexander C, et al. J Am Med Inform Assoc. 2017;24(6):1204-1210.

Safer opioid prescribing requires that providers and systems are able to identify patients who misuse or divert opioids. This systematic review assessed different automated algorithms to detect population-level nonmedical opioid use. The authors suggest that algorithms that integrate claims data with natural language processing or other advanced informatics techniques yield the best results.

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Canan C, Polinski JM, Alexander C, et al. Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review. J Am Med Inform Assoc. 2017;24(6):1204-1210. doi:10.1093/jamia/ocx066.