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

Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we...

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Podrobná bibliografia
Vydané v:	Journal of the American Medical Informatics Association : JAMIA Ročník 24; číslo 6; s. 1204
Hlavní autori:	Canan, Chelsea, Polinski, Jennifer M, Alexander, G Caleb, Kowal, Mary K, Brennan, Troyen A, Shrank, William H
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England 01.11.2017
Predmet:	Algorithms Data Mining - methods Databases, Pharmaceutical Electronic Health Records Humans Insurance Claim Reporting Opioid-Related Disorders Prescription Drug Misuse United States screening nonmedical opioid use electronic claims data electronic health record automated algorithms
ISSN:	1527-974X, 1527-974X
On-line prístup:	Zistit podrobnosti o prístupe
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Shrnutí:	Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Undefined-4
ISSN:	1527-974X 1527-974X
DOI:	10.1093/jamia/ocx066