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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Medical Informatics Association : JAMIA Jg. 24; H. 6; S. 1204
Hauptverfasser: Canan, Chelsea, Polinski, Jennifer M, Alexander, G Caleb, Kowal, Mary K, Brennan, Troyen A, Shrank, William H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England 01.11.2017
Schlagworte:
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
Online-Zugang:Weitere Angaben
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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