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Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7453-9186
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Lund University, Lund, Sweden.ORCID iD: 0000-0003-1145-4297
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2020 (English)In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 136, article id 104092Article in journal (Refereed) Published
Abstract [en]

Background and purpose: Patients’ adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.

Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.

Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.

Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals. © 2020 The Authors. Published by Elsevier B.V. 

Place, publisher, year, edition, pages
Shannon: Elsevier, 2020. Vol. 136, article id 104092
Keywords [en]
Medication refill adherence, Electronic health records, Data quality, Pitfalls
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-41712DOI: 10.1016/j.ijmedinf.2020.104092PubMedID: 32062562Scopus ID: 2-s2.0-85079281579OAI: oai:DiVA.org:hh-41712DiVA, id: diva2:1396244
Part of project
iMedA: Improving MEDication Adherence through Person Centered Care and Adaptive Interventions, Vinnova
Funder
Vinnova, 2017-04617
Note

Other funding: Health Technology Center and CAISR at Halmstad University and Halland's Hospital

Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-03-10Bibliographically approved

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Galozy, AlexanderNowaczyk, SławomirPinheiro Sant'Anna, AnitaOhlsson, Mattias

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