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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

Pitfalls of medication adherence approximation through EHR and pharmacy records : Definitions, data and computation

Author

  • Alexander Galozy
  • Slawomir Nowaczyk
  • Anita Sant'Anna
  • Mattias Ohlsson
  • Markus Lingman

Summary, in English

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.

Department/s

  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

Publishing year

2020-04

Language

English

Publication/Series

International Journal of Medical Informatics

Volume

136

Document type

Journal article

Publisher

Elsevier Ireland Ltd

Topic

  • Social and Clinical Pharmacy
  • Public Health, Global Health, Social Medicine and Epidemiology

Keywords

  • Adherence measures
  • Data quality
  • Electronic health records
  • Medication refill adherence
  • Pharmacy data
  • Pitfalls

Status

Published

Project

  • AIR Lund - Artificially Intelligent use of Registers

Research group

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Other

  • ISSN: 1386-5056