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

Galozy, Alexander ; Nowaczyk, Slawomir LU ; Sant'Anna, Anita ; Ohlsson, Mattias LU orcid and Lingman, Markus (2020) In International Journal of Medical Informatics 136.
Abstract

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... (More)

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.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Adherence measures, Data quality, Electronic health records, Medication refill adherence, Pharmacy data, Pitfalls
in
International Journal of Medical Informatics
volume
136
article number
104092
publisher
Elsevier
external identifiers
  • scopus:85079281579
  • pmid:32062562
ISSN
1386-5056
DOI
10.1016/j.ijmedinf.2020.104092
project
AIR Lund - Artificially Intelligent use of Registers
language
English
LU publication?
yes
id
0b7be53c-3cc1-4988-a96b-2e1dc00950f4
date added to LUP
2020-02-20 12:17:16
date last changed
2024-10-31 00:05:24
@article{0b7be53c-3cc1-4988-a96b-2e1dc00950f4,
  abstract     = {{<p>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 &lt; 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.</p>}},
  author       = {{Galozy, Alexander and Nowaczyk, Slawomir and Sant'Anna, Anita and Ohlsson, Mattias and Lingman, Markus}},
  issn         = {{1386-5056}},
  keywords     = {{Adherence measures; Data quality; Electronic health records; Medication refill adherence; Pharmacy data; Pitfalls}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{International Journal of Medical Informatics}},
  title        = {{Pitfalls of medication adherence approximation through EHR and pharmacy records : Definitions, data and computation}},
  url          = {{http://dx.doi.org/10.1016/j.ijmedinf.2020.104092}},
  doi          = {{10.1016/j.ijmedinf.2020.104092}},
  volume       = {{136}},
  year         = {{2020}},
}