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  Computation of Adherence to Medications and Visualization of Medication Histories in R with AdhereR: Towards Transparent and Reproducible Use of Electronic Healthcare Data

Dima, A. L., & Dediu, D. (2016). Computation of Adherence to Medications and Visualization of Medication Histories in R with AdhereR: Towards Transparent and Reproducible Use of Electronic Healthcare Data. PLoS One, 12(4): e0174426. doi:10.1371/journal.pone.0174426.

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journal.pone.0174426.pdf (Verlagsversion), 2MB
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2017
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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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https://cran.r-project.org/ (Ergänzendes Material)
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https://github.com/ddediu/AdhereR (Ergänzendes Material)
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 Urheber:
Dima, Alexandra Lelia1, 2, Autor
Dediu, Dan3, 4, Autor           
Affiliations:
1University of Amsterdam, Amsterdam, the Netherlands, ou_persistent22              
2University Claude Bernard Lyon 1, Lyon, France , ou_persistent22              
3Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society, ou_792549              
4Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              

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 Zusammenfassung: Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.

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Sprache(n): eng - English
 Datum: 20172016-04-26
 Publikationsstatus: Online veröffentlicht
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1371/journal.pone.0174426
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Projektname : ASTRO-LAB project
Grant ID : 282593
Förderprogramm : Funding Programme 7 (FP7)
Förderorganisation : European Commission (EC)

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Titel: PLoS One
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: San Francisco, CA : Public Library of Science
Seiten: - Band / Heft: 12 (4) Artikelnummer: e0174426 Start- / Endseite: - Identifikator: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850