ArticleBMC medical research methodology2023
Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Comprehensive Approach to Days' Supply Estimation in a Real-World Prescription Database: Algorithm Development and Validation Study.Online journal of public health informatics · 2026Article
- Factors Associated with Medication Adherence and Glycemic Control among American Indian/Alaska Native Adults with Type 2 Diabetes Within a Commercial Health Care System: A System-Wide Cross-Sectional Study.Journal of racial and ethnic health disparities · 2025Article
- Improving Timely Medication Access for Endocrinology and Diabetes Patients: A Quality Improvement Initiative.Global journal on quality and safety in healthcare · 2025Article
- Non-dispensed prescriptions - A nationwide descriptive study.Exploratory research in clinical and social pharmacy · 2024Article
- Quantitative Adherence Level Evaluation Among Long-Term Antiretroviral Therapy Patients in Latvia.Patient preference and adherence · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundMedication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study.
methodsOver 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared.
resultsEstimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures.
conclusionsWe evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.
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