Proposal of Standardization to Assess Adherence With Medication Records

Arnet, Isabelle; Kooij, Marcel J.; Messerli, Markus; Hersberger, Kurt E.; Heerdink, Eibert R.; Bouvy, Marcel L.

doi:10.1177/1060028016634106

Cited by 72 publications

(77 citation statements)

References 37 publications

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“…AdhereR is thus designed to be used after data extraction and preparation. For advice on these preliminary steps, we recommend referring to existing guidelines (e.g., [4,5]) and database-specific documentation. We describe the time period investigated using two terms: follow-up window ( FUW ; the total period for which relevant medication events are recorded for included patients), and observation window ( OW ; the period within the follow-up window for which adherence is computed).…”

Section: Terms Definitions and Basic Considerationsmentioning

confidence: 99%

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Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Dima

Dediu

2017

PLoS ONE

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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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Section: Terms Definitions and Basic Considerationsmentioning

confidence: 99%

“…EHD are an objective and low-cost solution to measuring adherence in large patient samples [1,3]. Yet, concerns have been raised regarding the lack of standardization and transparency in data analysis, which make it difficult to ascertain research quality and to compare studies when synthesizing available evidence [4,5]. …”

Section: Introductionmentioning

confidence: 99%

Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Dima

Dediu

2017

PLoS ONE

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“…As adherence research has matured, the term adherence has taken on two related meanings: (1) a specific research measure for the regularity with which patients take their medicines, typically expressed as a proportion or percentage of prescribed days and (2) a more general definition perhaps best exemplified by a World Health Organization (WHO) report, “the extent to which a person’s behavior—taking medication, following a diet, and/or executing lifestyle changes—corresponds with agreed recommendations from a health care provider” [9••]. This review will examine literature describing adherence using the WHO’s general definition, taking into consideration the following related concepts: (1) initiation, which refers to a patient starting a new therapy; (2) persistence, which refers to the duration of time that patients remain adherent to medicine(s); (3) discontinuation, refers to permanent or temporary abstinence from therapy; (4) reinitiation, whereby patients resume medication after a period of abstinence [10]. …”

Section: Classifying and Describing The Prevalence Of Medication Non-mentioning

confidence: 99%

Recent Approaches to Improve Medication Adherence in Patients with Coronary Heart Disease: Progress Towards a Learning Healthcare System

Levy¹,

Huang

et al. 2018

Curr Atheroscler Rep

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Purpose of Review Non-adherence to medications for the secondary prevention of myocardial infarction (MI) is a major contributor to morbidity and mortality in these patients. This review describes recent advances in promoting adherence to therapies for coronary artery disease (CAD). Recent Findings Two large randomized controlled trials to “incentivize” adherence were somewhat disappointing; neither financial incentives nor “peer pressure” successfully increased rates of adherence in the post-MI population. Patient education and provider engagement appear to be critical aspects of improving adherence to CAD therapies, where the provider is a physician, pharmacist, or nurse and follow-up is performed in person or by telephone. Fixed-dose combinations of CAD medications, formulated as a so-called “polypill,” have shown some early efficacy in increasing adherence. Technological advances that automate monitoring and/or encouragement of adherence are promising but seem universally dependent on patient engagement. For example, medication reminders via text message perform better if patients are required to respond. Multifaceted interventions, in which these and other interventions are combined together, appear to be most effective. Summary There are several available types of proven interventions through which providers, and the health system at large, can advance patient adherence to CAD therapies. No single intervention to promote adherence will be successful in all patients. Further study of multifaceted interventions and the interactions between different interventions will be important to advancing the field. The goal is a learning healthcare system in which a network of interventions responds and adapts to patients’ needs over time.

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“…The most common type of this measure is medical possession ratio (MPR) defined as the number of days of medication supplied to the number of days in the observation multiplied by 100 [25]. MPR >80% is generally considered as the threshold of good adherence as there is some evidence that it predicts future hospitalisation [26].…”

Section: How To Diagnose Non-adherencementioning

confidence: 99%

How to Screen for Non-Adherence to Antihypertensive Therapy

et al. 2016

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The quality of assessment of non-adherence to treatment in hypertensive is poor. Within this review, we discuss the different methods used to assess adherence to blood-pressure-lowering medications in hypertension patients. Subjective reports such as physicians’ perceptions are inaccurate, and questionnaires completed by patients tend to overreport adherence and show a low diagnostic specificity. Indirect objective methods such as pharmacy database records can be useful, but they are limited by the robustness of the recorded data. Electronic medication monitoring devices are accurate but usually track adherence to only a single medication and can be expensive. Overall, the fundamental issue with indirect objective measures is that they do not fully confirm ingestion of antihypertensive medications. Detection of antihypertensive medications in body fluids using liquid chromatography–tandem mass spectrometry is currently, in our view, the most robust and clinically useful method to assess non-adherence to blood-pressure-lowering treatment. It is particularly helpful in patients presenting with resistant, refractory or uncontrolled hypertension despite the optimal therapy. We recommend using this diagnostic strategy to detect non-adherence alongside a no-blame approach tailoring support to address the perceptions (e.g. beliefs about the illness and treatment) and practicalities (e.g. capability and resources) influencing motivation and ability to adhere.

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Proposal of Standardization to Assess Adherence With Medication Records

Abstract: The possible impact of the measures in adherence research is discussed. By doing this, the results of future adherence research should gain in accuracy. Finally, studies will become more transparent, enabling comparison between studies.

Cited by 72 publications

References 37 publications

Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Recent Approaches to Improve Medication Adherence in Patients with Coronary Heart Disease: Progress Towards a Learning Healthcare System

How to Screen for Non-Adherence to Antihypertensive Therapy

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