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

Abstract

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.

Operationalization and validation of a novel method to calculate adherence to polypharmacy with refill data from the Australian pharmaceutical benefits scheme (PBS) database

“…We define polypharmacy as the concurrent use of multiple medications, without restriction of the number, although the literature provides various definitions and numerical thresholds. 30 For the operationalization of the DPPR, the following definitions and assumptions were made, in accordance with others, 2 , 15 , 31 and integrated into the SAS programming: The observation window starts with the first dispensing (supply) date after the first hospitalization for myocardial infarction, and ends at the last refill date or the first readmission date, whichever came first. A gap may exist between two supplies when the current supply is depleted and before a refill supply is dispensed.…”

“… 13 Thus, recent effort has focused on a proposal to standardize new measures for adherence calculations for single medications from dispensing data, 14 including their operationalization. 15 …”

The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint

“…In summary, the major disadvantage of the current lack of widely accepted standards for adherence assessment is the difficulty in comparing and interpreting scientific studies' results. Uniform adherence measurement and a common ontology urgently need to be developed in order to support research and enable real-life implementation of study findings [ 40 , 52 , 53 ]. This is also necessary for cross-study comparisons and fair benchmarking of adherence-targeting interventions.…”

Effect of Mobile Device-Assisted N-of-1 Trial Participation on Analgesic Prescribing for Chronic Pain: Randomized Controlled Trial

“…This calculation is analogous to the method routinely employed by pharmacies to estimate "days supply" of medication. 35 For opioids, abstracted fields included name of medication, amount per dispensed unit, route of administration, length of prescription, and number of pills per prescription. At each time point, morphine milligram equivalents (MMEs) per day were calculated for patients taking opioids (which included codeine, hydrocodone, oxycodone, morphine, and tramadol) using the conversion formulae applied by Von Korff et al 36 For NSAIDs, acetaminophen, and gabapentenoids, abstracted elements included name of the medication, amount per dispensed unit (in mg or μg), number of units (e.g., pills) per prescribed dose, maximum frequency per day, scheduled versus as needed (prn), and route of administration.…”