An analytical model to evaluate reminders for medication adherence. (April 2020)

• Record Type: Journal Article
• Authors: Varshney, Upkar; Singh, Neetu
• Is Part Of: International journal of medical informatics. Volume 136(2020)
• Publication Date: 2020-04
• Subjects: Medication adherence -- Reminders -- Analytical model -- Performance evaluation -- Guidelines; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104091
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Developed an approach and analytical model to evaluate reminders. The model can be used for effectiveness, side effects, and healthcare cost savings. The model can be utilized as a precursor and decision-making tool to decide whether an RCT is needed or not. The evaluation results in numerous insights for healthcare professionals, patients, decision makers, and app developers. Abstract: Objectives: Several interventions have been proposed to improve medication adherence including those using reminders. The performance of reminders, including effectiveness and side effects, varies widely in different settings. We must study this for improving decision making on how, when, and where to use what type of reminders. Methods: Analytical modeling is an effective and low-cost method to derive preliminary or intermediate results and insights for further study of interventions for medication adherence. We developed an analytical model that can be used to evaluate the performance of reminders in various settings, including effectiveness, side effects, and healthcare cost savings for medication adherence. Results: Context-aware reminders perform better than simple reminders for willing patients even when they completely rely on reminders for taking their doses. Simple reminders lead to more side effects than context-aware reminders. Further, context-aware reminders generate more healthcare savings without side effects and a comparable cost of the intervention. The results contribute to an improved understanding of reminders and are used to derive a set of guidelines for patients, healthcare professionals, decision-makers, and mobile app developers. Conclusions: The proposed model is a low cost and effective tool to derive results and insights for the use of reminders in different settings to improve medication adherence. Therefore, the model can be utilized as a decision-making tool for deciding whether to pursue an RCT on healthcare interventions. The analytical model can be extended for complex scenarios of multiple interdependent medications, adaptation with patients' condition and behavior, and composite interventions.