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IT-based reminders for medication adherence: systematic review, taxonomy, framework and research directions

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  • Neetu Singh
  • Upkar Varshney

Abstract

IT-based reminders have been one of the most promising interventions to improve medication adherence. Even with considerable research, it is not clear what types of reminders are effective for different patients and diseases and how much improvement in adherence is sustainable over time. To answer this, we conduct a systematic literature review of IT-based reminders. We utilise a six-step process reflecting the systematicity and transparency which is implemented using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Then, we develop a taxonomy of reminders, using Nickerson’s method, including thirteen characteristics categorised in four different dimensions. The findings are used in deciding when and where and how to use reminders with what type of patients for how long in improving medication adherence. The subsequent detailed analysis of the articles brought numerous insights leading to the development of Comprehensive Framework for Medication Reminders (CFMR). The framework can be used by the IS researchers for developing theoretical models to study the effectiveness of interventions for improving medication adherence. The taxonomy can be extended to a multi-level taxonomy using the proposed framework and research directions and can be further evaluated using domain experts.

Suggested Citation

  • Neetu Singh & Upkar Varshney, 2020. "IT-based reminders for medication adherence: systematic review, taxonomy, framework and research directions," European Journal of Information Systems, Taylor & Francis Journals, vol. 29(1), pages 84-108, January.
  • Handle: RePEc:taf:tjisxx:v:29:y:2020:i:1:p:84-108
    DOI: 10.1080/0960085X.2019.1701956
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    Cited by:

    1. Gupta, Shivam & Modgil, Sachin & Bhatt, Priyanka C. & Chiappetta Jabbour, Charbel Jose & Kamble, Sachin, 2023. "Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry," Technovation, Elsevier, vol. 120(C).

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