Data for Adherence Decision Support.

Journal: Studies in health technology and informatics
PMID: 30741176

Abstract

Technological interventions aimed at addressing medication non-adherence have shown some promise but do not deliver the full potential of an Internet of Things based Adherence Decision Support (ADS) system due, in part, to a lack high-resolution definition and measure of adherence. This paper presents a novel methodology and pilot study aimed at collecting data to support an AI-based measure of adherence. The pilot study results demonstrate the viability of the methodology and that a full-scale study could provide meaningful data to support to an AI-based ADS system.

Authors

  • Simon Diemert
    Critical Systems Labs Inc.
  • Jens Weber
    Department of Computer Science, University of Victoria.
  • Morgan Price
    Department of Family Practice, University of British Columbia.
  • Jordan Bannman
    Department of Family Medicine, University of British Columbia.

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