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Medication Adherence Technologies: A Classification Taxonomy Based on Features.

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This study developed a comprehensive classification system for medication adherence technologies. The new taxonomy categorizes devices by features, aiding in selection and usability testing for better patient outcomes.

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Digital Health

Background:

  • Medication adherence technologies lack a standardized classification system.
  • Existing technologies vary widely in features and characteristics.
  • A structured framework is needed for evaluation and selection.

Purpose of the Study:

  • To develop a comprehensive classification system for medication adherence technologies.
  • To create a taxonomy based on an inventory of technology characteristics and features.
  • To address the critical gap in literature regarding technology categorization.

Main Methods:

  • Employed a 3-stage methodology: development, validation, and evaluation.
  • Utilized taxonomy development methods, including empirical-to-conceptual and conceptual-to-empirical approaches.
  • Validated the taxonomy using the Delphi consensus approach and classified 20 sample technologies.

Main Results:

  • The final taxonomy includes 7 dimensions, 25 subdimensions, and 320 characteristics.
  • Key dimensions encompass Physical Features, Display, Connectivity, System Alert, Data Collection and Management, Operations, and Integration.
  • Applicability and comprehensiveness were verified through technology comparison and qualitative code mapping.

Conclusions:

  • Successfully established the first comprehensive classification system for medication adherence technologies.
  • The taxonomy provides a structured framework for categorizing and evaluating these technologies.
  • Supports usability testing and selection of appropriate devices, particularly for older adults.