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Engaging end-users to develop a novel algorithm to process electronic medication adherence monitoring device data.

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An algorithm was developed to process electronic adherence monitoring device (EAMD) data, achieving high accuracy. End-users found the algorithm valuable, providing feedback for future software development to enhance adherence science.

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Area of Science:

  • Adherence science
  • Health informatics
  • Algorithm development

Background:

  • Accurate adherence data is crucial for clinical trial analysis.
  • Electronic adherence monitoring devices (EAMD) generate complex data requiring processing.
  • Standardized methods for converting EAMD output into usable adherence data are needed.

Purpose of the Study:

  • To develop and evaluate an algorithm for converting electronic adherence monitoring device (EAMD) output into adherence data.
  • To engage end-users in the algorithm development and evaluation process.
  • To assess the algorithm's accuracy and end-user satisfaction.

Main Methods:

  • Utilized a 4-phase approach: process mapping interviews, focus groups, algorithm parameter definition and coding in R (OncMAP), and pilot testing.
  • Compared algorithm-produced data against manually recoded data to determine sensitivity, specificity, and accuracy.
  • Gathered end-user feedback on perceived value and essential features for software development.

Main Results:

  • Developed an R package (OncMAP) with parameterized decision rules for processing EAMD data.
  • The algorithm demonstrated 100% sensitivity and specificity in classifying complete observations, with an Area Under the Curve of 1.00.
  • Pilot testers expressed strong interest (Net Promoter Score = 71%) and provided crucial feedback for software enhancement.

Conclusions:

  • A rule-based algorithm can accurately automate the processing of EAMD actuation data.
  • The developed algorithm shows high sensitivity, specificity, and accuracy, improving adherence data rigor.
  • End-user engagement is vital for developing a robust software package that advances adherence science.