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

Objective: To engage end-users to develop and evaluate an algorithm to convert electronic adherence monitoring device (EAMD) output into the adherence data required for analyses. Materials and Methods: This study included 4 phases. First, process mapping interviews and focus groups were conducted to identify rules for EAMD data processing and user needs. Second, algorithm parameters required to compute daily adherence values were defined and coded in an R package (OncMAP). Third, algorithm-produced data were compared to manually recoded data to evaluate the algorithm's sensitivity, specificity, and accuracy. Finally, pilot testing was conducted to obtain feedback on the perceived value/benefit of the algorithm and features that should be considered during software development. Results: EAMD data processing rules were identified and coded in an R application. The algorithm correctly classified all complete observations with 100% sensitivity and specificity. The receiver operating characteristic curve analysis yielded an area under the curve of 1.00. All pilot testing participants expressed interest in using the algorithm (Net Promoter Score = 71%) but identified several features essential for inclusion in the software package to ensure widespread adoption. Discussion: The decision rules implemented to process EAMD actuation data can be parameterized to develop an algorithm to automate this process. The algorithm demonstrated high sensitivity, specificity, and accuracy. End-users were enthusiastic about the product and provided insights to inform the development of a software package including the algorithm. Conclusion: A rule-based algorithm can accurately process EAMD actuation data and has the potential to improve the rigor and pace of adherence science.