Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation

Abstract

Background: Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to potential side effects. Robotic process automation uses software robots to automate repetitive and routine tasks, and their use may be extended to clinical settings. Objective: Thus, this study aimed to develop a robot using a diagnostic classification algorithm to automate repetitive laboratory evaluations for amiodarone follow-up. Methods: We designed a robot and clinical decision support system based on expert clinical advice and current best practices in thyroid and liver disease management. The robot provided recommendations on the time interval to follow-up laboratory testing and management suggestions, while the final decision rested with a physician, acting as a human-in-the-loop. The performance of the robot was compared to the existing real-world manual follow-up routine for amiodarone treatment. Results: Following iterative technical improvements, a robot prototype was validated against physician orders (n=390 paired orders). The robot recommended a mean follow-up time interval of 4.5 (SD 2.4) months compared to the 3.1 (SD 1.4) months ordered by physicians (P