Journal of medical Internet research · 2025
Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation.
СтатьяНазвание и аннотация: Перевод в очереди
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Johansson Birgitta I, Landahl Jonas, Tammelin Karin, Aerts Erik, Lundberg Christina E, Adiels Martin, Lindgren Martin, Rosengren Annika, Papachrysos Nikolaos, Filipsson Nyström Helena, Sjöland Helen
Аннотация
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<.001). For normal laboratory values, the robot recommended a 6-month follow-up in 281 (72.1%) of cases, whereas physicians did so in only 38 (9.7%) of cases, favoring a 3- to 4-month follow-up (n=227, 58.2%). All patients diagnosed with new side effects (n=12) were correctly detected by the robot, whereas only 8 were by the physician.
CONCLUSIONS: An automated process, using a software robot and a diagnostic classification algorithm, is a technically and medically reliable alternative for amiodarone follow-up. It may reduce manual labor, decrease the frequency of laboratory testing, and improve the detection of side effects, thereby reducing costs and enhancing patient value.
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<.001). For normal laboratory values, the robot recommended a 6-month follow-up in 281 (72.1%) of cases, whereas physicians did so in only 38 (9.7%) of cases, favoring a 3- to 4-month follow-up (n=227, 58.2%). All patients diagnosed with new side effects (n=12) were correctly detected by the robot, whereas only 8 were by the physician.
CONCLUSIONS: An automated process, using a software robot and a diagnostic classification algorithm, is a technically and medically reliable alternative for amiodarone follow-up. It may reduce manual labor, decrease the frequency of laboratory testing, and improve the detection of side effects, thereby reducing costs and enhancing patient value.