Rational Drug Use Support System: design, artificial intelligence integration, and pilot feasibility trial.

OBJECTIVE

The Rational Drug Use Support System was developed to assist patients in using the right drug, at the right time and at the right dose according to their treatment protocols. The aim of this study was to evaluate the performance of an artificial intelligence-enhanced mobile application designed to support rational drug use and to assess its feasibility and usability through a pilot trial.

METHODS

The system includes an artificial intelligence -powered mobile application and a web-based interface integrating convolutional neural networks and object detection algorithms for human activity recognition. To build an image library, real patient videos were recorded in a hospital setting under physician supervision. A total of 357 original medication-intake videos were collected from 31 patients, which were then segmented into 1,626 short clips to train the artificial intelligence model. The system was first tested under controlled laboratory conditions and then evaluated for feasibility in a hospital pilot trial involving eight participants over 15 days.

RESULTS

The proposed artificial intelligence model achieved 94% accuracy in recognizing medication intake activities in the test set, suggesting promising performance that warrants further evaluation. Exploratory analyses of the 36-Item Short Form Health Survey Questionnaire administered to patients who utilized the mobile application for 15 days revealed improvements in the domains of social functioning, vitality, and mental health.

CONCLUSION

The findings suggest that the "Rational Drug Use Support System" appears feasible for implementation in telemedicine and clinical settings to support rational drug use and patient engagement in chronic disease management. However, larger controlled studies are warranted to confirm these preliminary findings.