Robotic process automation support in telemedicine: Glaucoma screening usage case
4
Issued Date
2022-01-01
Resource Type
ISSN
23529148
Scopus ID
2-s2.0-85133641152
Journal Title
Informatics in Medicine Unlocked
Volume
31
Rights Holder(s)
SCOPUS
Bibliographic Citation
Informatics in Medicine Unlocked Vol.31 (2022)
Suggested Citation
Thainimit S. Robotic process automation support in telemedicine: Glaucoma screening usage case. Informatics in Medicine Unlocked Vol.31 (2022). doi:10.1016/j.imu.2022.101001 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/86340
Title
Robotic process automation support in telemedicine: Glaucoma screening usage case
Author(s)
Other Contributor(s)
Abstract
Glaucoma is a worldwide leading cause of irreversible blindness. Early detection is crucial for successful treatment and prevention of vision loss. Recently, tele-ophthalmology has gained more acceptance for remote consulting, and eye screening. The tele-ocular screening can be enhanced further with emerging technologies in robotic process automation (RPA) that offers the ability to automate repetitive human tasks of the ocular screening process. This work aims to assess time effectiveness of the RPA-supported glaucoma screening. Mobile-based glaucoma screening combining RPA and machine learning (ML) is developed for assessing its usability and the time effectiveness of the RPA-supported glaucoma screening. The usability evaluation of the developed application is conducted with 68 participants including both patients and medical staff. Handling times that users spent with or without RPA support are recorded along with the satisfactory questionnaire surveys. The results show that the screening system with integration of RPA reduces the average handling time per user by 75%. The overall satisfaction score of the application is 8.10 out of 10. The machine learning module helps in notifying clinicians when the preliminary diagnosis results in severe conditions, allowing timely treatment. Integration of RPA and ML can assist clinicians and reduce the workload of medical staff significantly. Our study shows that the RPA and ML-based framework improves customer experiences and cost-time efficiency. It promotes feasibility in large-scale population glaucoma screening and data collection.