An Automated Patient Self-Monitoring System to Reduce Health Care System Burden During the COVID-19 Pandemic in Malaysia: Development and Implementation Study.

Autor: Lim HM; Department of Primary Care Medicine, University of Malaya Medical Centre, Kuala Lumpur, Malaysia.; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Teo CH; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Dean's Office, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Ng CJ; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Chiew TK; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia., Ng WL; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Abdullah A; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Abdul Hadi H; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia., Liew CS; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia., Chan CS; eHealth Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
Jazyk: angličtina
Zdroj: JMIR medical informatics [JMIR Med Inform] 2021 Feb 26; Vol. 9 (2), pp. e23427. Date of Electronic Publication: 2021 Feb 26.
DOI: 10.2196/23427
Abstrakt: Background: During the COVID-19 pandemic, there was an urgent need to develop an automated COVID-19 symptom monitoring system to reduce the burden on the health care system and to provide better self-monitoring at home.
Objective: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process.
Methods: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing.
Results: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety.
Conclusions: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.
(©Hooi Min Lim, Chin Hai Teo, Chirk Jenn Ng, Thiam Kian Chiew, Wei Leik Ng, Adina Abdullah, Haireen Abdul Hadi, Chee Sun Liew, Chee Seng Chan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.02.2021.)
Databáze: MEDLINE
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