Affiliations 

  • 1 Department of Primary Care Medicine, University of Malaya Medical Centre, 59100 Kuala Lumpur, Malaysia; University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 2 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: [email protected]
  • 3 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 4 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 5 Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 6 Department of Emergency Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 7 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Software Engineering, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 8 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 9 University of Malaya eHealth Unit, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Int J Med Inform, 2021 Nov;155:104567.
PMID: 34536808 DOI: 10.1016/j.ijmedinf.2021.104567

Abstract

BACKGROUND: COVID-19 telemonitoring applications have been developed and used in primary care to monitor patients quarantined at home. There is a lack of evidence on the utility and usability of telemonitoring applications from end-users' perspective.

OBJECTIVES: This study aimed to evaluate the feasibility of a COVID-19 symptom monitoring system (CoSMoS) by exploring its utility and usability with end-users.

METHODS: This was a qualitative study using in-depth interviews. Patients with suspected COVID-19 infection who used CoSMoS Telegram bot to monitor their COVID-19 symptoms and doctors who conducted the telemonitoring via CoSMoS dashboard were recruited. Universal sampling was used in this study. We stopped the recruitment when data saturation was reached. Patients and doctors shared their experiences using CoSMoS, its utility and usability for COVID-19 symptoms monitoring. Data were coded and analysed using thematic analysis.

RESULTS: A total of 11 patients and 4 doctors were recruited into this study. For utility, CoSMoS was useful in providing close monitoring and continuity of care, supporting patients' decision making, ensuring adherence to reporting, and reducing healthcare workers' burden during the pandemic. In terms of usability, patients expressed that CoSMoS was convenient and easy to use. The use of the existing social media application for symptom monitoring was acceptable for the patients. The content in the Telegram bot was easy to understand, although revision was needed to keep the content updated. Doctors preferred to integrate CoSMoS into the electronic medical record.

CONCLUSION: CoSMoS is feasible and useful to patients and doctors in providing remote monitoring and teleconsultation during the COVID-19 pandemic. The utility and usability evaluation enables the refinement of CoSMoS to be a patient-centred monitoring system.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.