A Communication System to Care for Patients with COVID-19 Using Deep Learning Technology: A Mixed Method Study
DOI:
https://doi.org/10.60099/prijnr.2026.272913Keywords:
Caring, Communication system, COVID-19, Deep learning technology, Mixed Method, NursingAbstract
COVID-19 is an emerging infectious disease whose rapid mutation outpaces timely drug or vaccine development and disrupts face-to-face communication in clinical care. This exploratory sequential mixed‑methods study in Thailand employed a 3-stage approach to identify communication barriers and needs, develop a deep learning technology-based system for hospitalized patients, and evaluate its effectiveness. Phase 1, qualitative data were collected for descriptive purposes through focus group discussions with 16 participants, including nurses, physicians, and patients with COVID-19. The analysis revealed structural, process, and outcome obstacles, which were organized into four themes of barriers and three categories of requirements for the communication system. Phase 2 translated these requirements into a deep learning technology communication system featuring a two-way, real-time platform powered by a logistic regression classifier. Phase 3 quantitatively tested the prototype with 39 patients, 70 nurses, and 15 digital technology experts across four public hospitals. Instruments included Focus-Group Protocols, the Deep Learning Technology Communication System, the State-Trait Anxiety Inventory, and two Quality-Assessment Forms. Data were analyzed using descriptive statistics, content analysis, accuracy testing, and the Wilcoxon matched-pairs signed-rank test.
The study identified key communication barriers across structural, process, and outcome dimensions. Three core needs emerged: a user-friendly, real-time two-way system; systematic communication of patient needs; and timely responses via technology. The developed system, utilizing a logistic regression algorithm with an 80% accuracy rate, enabled clear and responsive communication, significantly reducing patient anxiety. Its quality was rated significantly higher than previous systems by digital experts. While effective in enhancing patient–nurse coordination, limitations included limited access for frail patients, unstable Wi-Fi, and a small sample size, which affected generalizability. Further refinement is needed to improve accessibility, reliability, and scalability.
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