Using surveillance system evaluation to develop an influenza surveillance system for strengthened disease prevention and control at Bang Pahan Hospital, Phra Nakhon Si Ayutthaya Province - Ketwang, Wipawee (Journal of Medical and Public Health Region 4, 2026)
This study aimed to evaluate the performance of the influenza surveillance system at Bang Pahan Hospital and to develop an automated alert system using the Telegram application to enhance disease prevention and control. This developmental research applied an action research framework combined with a quasi-experimental before–after design. Data from the pre-development phase (June 1, 2021 – May 31, 2022) were compared with the post-development phase (June 1, 2024 – May 31, 2025). The intervention involved developing an automated alert system (Telegram Bot) integrated with the hospital information system (HOSxP) to identify suspected influenza cases based on surveillance case definitions. Quantitative surveillance attributes, including sensitivity, positive predictive value (PPV), and timeliness, were analyzed, along with qualitative attributes such as acceptability, simplicity, and stability. Statistical analyses were conducted using the chi-square test, Fisher’s exact test, and Mann–Whitney U test. After implementation, the surveillance system showed significant improvements. Sensitivity increased from 4.78% to 42.16% (p < 0.001), and the PPV increased from 23.81% to 91.21% (p < 0.001). The average reporting time significantly decreased from 3.76 days to 1.55 days (p < 0.001). Qualitative findings indicated improved staff acceptance of the system, with knowledge transfer, standard operating procedures, and operational coordination reaching 100% acceptance (p < 0.05). In conclusion, the automated alert system significantly improved influenza surveillance performance by increasing case detection, reducing reporting delays, and strengthening rapid response capacity. This digital surveillance model represents a practical and scalable approach for improving infectious disease surveillance systems in community hospitals.