Reform Pathways for Healthcare Institution Management in the Context of National Infectious Disease Surveillance and Pre-Warning Software Systems
Year: 2025
Type: article
Abstract: Objective To study the driving role of the deployment and application of the national infectious disease monitoring and early warning front-end software in enhancing the management mode and efficiency of medical institutions, and to provide more theoretical support and practical reference for the establishment of digital medical management system in the new era. Methods To analyze the technical principles and core functions of the National Infectious Disease Surveillance and Early Warning Front-end Software, and to reveal the driving role of the National Infectious Disease Surveillance and Early Warning Front-end Software related technologies in the medical management mode of organizational structure, workflow, information management, personnel responsibilities, and resource allocation through the analysis of systematic literature and theoretical retrospective causes. Results The deployment and application of national infectious disease surveillance and early warning precursor software will drive the transformation of healthcare organizations' management model to data intelligence, but the change may face challenges such as technological integration, organizational inertia, and ethical risks. Conclusion The hospital management model driven by the NIDSS is transforming from “experience-driven” to “data-intelligence-driven”, and the change of its management model is essentially a self-adaptive revolution of healthcare organizations into the era of digital healthcare, which requires the use of intelligent algorithmic modeling technology, organizational inertia, and ethical risks. It is necessary to make breakthroughs in intelligent algorithmic modeling technology, digital literacy competence certification and data regulatory innovation system to build a modern medical management system that combines “level and urgency”.
DOI: 10.64904/fpm25004
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4414413441