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AI-Driven Public Health Information Management and Emergency Decision-Making: A Case Study of Hospital Information Systems
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Author(s): Zhixin Zou (Center for Economic Behavior and Decision-Making, Key Research Center of Philosophy and Social Sciences, Zhejiang University of Finance and Economics, China), Yan Kang (Medical Humanities and Management, Wenzhou Medical University, China), Jiahui Zheng (Medical Humanities and Management, Wenzhou Medical University, China), Portia Cobbinah (Medical Humanities and Management, Wenzhou Medical University, China), Rogozhina Mariia (Medical Humanities and Management, Wenzhou Medical University, China), Runshu Xu (Medical Humanities and Management, Wenzhou Medical University, China)and Bojing Liu (Key Research Center of Philosophy and Social Sciences of Zhejiang Province, Institute of Medical Humanities, Wenzhou Medical University, China)
Copyright: 2026
Volume: 38
Issue: 1
Pages: 24
Source title:
Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.407232
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Abstract
The increasing complexity of healthcare systems and the rapid growth of heterogeneous medical data pose significant challenges to effective decision-making in public health and clinical practice. Existing data-driven approaches often struggle to balance predictive accuracy, robustness, and interpretability, particularly under dynamic and uncertain conditions. To address these challenges, this study proposes an AI-driven Emergency Decision-Making framework (AIM-EDM) that integrates multi-source health data, temporal modeling, and causal reasoning into a unified decision-support architecture. The proposed framework leverages deep representation learning to capture complex temporal patterns, incorporates knowledge-guided causal inference to enhance interpretability, and employs decision optimization to support reliable and actionable outcomes.
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