Title: AI-Assisted Multi-Criteria Sorting for Decision Support in Healthcare Systems
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 90-93
Publication Date: 2025/08/28
Abstract:
Decision-making in modern healthcare is a complex process often involving numerous, conflicting criteria such as patient symptoms, lab results, medical history, and resource availability. This complexity can lead to diagnostic inconsistencies and suboptimal treatment plans. This paper proposes a novel AI-assisted system that utilizes multi-criteria sorting to provide robust decision support in healthcare. By formalizing clinical knowledge into a rule-based expert system and combining it with a multi-criteria decision analysis (MCDA) framework, the system is designed to efficiently sort patients into prioritized categories (e.g., high-risk, medium-risk, low-risk). The methodology involves the acquisition of expert knowledge to define the decision criteria and rules, the implementation of a forward-chaining inference engine, and the validation of the system using a simulated patient dataset. Preliminary findings indicate that this approach can significantly improve the accuracy, consistency, and timeliness of medical decisions, thereby enhancing patient outcomes and optimizing clinical workflows.