2025, Vol. 7, Issue 2, Part B

The exponential growth of healthcare data necessitates robust knowledge management frameworks to optimize resource allocation and clinical decision-making. This study introduces a novel framework integrating Nonaka’s SECI Model with the War Strategy Optimization (WSO) algorithm, aiming to bridge knowledge-sharing gaps in dynamic healthcare environments. The WSO-SECI framework enhances tacit-to-explicit knowledge conversion while adapting resource distribution using AI-driven optimization techniques.

Using quantitative and qualitative methodologies on real-world datasets (UCI Heart Disease, Pima Indians Diabetes), the system demonstrates faster convergence, superior knowledge alignment, improved clustering performance, and enhanced decision accuracy compared to baseline methods (PSO, GA, traditional WSO). Statistical validation confirms the significance of WSO-SECI improvements (p<.01, Cohen’s d > 1.5).

Expert evaluations emphasize its clinical applicability, achieving a Likert score of 4.4/5 for diagnostic relevance. The study underscores AI’s transformative role in healthcare knowledge management, addressing challenges in resource optimization, interdisciplinary collaboration, and personalized patient care.