Policy Library Redundancy Analysis Using K-means Clustering.

Abstract

This capstone project investigates the application of artificial intelligence (AI) techniques, specifically sentence embedding and k-means clustering using large language models, to address the challenge of policy library redundancy within a healthcare setting. The project aimed to demonstrate the viability of using AI-assisted tools in policy library management, targeting a 5% reduction in the overall policy library at a large academic healthcare system. By collaborating with the accreditation team and developing a Python-script prototype, the study showed that AI-assisted methods could significantly enhance efficiency and reduce labor in policy library management. Results indicate a potential 4% reduction in library size, underscoring the method's effectiveness and the opportunity for further optimization. This research contributes to the emerging field of AI in healthcare administration, offering a scalable model for improving policy library management processes in various healthcare contexts.