Stratification of Medical Equipment Using Clustering Algorithm and Optimized Maintenance Scheduling

Medical equipment maintenance in healthcare facilities requires strategic prioritization to optimize resource allocation and ensure patient safety. This study presents a novel approach for stratifying medical equipment using K-means clustering algorithm combined with optimized maintenance scheduling. A comprehensive dataset of 1,973 medical equipment from X Hospital, Chennai, was analyzed using features including purchase cost, downtime, usage patterns, and preventive maintenance costs. The clustering algorithm successfully stratified equipment into three priority categories: High Priority (658 equipment, 33.4%), Medium Priority (657 equipment, 33.3%), and Low Priority (658 equipment, 33.4%). The silhouette score of 0.154 indicates reasonable clustering validity. Optimized maintenance scheduling based on priority stratification resulted in estimated annual cost savings of Rs. 1,580,337 (4.26% reduction) and downtime reduction of 20,207 days (17.1% improvement). High-priority equipment received monthly preventive maintenance intervals (30 days), medium-priority equipment received bi-monthly intervals (60 days), and low-priority equipment received quarterly intervals (90 days). The implementation requires 14,470 annual PM activities, 59,194 inspections, and 4,604 calibrations, totaling 79,584 maintenance hours annually. The study demonstrates that data-driven equipment stratification can significantly improve maintenance efficiency and reduce operational costs in healthcare settings.

Medical equipment, clustering algorithm, maintenance scheduling, healthcare management, predictive maintenance

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