This study develops an analytical framework to optimize service strategies in primary healthcare, focusing on midwifery clinics that use electronic medical records. It employs the K-Means clustering algorithm to segment patients by visit time, diagnosis, and demographic characteristics, addressing the limitations of intuition-based decision-making for fluctuating patient volumes and resource needs. The clustering results provide an objective basis for designing adaptive interventions in staffing schedules, queue management, and pharmaceutical inventory, with the overall aim of improving patient satisfaction and operational efficiency
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