<![CDATA[Background: Healthcare service efficiency has become a critical concern due to increasing patient demand and limited resources, where prolonged waiting time is a key indicator of inefficiency and reduced service quality. However, existing studies predominantly focus on prediction or aggregate analysis without integrating temporal characteristics and process-oriented perspectives to explicitly identify bottlenecks and patient heterogeneity. Objective:This study aims to evaluate healthcare service efficiency through a data-driven framework that integrates temporal analysis, bottleneck identification, and clustering techniques. Methods: The study utilizes 3,335 patient records and analyzes waiting time and service time using descriptive statistics, distribution analysis, rule-based bottleneck classification, and K-Means clustering with data normalization. Results: The results show a significant imbalance between waiting time (mean = 40.03 minutes; max = 1428; Standard Deviation (SD) = 103.01) and service time (mean = 4.45 minutes; max = 59; SD = 7.88), indicating high variability and extreme delays. Bottleneck analysis reveals that 79.7% of cases are dominated by waiting time, while only 20.3% are related to service processes. Clustering identifies three distinct patterns: efficient service (19.34 min waiting), queue bottleneck (505.25 min waiting), and complex service (29.70 min service time). Conclusions & Implications: This study contributes by providing an integrated analytical framework that uncovers both structural inefficiencies and heterogeneous patient patterns. In conclusion, inefficiency is predominantly driven by queue-related delays, highlighting the importance of optimizing patient flow management rather than solely improving clinical capacity.]]>
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