Study on the operational efficiency of clinical departments in a Grade III general hospital

To analyze the operational efficiency of clinical departments in tertiary general hospitals, thereby providing a basis for optimizing management processes and adjusting the allocation of departmental resources.

Given the variations in the size of clinical departments and the difficulty of rapidly adjusting resource allocation in the short term, this study employed the DEA-BCC model to analyze efficiency. Using the DEA composite technical efficiency score as the dependent variable and incorporating three environmental variables-departmental characteristics, designation as a national or provincial key specialty, and the proportion of doctors holding senior professional titles-a Tobit regression model was utilized to analyze the factors influencing the operational efficiency of clinical departments.

The comprehensive technical efficiency of this clinical department was 0.756, the pure technical efficiency was 0.856, and the scale efficiency was 0.888. Tobit analysis revealed a significant positive correlation between the proportion of senior-level physicians and the comprehensive technical efficiency of clinical departments; neither the department's attributes nor its status as a national or provincial key speciality reached statistical significance.

There are disparities in operational efficiency across clinical departments, with some departments exhibiting a mismatch between inputs and outputs. It is necessary to optimize resource allocation in line with each department's functional role and the characteristics of returns to scale while enhancing the capacity to diagnose and treat complex and critical cases, thereby achieving a synergistic improvement in both efficiency and healthcare service capacity. Compared with previous efficiency studies that treated the hospital as a whole as the decision-making unit, this study focuses on clinical departments within a tertiary general hospital. It utilizes the CMI to adjust for differences in case mix and combines this with DEA efficiency analysis, slack variables, sensitivity analysis, and department-specific contextual variables to identify variations in resource allocation at the departmental level, thereby providing guidance for refined resource management in hospitals.