Objective:To evaluate the service capability and operational efficiency of surgical departments in a hospital using data analysis models such as entropy weight TOPSIS and Boston matrix analysis, for references for optimizing medical resource allocation, promoting refined management and sustainable development of the hospital.Methods:The operational data of 24 surgical departments (A~X) in a tertiary public hospital in 2023 from its information system were extracted. The number of doctors, actual number of open beds, and average length of stay etc., were served as evaluation indicators. The TOPSIS and rank sum ratio methods were used to evaluate the medical service capabilities, the data envelopment analysis (DEA) was used to analyze operational efficiency, and Boston matrix analysis was used for departmental classification.Results:In terms of medical service capabilities, the top 5 departments were F, S, K, D, and C ( Ci>0.41), all of which were in the excellent category; The bottom three departments were L, N, and G ( Ci<0.04), all of which were in the poor range. From the perspective of operational efficiency, 8 departments had achieved strong DEA effectiveness, while the remaining 16 departments were non DEA effective, resulting in resource redundancy or insufficient output. According to the Boston Matrix analysis, 8 departments (stars) had strong medical service capabilities and high operational efficiency; 6 departments (cash cows) had poor medical service capabilities but high operational efficiency, and 8 departments (dogs) had poor medical service capabilities and operational efficiency; 2 departments (question marks) had strong medical service capabilities but low operational efficiency. Conclusions:This study comprehensively applied multiple data models to objectively and comprehensively evaluate the service capabilities and operational efficiency of surgical departments. Hospitals could develop corresponding resource allocation optimization strategies based on the Boston matrix classification results, combined with the disease characteristics and business scale configuration of each department.

Objective:To evaluate the service capability and operational efficiency of surgical departments in a hospital using data analysis models such as entropy weight TOPSIS and Boston matrix analysis, for references for optimizing medical resource allocation, promoting refined management and sustainable development of the hospital.Methods:The operational data of 24 surgical departments (A~X) in a tertiary public hospital in 2023 from its information system were extracted. The number of doctors, actual number of open beds, and average length of stay etc., were served as evaluation indicators. The TOPSIS and rank sum ratio methods were used to evaluate the medical service capabilities, the data envelopment analysis (DEA) was used to analyze operational efficiency, and Boston matrix analysis was used for departmental classification.Results:In terms of medical service capabilities, the top 5 departments were F, S, K, D, and C ( Ci>0.41), all of which were in the excellent category; The bottom three departments were L, N, and G ( Ci<0.04), all of which were in the poor range. From the perspective of operational efficiency, 8 departments had achieved strong DEA effectiveness, while the remaining 16 departments were non DEA effective, resulting in resource redundancy or insufficient output. According to the Boston Matrix analysis, 8 departments (stars) had strong medical service capabilities and high operational efficiency; 6 departments (cash cows) had poor medical service capabilities but high operational efficiency, and 8 departments (dogs) had poor medical service capabilities and operational efficiency; 2 departments (question marks) had strong medical service capabilities but low operational efficiency. Conclusions:This study comprehensively applied multiple data models to objectively and comprehensively evaluate the service capabilities and operational efficiency of surgical departments. Hospitals could develop corresponding resource allocation optimization strategies based on the Boston matrix classification results, combined with the disease characteristics and business scale configuration of each department.