Analysis of operation efficiency and resource allocation of clinical departments in a hospital based on data envelopment andlysis
10.3760/cma.j.cn111325-20221223-01101
- VernacularTitle:基于数据包络分析的某医院临床科室运行效率和资源配置分析
- Author:
Xiaoxiong HAO
1
;
Lei HAN
;
Xiaozhi JIN
;
Chenguang LI
;
Lüshuai HUANG
Author Information
1. 中部战区总医院卫勤部，武汉 430070
- Keywords:
Operation efficiency;
Data envelopment analysis;
BCC model;
Input redundancy;
Allocation of medical resources;
Clinical department
- From:
Chinese Journal of Hospital Administration
2023;39(5):352-357
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a calculation model for the operational efficiency and resource allocation of clinical departments in hospitals, for references for hospitals to optimize resource allocation.Methods:The informations including hospitalization time, nursing grade, etc. of inpatients admitted by 32 clinical departments in a tertiary public hospital from January to December in 2021 were extracted. A data envelopment analysis method was conducted on the operation efficiency and input edundancy of the departments. The K-means algorithm was used to divide inpatients into 3 categories according to the level of medical workload. Taking the numbers of doctors, nurses and beds as the input indicators, and the numbers of patients in the 3 categories as the output indicators, a BCC model 1 was established to evaluate the efficiency of resources invested by clinical departments into professional human value. At the same time, a BCC model 2 was established with the total number of patients admitted and medical income as the output indicators to evaluate the efficiency of resources invested by clinical departments into economic benefits.Results:A total of 38 147 inpatients were enrolled. There were 14 departments with overall technical efficiency (OTE) =1.000 in the BCC model 1, 10 departments with OTE=1.000 in the BCC model 2, and 8 departments with OTE=1.000 in the 2 models. As for the input redundancy, 6 departments had high input redundancy in the BCC model 1, 11 departments had high input redundancy in the BCC model 2, and 4 departments had high input redundancy in both models.Conclusions:The model established by this study could effectively evaluate the operational efficiency and input redundancy of clinical departments, identify departments with high workload and low economic benefits, and provide reference for the rational allocation of medical resources in hospitals.
