Evaluation of Nosocomial Infection Management Efficiency Based on the Data Envelopment Analysis Model

Jin Wang,1,* Gan Wang,2,3,* Chaoyi Qi4 1Department of Healthcare-associated Infection Management, Qingdao Municipal Hospital, Qingdao, People’s Republic of China; 2Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China; 3School of Public Health, Fudan University, Shanghai, People’s Republic of China; 4Department of Anorectal Surgery, Taian Hospital of Traditional Chinese Medicine, Taian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Gan Wang, Email [email protected]: This study used data envelopment analysis (DEA), to assess relative efficiency of infection control in different clinical departments of the hospital for performance evaluation purposes.Methods: All wards and departments from January to December 2022 were selected as decision units, and five input and two output indicators related to infection prevention and control were determined using DEA. Pure technical efficiency was evaluated using the Banker–Charnes–Cooper (BCC) model.Results: In the study, the input-output indexes of the 27 clinical departments varied significantly. The average values of technical efficiency, pure technical efficiency, scale efficiency, and comprehensive benefit were 0.987, 0.995, 0.992, and 0.980, respectively. Among the 27 departments, 52% exhibited constant returns to scale, 44% showed increasing returns to scale, and 4% had decreasing returns to scale. In the context of DEA, 44% of the departments were classified as highly efficient, indicating that their input-output ratios had reached an optimal state. Meanwhile, 56% of the departments were identified as non-DEA efficient, suggesting that there was room for improvement in their input-output efficiency.Conclusion: The improvement of input-output indexes of non-DEA effective clinical departments was defined by the BCC model. Use of DMUs could improve the efficiency of inventory control by optimizing the allocation of inventory control resources and refining inventory control measures.Keywords: data envelopment analysis, performance evaluation, hospital infection, efficiency