Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region

• Main Authors: Luca Piubello Orsini (Author), Chiara Leardini (Author), Silvia Vernizzi (Author), Bettina Campedelli (Author)
• Format: Book
• Published: BMC, 2021-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12913-021-07276-5; 1472-6963
• Keywords: Data envelopment analysis; Efficiency; Quality; Public hospitals; Tobit; Malmquist productivity index; Public aspects of medicine; RA1-1270
• Source: BMC Health Services Research, Vol 21, Iss 1, Pp 1-15 (2021)

Abstract Background The objective of this study was to assess public hospital efficiency, including quality outputs, inefficiency determinants, and changes to efficiency over time, in an Italian region. To achieve this aim, the study used secondary data from the Veneto region for the years 2018 and 2019. Methods A nonparametric approach-that is, multistage data envelopment analysis (DEA)-was applied to a sample of 43 hospitals. We identified three categories of input: capital investments (Beds), labor (FTE), operating expenses. We selected five efficiency outputs (outpatient visits, inpatients, outpatient visit revenue, inpatient revenue, bed occupancy rate) and two quality outputs (mortality rate and inappropriate admission rate). Efficiency scores were estimated and decomposed into two components. Slack analysis was then conducted. Further, DEA efficiency scores were regressed on internal and external variables using a Tobit model. Finally, the Malmquist Productivity Index was applied. Results On average, the hospitals in the Veneto region operated at more than 95% efficiency. Technical and scale inefficiencies often occurred jointly, with 77% of inefficient hospitals needing a downsizing strategy to gain efficiency. The inputs identified as needing significant reductions were full-time employee (FTE) administrative staff and technicians. The size of the hospital in relation to the size of the population served and the length of patient stay were important factors for the efficiency score. The major cause of decreased efficiency over time was technical change (0.908) rather than efficiency change (0.974). Conclusions The study reveals improvements that should be made from both the policy and managerial perspectives. Hospital size is an important feature of inefficiency. On average, the results show that it is advisable for hospitals to reorganize nonmedical staff to enhance efficiency. Further, increasing technology investment could enable higher efficiency levels.