Department of Business Administration, University of Verona, Via Cantarane 24, 37129, Verona, Italy.
BMC Health Serv Res. 2021 Nov 27;21(1):1281. doi: 10.1186/s12913-021-07276-5.
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.
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.
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).
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.
本研究旨在评估意大利某地区公立医院的效率，包括质量产出、无效率决定因素以及随时间的效率变化。为实现这一目标，本研究使用了威尼托地区 2018 年和 2019 年的二次数据。
采用非参数方法——多阶段数据包络分析（DEA）——对 43 家医院进行了样本分析。我们确定了三类投入：资本投资（床位数）、劳动力（FTE）、运营费用。我们选择了五个效率产出（门诊就诊量、住院病人、门诊就诊收入、住院收入、床位使用率）和两个质量产出（死亡率和不当入院率）。对效率得分进行了估计，并分解为两个组成部分。然后进行了松弛分析。进一步，使用 Tobit 模型对 DEA 效率得分与内部和外部变量进行回归。最后，应用了 Malmquist 生产力指数。
威尼托地区的医院平均运营效率超过 95%。技术和规模无效率往往同时发生，77%的无效率医院需要采取缩小规模的策略来提高效率。需要大幅减少的投入包括全职行政人员和技术人员。医院的规模与服务人口规模和病人住院时间的关系是效率得分的重要因素。效率随时间下降的主要原因是技术变革（0.908）而不是效率变化（0.974）。
本研究揭示了政策和管理方面应做出的改进。医院规模是无效率的一个重要特征。总体而言，结果表明，医院重新组织非医疗人员以提高效率是明智的。此外，增加技术投资可以实现更高的效率水平。
