Background
In order to facilitate evidence-based decision on service provision, suitable methods that measure and compare
efficiency of service providers need to be employed. We explore the use of various data envelopment analysis (DEA)
approaches for measuring the performance of departments for urology, which is particularly suitable due the
distinctive set of services provided in this speciality.
Data & Methods
We use patient data which includes diagnoses, services, patient demographics and cost-weights from the Austrian
DRG system as well as hospital accounting data which has data on annual costs by cost centre any type of costs
(e.g. personnel, materials, etc.) as well as number of staffing. From this data we calculate department-specific
variables that describe inputs and outputs, as well as a set of quality indicators. These variables are used in various
data envelopment analysis (DEA) models. Advanced methods, such as slack-based methods (SBM), super-efficiency,
and regression-based DEA allow to investigate effects of certain variables on efficiency scores.
Results
Results reveal that various factors both within (e.g. pre-operative length of stay, bed occupancy rates) and outside
(e.g. average patient comorbidity, ratio of patients transferred from other hospitals) the control of the departments drive efficiency scores. Ownership structure does not play a decisive role since there are efficient departments among both publicly and privately operated hospitals.
Discussion
Measuring efficiency of service provision is a complex task because it is difficult to correctly quantify inputs and
outputs from available datasets. It is therefore vital to take into account the different structures that decisionmaking units (DMU) operate in, e.g., to include the relevant portion of costs operating rooms shared with other
specialities. Advanced DEA methods, such as slack-based measures of efficiency and regression-based DEA reveal
the importance of controlling for patient severity and other factors outside the control of the DMU. The analysis
Parallel Session 4: Efficiency and cost effectiveness analysis can identify areas of potential improvement in non-efficient departments that can be used in further strengthening efficiency of healthcare provision.