Efficiency and productivity change in Ukrainian health care

The study analyses technical efficiency and efficiency change of 193 community hospitals and polyclinics across Ukraine, for the years 1997–2001. These facilities are a subset of the medical institutions in rural Ukraine; they are identical w.r.t. their function in the health system and share the same departmental structure. The data comprise the number of beds in the hospitals, the number of staff employed in the hospitals as well as the polyclinics connected to the hospitals, the number of inpatient and outpatient admissions as well as the number of surgical procedures, lab tests, X-rays performed and the number of deaths and deaths after surgery. Because of the known sensitivity of traditional nonparametric frontier estimators to outlier observations, we employ an order-m estimator, a robust technique, to assess the efficiency of these health care providers as well as changes of their productivity time. The efficiency scores are calculated with an output-oriented model; they are close to unity for hospitals whereas polyclinics seem somewhat less efficient. The Malmquist-indices averaged over all observations are close to unity indicating that productivity does not change over during our observation period. But, depending on the period and the region, substantial deviations from unity can be observed.

Estimating the efficiency of general practitioners controlling for case mix and outlier effects

Data on some 600 general practitioners located in the same region of Austria for the years 2001–2003 are analyzed using Data Envelopment Analysis. The available information comprises patient numbers by age category, case mix, and resource use; outliers are removed with a procedure based on the order-m estimator. The results do not vary much over different samples and specifications and imply an average inefficiency of around 15%. Throughout the observation period, only slight changes in total factor productivity are observed.     

Efficiency of hospitals in Germany: a DEA-bootstrap approach

Various attempts to assess the performance of German hospitals have generated a wide range of estimates regarding their efficiency. These attempts were based on different, often rather small data sets consisting of heterogeneous hospitals; the techniques applied range from simple benchmarking approaches to studies which employ Data Envelopment Analysis (DEA). Some studies report ‘dramatic differences in efficiency’ and propose savings potentials of 50%; others find an average efficiency in excess of 95% and characterize almost 75% of their observations as fully efficient. This study presents results for two datasets representative of two segments of the German hospital system. These segments comprise all hospitals that have one internal medicine and one surgery department; the hospitals are located in the old federal states of Germany. None of the hospitals provides tertiary care. DEA can be applied because all hospitals offer a comparable quality and range of services. The results were estimated with a DEA-bootstrapping procedure and suggest an average bias–corrected efficiency of around 80%.     

The Effect of Sample Size on the Mean Efficiency in DEA: Comment

Zhang and Bartels (1998) show formallyhow DEA efficiency scores are affected by sample size. They demonstratethat comparing measures of structural inefficiency between samplesof different sizes leads to biased results. This note arguesthat this type of sample size bias has much wider implicationsthan suggested by their example. Models which implicitly restrictthe comparison set like some models for non-discretionary variableslead to biased efficiency scores as well. A reanalysis of theBanker and Morey (1986b) data shows that the efficiency scoresderived there are significantly influenced by the variation insample size implicit in their model.