The purpose of this study was to measure Greek hospital performance using different input-output combinations, and to identify the factors that influence their efficiency thus providing policy makers with valuable input for the decision-making process. Using a unique dataset, we estimated the productive efficiency of each hospital through a bootstrapped data envelopment analysis (DEA) approach. In a second stage, we explored, using a bootstrapped truncated regression, the impact of environmental factors on hospitals' technical and scale efficiency. Our results reveal that over 80% of the examined hospitals appear to have a technical efficiency lower than 0.8, while the majority appear to be scale efficient. Moreover, efficiency performance differed with inclusion of medical examinations as an additional variable. On the other hand, bed occupancy ratio appeared to affect both technical and scale efficiency in a rather interesting way, while the adoption of advanced medical equipment and the type of hospital improves scale and technical efficiency, correspondingly. The findings of this study on Greek hospitals' performance are not encouraging. Furthermore, our results raise questions regarding the number of hospitals that should operate, and which type of hospital is more efficient. Finally, the results indicate the role of medical equipment in performance, confirming its misallocation in healthcare expenditure.
The paper aims to unravel the elements which constitute the decision-making process concerning new medical technologies in the context of the Greek Health System, where there are more than one decision makers. Computerized tomography is used as a case study. Using a unique data setting that refers to the total number of the Greek Public Hospitals, the pattern of adoption is outlined. At the second stage, data is associated with regional and geographical characteristics as well as information related to the hospital efficiency. A probit model is used for the factor analysis and a survival function hazard model for time to adopt. Results indicate that the models used are suitable for examining the factors influencing the adoption of medical technologies as well as the time that such technologies are adopted. It was found that the size of the hospital and its plenitude positively influence not only the probability of adoption but also the time of adoption of computerized tomography. Findings are encouraging; they support the use of the model in studying the adoption of other medical technologies too and can be used also as a tool by policy makers to assist the process of investment in new health technologies.
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