Frontier efficiency of hospitals in United Arab Emirates: An application of data envelopment analysis
Why this work is in the frame
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Bibliographic record
Abstract
Background: Over the past four decades the United Arab Emirates (UAE) has undertaken a series of initiatives to improve the efficiency of hospitals. This study aims to examine the efficiency of private and public hospitals in the UAE. A clearer understanding of the technical efficiency of private and public hospitals will be important in shaping future policy reforms as well as assisting private investors that play an important role in the provision of healthcare within the UAE.Methods: This study employs the Data Envelopment Analysis (DEA) technique to measure the efficiency of both private and public hospitals in the UAE. Efficiency scores are calculated using both Banker, Charnes, and Cooper (BCC) and Charnes, Cooper, and Rhodes (CCR) models. The inputs into the models are number of beds, numbers of doctors, dentists, nurses, pharmacists and allied health staff, and administrative staff, while the outputs are the number of treated inpatients, outpatients, and average length of stay.Results: We find that public hospitals represent about a third of the total number of facilities but treat about 60% of the total number of patients. On the positive side we find that a third of the hospitals in the UAE to be efficient. On the other extreme we find that half the hospitals are less than half as efficient as the top hospital. The average technical efficiency of 96 hospitals is 59% using BCC model and 48% using CCR model. The results show no difference in the average efficiency scores between public and private hospitals, nor between foreign and domestically managed hospitals. We find that there is an almost equal probability to be an efficient or inefficient hospital in any of the emirates.Conclusions: The study contributes to the existing body of literature by establishing baseline technical efficiency scores that could be used in monitoring the efficiency effects of future policy changes. About 41% to 52% of the production factors are wasted during the service delivery process in the hospitals. Using the existing amount of resources, the amount of delivered outputs can be doubled, which can significantly impact patient outcomes. This leads us to believe that the ownership itself and foreign management is not sufficient to bring about improvements in efficiency. Interventions to improve the quality of management in hospitals could help to improve efficiency. National and international benchmarking of hospital performance help to provide more insights on sources of hospital inefficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it