Measuring the Effect of Size on Technical Efficiency of the United Arab Emirates Hospitals
Bibliographic record
Abstract
<p><strong>OBJECTIVE:</strong> The main purpose of this study is to estimate the technical efficiency of the United Arab Emirates (UAE) hospitals and examine the effect of hospital size on estimated technical efficiency scores.</p><p><strong>METHODS: </strong>Using 2012 data from Ministry of Health, Dubai Health Authority, and Health Authority in Abu Dhabi,<strong> </strong>we employed a nonparametric method, data envelopment analysis (DEA), to estimate the technical efficiency of 96 private and governmental hospitals in the UAE. Efficiency scores are calculated using both Banker, Charnes, and Cooper (BCC) and Charnes, Cooper, and Rhodes (CCR) models. </p><p><strong>RESULTS: </strong>The average technical efficiency of the UAE hospitals is estimated at 59% based on the BBC model and at 48% based on the CCR model. The optimal size of a hospital in the UAE is between 100 to 300 beds. We also found evidence of economies of scope between the provision of outpatient and inpatient care in the UAE hospitals.</p><p><strong>CONCLUSION: </strong>Our findings indicate that only one third of the UAE hospitals are technically efficient. There is evidence to suggest that there are considerable efficiency gains yet to be made by many UAE hospitals. Additional empirical research is needed to inform future health policies aimed at improving both the technical and allocative efficiency of hospital services in the UAE. </p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".