Determining the Technical Efficiency of Hospitals in Tabriz City Using Data Envelopment Analysis for 2013-2014
Bibliographic record
Abstract
<p><strong>BACKGROUND &amp; OBJECTIVES: </strong>This study was conducted to evaluate the technical efficiency of hospitals (training and non-training hospitals of Tabriz city) affiliated with the Medical Science University, based on performance indicators and mathematical model of data envelopment analysis (DEA) in 2014.</p><p><strong>METHODS:</strong><strong> </strong>The present research is a cross sectional study conducted to assess the scale, technical and managerial efficiency of hospitals from2013 to2014. Then, a comparison of the collected data was made among the hospitals under study. The model of minimization of production factors and variable return was used in analyzing the data.</p><p><strong>RESULTS:</strong> The collected information included two input groups which consisted of the number of physicians (general physicians and specialists), total personnel and active beds, and output groups which consisted of the number of out-patients and bed occupancy rate. Then, the technical, scale and managerial efficiency of the hospitals were calculated and the efficient (Performance Coefficient of E =1) and inefficient (below 1) hospitals were obtained. The average technical, scale and managerial efficiencies in both 2013 and 2014 was equal to 0.817, 0.956 and 0.856, respectively.</p><p><strong>CONCLUSION:</strong><strong><em> </em></strong>Hospitals having lower efficiency can model efficient reference hospitals, so as to increase their performance and also approach the efficiency border by better management of human and financial resources.</p>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".