Data Envelopment Analysis in Healthcare Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter explores the applications, contributions, limitations, and challenges of data envelopment analysis (DEA) in healthcare management. DEA, a non-parametric method used for evaluating the efficiency of decision-making units, has found extensive applications in healthcare sectors such as hospital management, nursing, and outpatient services. The review consolidates findings from a broad range of studies, highlighting DEA's significant contributions to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. However, despite DEA's robust applications, the chapter also identifies several limitations and challenges, including the selection of inputs and outputs, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, homogeneity assumption, and the static nature of traditional DEA models. These challenges underscore the need for further research and methodological advancements in applying DEA in healthcare management.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.001 |
| 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