The impact of management on hospital performance
Bibliographic record
Abstract
Abstract There is a prevailing popular belief that expenditure on management by health‐care providers is wasteful, diverts resources from patient care, and distracts medical and nursing staff from getting on with their jobs. There is little existing evidence to support either this narrative or counter‐claims. We explore the relationship between management and public sector hospital performance using a fixed effects empirical econometric specification on a panel data set consisting of all 129 non‐specialist acute National Health Service (NHS) hospitals in England for the financial years 2012/13 to 2018/19. Measures of managerial input and quality of management practice are constructed from NHS Electronic Staff Records and NHS Staff Survey data. Hospital accounts and Hospital Episode Statistics data are used to construct five measures of financial performance and of timely and high‐quality care. We find no evidence of association either between quantity of management and management quality or directly between quantity of management and any of our measures of hospital performance. However, there is some evidence that higher‐quality management is associated with better performance. NHS managers have limited discretion in performing their managerial functions, being tightly circumscribed by official guidance, targets, and other factors outside their control. Given these constraints, our findings are unsurprising.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".