Process reengineering in public hospitals. Reinventing the wheel?
Bibliographic record
Abstract
The purpose of this paper is that of analyzing the hypothesis that, as originally stated by its creators, the reengineering methodology for the improvement of efficiency and productivity, cannot be successfully implemented in Spanish public hospitals, and in fact, the so called experiences do not keep with the basics of such an approach. The technique employed for this study consisted, on one hand, of reviewing and comparing the literature published on this subject and, on the other, on experimentation by means of a case study conducted at a public hospital for the purpose of testing out the outlined hypothesis. The review and comparison of works previously published on this subject revealed that the technique for improving on-the-job efficiency according to the theory-based concepts of the process reengineering approach is not adaptable to public hospitals in Spain. The case study supported this finding, additionally highlighting the fact that in order for any relatively major changes in the working processes at public hospitals to be recommended, a number of organizational and human factors must be taken into consideration as aspects involved regardless of the methodological approach taken. The indiscriminate implementation in public hospital administration of trends currently fashionable in the business administration field may defeat its own purpose if these trends are not previously evaluated prior to being implemented. An assessment must first be made as to their being suited to the intended purpose.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".