Towards Process-Oriented Hospital Structures; Drivers behind the Development of Hospital Designs
Bibliographic record
Abstract
Hospitals have been encouraged to develop more process-oriented designs, structured around patient needs, to better deal with patients suffering from multi-morbidity. However, most hospitals still have traditional designs built around medical specialties. We aimed to understand how hospital designs are currently developing and what the important drivers are. We built a typology to categorize all Dutch general hospitals (61), and we interviewed hospital managers and staff. The inventory showed three types of hospital building blocks: units built around specific medical specialties, clusters housing different medical specialty units, and centers; multi-specialty entities provide the most suitable structure for a process-oriented approach. Only some Dutch hospitals (5) are mainly designed around centers. However, most hospitals are slowly developing towards hybrid designs. Competitive drivers are not important for stimulating these redesigns. Institutional pressures from within the health care sector and institutional 'mimicking' are the main drivers, but the specific path they take is dependent on their 'heritage'. We found that hospital structures are more the result of incremental, path-dependent choices than 'grand-designs'. Although the majority of the Dutch general hospitals still have a general design built around medical specialties, most hospitals are moving towards a more process-oriented design.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".