The Future Hospital in Global Health Systems: The Future Hospital as an Entity
Bibliographic record
Abstract
Health care is changing rapidly. Hospitals are, and will remain, an essential setting to deliver it. We discuss how to maximise the benefits of hospitals in the future in different geographic and health system settings, highlighting a series of cross-cutting issues. We do this by exploring the evolving roles of hospitals and the main factors that we must consider as they adapt. These include changing population and disease profiles, the impact of evolving technology, and new concepts in hospital design and planning. Our focus is on delivering high-quality, patient-centred care while ensuring equitable access, even if strategic decisions require compromise across these functions. The COVID-19 pandemic has shown the importance of hospitals in societies while also revealing the limitations of current structures and the potential of technology to transform hospital services within the broader healthcare system. The aim of this multidisciplinary perspective is to provide an overview of pertinent issues whilst highlighting the challenges and opportunities in optimising future hospital planning, construction, design, and development in high-income (HIC) and low -and medium-income country (LMIC) settings.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".