The Future Hospital in Global Health Systems: The Future Hospital Within the Healthcare System
Bibliographic record
Abstract
Future hospitals must be able to adapt in many ways to the changing demands on their roles and functions within evolving healthcare delivery infrastructures. These include changing population structures and needs, new models of healthcare provision, technological advances, and innovations in design, all while enhancing their environmental sustainability. This article sets out the issues that those determining healthcare policy and designing future hospitals must consider if they are to become and remain fit for purpose within the wider health and social care system. It also examines the need for, and challenges to, strategic healthcare planning, creating future hospitals that are sustainable, net-zero carbon organisations, and ensuring resilience in the face of a range of potential shocks. Future hospitals play a crucial role in healthcare worldwide, regardless of the country's income level. Hospitals cannot be viewed without broader health system changes, infrastructure, community and cultural factors, staffing and other considerations. We anticipate that future hospitals will enhance population health in all settings and support the move towards more consumer-centric healthcare. We urge clinical and policy planners to consider the factors discussed carefully to maximise the benefits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".