International Models for Hospital Medicine: A Global Scoping Review of Implementation, Outcomes, and Best Practices
Bibliographic record
Abstract
Description This scoping review systematically examines the implementation and evolution of hospital medicine models across eight countries and regions (United States, Canada, United Kingdom, Japan, South Korea, Taiwan, Singapore, and Brazil) to understand how different healthcare systems have adapted the hospitalist model since its inception in 1996. Purpose: The primary objective is to consolidate existing evidence on international hospital medicine models, describe diverse care delivery approaches, and identify transferable lessons that can enhance hospital medicine implementation globally. This research addresses the gap in comprehensive documentation of how hospital medicine has evolved outside the United States and explores adaptations to varying healthcare systems, funding mechanisms, and cultural contexts. Methodology: Using PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, this study conducted comprehensive searches across multiple medical databases (PubMed, LILACS, CINAHL, Scopus, and SciELO) covering literature from 1996 to 2025. The analysis systematically evaluates four key dimensions across countries: (1) care delivery models and organizational structure, (2) educational and training approaches, (3) financial support and funding mechanisms, and (4) clinical and economic outcomes. Expected Outcomes: This research aims to demonstrate how hospitalist models consistently achieve improved outcomes through reduced length of stay, decreased healthcare costs, and maintained or improved quality of care across diverse healthcare settings. The study will identify common implementation barriers including role definition challenges, training pathway development, sustainable funding mechanisms, and integration with existing specialty services. Significance: With the global aging population and increasing healthcare complexity, this research provides evidence-based insights for healthcare systems considering hospitalist model adoption or refinement. The findings will inform strategic planning for hospital medicine development, support international collaboration efforts, and contribute to formal recognition of hospital medicine as a distinct specialty requiring appropriate compensation and career advancement opportunities. This work is particularly relevant for addressing the quadruple aim of improving patient experience, enhancing population health, reducing costs, and supporting provider wellbeing in the context of mounting pressures on healthcare systems worldwide.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".