Re-designing Hospital Care: Learning from the Experience of Hospital Medicine in Canada
Bibliographic record
Abstract
The emergence of the hospitalist model (a model of inpatient care delivery by physicians referred to as hospitalists who spend the majority of their time in the hospital setting) has been a major development in the Canadian healthcare landscape over the past decade. Similar to the United States, the number of hospitalist programs in Canada has grown exponentially since the late 1990's. More recently, the model is being adopted in other countries such as Singapore and Brazil. The Canadian hospitalist model is still evolving, but its development can provide learning opportunities for policy makers and healthcare practitioners in other countries who are developing their own versions of this health delivery model. This article provides a comparative overview of the development of hospital medicine (an emerging medical specialty dedicated to the delivery of comprehensive medical care to hospitalized patients) in Canada and the United States and proposes strategies for more effective integration of Canadian hospitalists into the medical establishment. It outlines the results of national hospitalist surveys in Canada and discusses some of the main challenges and opportunities facing its adoption. National surveys have demonstrated an increase in the number of physicians providing hospitalist care in Canada. There is increasing evidence for the effectiveness of the hospitalist model in enhancing the efficiency of resource utilization and improving quality of care indicators. Hospitalists across North America are increasingly taking part in quality improvement initiatives and establishing themselves as major contributors to the broader healthcare system. Despite their success, Canadian hospitalists continue to face challenges gaining acceptance for alternate funding models, establishing sustainable working conditions, and overcoming criticisms of discontinuity of care by improving communication with other physician groups. Healthcare leaders in other parts of the world that are looking to develop hospitalist programs need to consider such challenges to ensure successful adoption of this emerging inpatient care model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".