Does implementation of a hospitalist program in a Canadian community hospital improve measures of quality of care and utilization? an observational comparative analysis of hospitalists vs. traditional care providers
Bibliographic record
Abstract
BACKGROUND: Despite the growth of hospitalist programs in Canada, little is known about their effectiveness for improving quality of care and use of scarce healthcare resources. The objective of this study is to compare measures of cost and quality of care (in-hospital mortality, 30-day same-facility readmission, and length of stay) of hospitalists vs. traditional physician providers in a large Canadian community hospital setting. METHODS: We performed a retrospective analysis of data from the Canadian Institute for Health Information (CIHI) Discharge Abstract Database, using multivariate logistic and linear regression analyses comparing performance of four provider groups of traditional family physicians (FPs), traditional internal medicine subspecialists (other-IM), family physician-trained hospitalists (FP-Hospitalist), and general internal medicine-trained hospitalists (GIM-Hospitalist). RESULTS: Compared to traditional FPs, FP-Hospitalists and GIM-Hospitalists demonstrate lower mortality [OR 0.881, (CI 0.779 - 0.996); and OR 0.355, (CI 0.288 - 0.436)] and readmission rates [OR 0.766, (CI 0.678 - 0.867); and OR 0.800, (CI 0.675 - 0.948)]. Compared to traditional FPs, GIM-Hospitalists appear to improve length of stay [OR-2.975, (CI -3.302 - -2.647)] while FP-Hospitalists perform similarly [OR 0.096, (CI -0.136 - 0.329)]. Compared to other-IM, GIM-Hospitalists have similar performance on all measures while FP-Hospitalists show a mixed impact. CONCLUSIONS: Compared to traditional family physicians, hospitalists appear to improve measures of quality and resource utilization. Specifically, hospitalists demonstrate lower in-hospital mortality and 30-day readmission rates while improving (or at least showing similar) length of stay. Compared to traditional subspecialists, hospitalists demonstrate similar performance despite looking after sicker and more complex medical patients.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".