Impact of Team-Based Care on Emergency Department Use
Bibliographic record
Abstract
<h3>PURPOSE</h3> We sought to assess the impact of team-based care on emergency department (ED) use in the context of physicians transitioning from fee-for-service payment to capitation payment in Ontario, Canada. <h3>METHODS</h3> We conducted an interrupted time series analysis to assess annual ED visit rates before and after transition from an enhanced fee-for-service model to either a team capitation model or a nonteam capitation model. We included Ontario residents aged 19 years and older who had at least 3 years of outcome data both pretransition and post-transition (N = 2,524,124). We adjusted for age, sex, income quintile, immigration status, comorbidity, and morbidity, and we stratified by rurality. A sensitivity analysis compared outcomes for team vs nonteam patients matched on year of transition, age, sex, rurality, and health region. <h3>RESULTS</h3> We compared 387,607 team and 1,399,103 nonteam patients in big cities, 213,394 team and 380,009 nonteam patients in small towns, and 65,289 team and 78,722 nonteam patients in rural areas. In big cities, after adjustment, the ED visit rate increased by 2.4% (95% CI, 2.2% to 2.6%) per year for team patients and 5.2% (95% CI, 5.1% to 5.3%) per year for nonteam patients in the years after transition (<i>P</i> <.001). Similarly, there was a slower increase in ED visits for team relative to nonteam patients in small towns (0.9% [95% CI, 0.7% to 1.1%] vs 2.9% [95% CI, 2.8% to 3.1%], <i>P</i> <.001) and rural areas (‒0.5% [95% CI, –0.8% to 0.2%] vs 1.3% [95% CI, 1.0% to 1.6%], <i>P</i> <.001). Results were much the same in the matched analysis. <h3>CONCLUSIONS</h3> Adoption of team-based primary care may reduce ED use. Further research is needed to understand optimal team composition and roles.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".