Team-based versus traditional primary care models and short-term outcomes after hospital discharge
Bibliographic record
Abstract
BACKGROUND: Strategies to reduce hospital readmission have been studied mainly at the local level. We assessed associations between population-wide policies supporting team-based primary care delivery models and short-term outcomes after hospital discharge. METHODS: We extracted claims data on hospital admissions for any cause from 2002 to 2009 in the province of Quebec. We included older or chronically ill patients enrolled in team-based or traditional primary care practices. Outcomes were rates of readmission, emergency department visits and mortality in the 90 days following hospital discharge. We used inverse probability weighting to balance exposure groups on covariates and used marginal structural survival models to estimate rate differences and hazard ratios. RESULTS: We included 620 656 index admissions involving 312 377 patients. Readmission rates at any point in the 90-day post-discharge period were similar between primary care models. Patients enrolled in team-based primary care practices had lower 30-day rates of emergency department visits not associated with readmission (adjusted difference 7.5 per 1000 discharges, 95% confidence interval [CI] 4.2 to 10.8) and lower 30-day mortality (adjusted difference 3.8 deaths per 1000 discharges, 95% CI 1.7 to 5.9). The 30-day difference for mortality differed according to morbidity level (moderate morbidity: 1.0 fewer deaths per 1000 discharges in team-based practices, 95% CI 0.3 more to 2.3 fewer deaths; very high morbidity: 4.2 fewer deaths per 1000 discharges, 95% CI 3.0 to 5.3; p < 0.001). INTERPRETATION: Our study showed that enrolment in the newer team-based primary care practices was associated with lower rates of postdischarge emergency department visits and death. We did not observe differences in readmission rates, which suggests that more targeted or intensive efforts may be needed to affect this outcome.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".