The independent association of provider and information continuity on outcomes after hospital discharge: Implications for hospitalists
Bibliographic record
Abstract
BACKGROUND: Since hospitalist physicians do not frequently see patients in follow-up after discharge from the hospital, patient continuity of care will decrease. To determine how this influenced patient outcomes, we examined the independent association of several physician continuity and information continuity measures on death or urgent readmission after discharge from hospital. DESIGN: Multicenter, prospective cohort study of patients discharged to the community after elective or emergency hospitalization. We measured three physician continuity scores (preadmission; hospital; and postdischarge) and two information continuity scores (discharge summary; postdischarge visit information) as time-dependent covariates. Continuity scores ranged from 0 (perfect discontinuity) to 1 (perfect continuity). The primary outcomes were time to all-cause death or urgent readmission. RESULTS: A total of 3876 people were followed for a median of 175 days. Death rate was 2.6 events per 100 patient-years observation (pys) (95% confidence interval [CI], 2.0-3.4) and urgent readmission rate was 19.6 events per 100 pys (95% CI, 15.9-24.3). After adjusting for important covariates and other continuity scores, increased preadmission physician continuity was independently associated with a decreased risk of urgent readmission (adjusted hazard ratio 0.94 [95% CI, 0.91-0.98] for each absolute increase in continuity of 0.1). Other continuity measures-including hospital physician continuity-were not associated with either outcome. CONCLUSIONS: After discharge from the hospital, increased continuity with physicians who routinely treated the patient prior to the admission was significantly and independently associated with a decreased risk of urgent readmission. These data suggest that continuity with the hospital physician after discharge did not independently influence the risk of patient death or urgent readmission.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".