Optimal Timing of Physician Visits after Hospital Discharge to Reduce Readmission
Bibliographic record
Abstract
OBJECTIVE: To identify the optimal timing of in-person physician visit after hospital discharge to yield the largest reduction in readmission among elderly or chronically ill patients. DATA SOURCES/STUDY SETTING/EXTRACTION METHODS: We extracted insurance billing data on 620,656 admissions for any cause from 2002 to 2009 in Quebec, Canada. STUDY DESIGN: We used flexible survival models to estimate inverse probability weights for the precise timing (days) of in-person physician visit after discharge and weighted competing risk outcome models. PRINCIPAL FINDINGS: Readmission reduction associated with in-person physician visits (compared to none) was seen early after discharge, with 67.8 fewer readmissions per 1,000 discharges if physician visit occurred within 7 days (95 percent CI: 66.7-69.0), and 110.0 fewer readmissions within 21 days (95 percent CI: 108.2-111.7). The period of largest contribution to readmission reduction was seen in the first 10 days, while physician visits occurring later than 21 days after discharge did not further contribute to reducing hospital readmissions. Larger risk reductions were observed among patients in the highest morbidity level and for in-person follow-up with a primary care physician rather than a medical specialist. CONCLUSIONS: When provided promptly, postdischarge in-person physician visit can prevent many readmissions. The benefits appear optimal when such visit occurs within the first 10 days, or at least within the first 21 days of discharge.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".