Impact of a family medicine-based transitional care intervention on readmission and length of stay: a pilot study
Bibliographic record
Abstract
Transitional care interventions, often led by hospital specialists, have mixed impact on reducing readmissions. Interventions led by family physicians may be more promising. The objective of this study was to evaluate the impact of a family-medicine-based intervention in reducing the incidence of emergency department (ED) visits, hospital readmissions and the length of stay (LOS) of older patients. A quasi-experimental pilot-study was conducted at a Family Medicine Group (FMG) in Montreal. Thirty-five patients discharged from the FMG-affiliated hospital between July 2014-2015 were compared to 68 historical controls discharged from the same hospital one year prior. Inclusion criteria were: 65+ years, rostered at FMG, high-risk of readmission; and discharged to home/senior residence. Patients’ charts were reviewed to determine a composite outcome of all-cause rates of acute hospital use (ED visit/hospital readmissions) and LOS at 30, 60, 90 and 180-days post-discharge. We found no statistically significant differences in acute hospital use rates between groups. LOS was statistically significantly shorter at 90- and 180-days for patients compared to controls: Incidence Rate Ratio (95% Confidence Interval) at 90-days: 0.66 (0.64-0.69) and at 180-days: 0.49 (0.43-0.55). Our study provides support to the impact of a family-medicine-based transitional care intervention in reducing the LOS of vulnerable older patients readmitted to hospital.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".