EFFECT OF A PRIMARY CARE VIRTUAL WARD ON THE READMISSION RATES OF OLDER PATIENTS POST DISCHARGE
Bibliographic record
Abstract
Transitional care programs to reduce readmissions have had mixed results. Interventions led by primary care physicians may have a better impact. Our objective is to evaluate the impact of a Family Medicine-based Virtual Ward (VW) intervention at the Jewish General Hospital in reducing the emergency room (ER) visits, readmissions and the length of stay of older patients. Our study is quasi-experimental with a historical control group. All 42 patients who received the intervention between July 1st 2014 and June 30th 2015 were included. These patients were compared to all 68 consecutive historical controls discharged from the hospital one year prior. Inclusion criteria were: 65 years or older, having a family doctor at the clinic, a high risk of readmission (LACE score above 10) and being discharged to home/senior residence. The patients’ charts were reviewed to determine rates of ER visits and readmissions at 30, 60, and 90 days after discharge and cumulative length of stay (LOS) for all readmissions within 90 days. Clinically meaningful decreases in ER visits, readmission rates and LOS were observed in the VW group compared to the control group; however, these differences were not statistically significant. ER visits at 30, 60, and 90 days were decreased by 2%-17%. Readmissions were decreased by 22%-26%. LOS at 90 days was decreased by 35%. Replication in a larger sample is warranted to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".