Effect of an Intervention to Improve Team Coordination on Patients Who are Likely to be Discharged on General Internal Medicine
Bibliographic record
Abstract
Summary Effective discharge planning is important to ensuring a high quality of patient care and operational efficiency. The general internal medicine (GIM) environment is very complex and fluid, with multiple health professions providing care for patients. This makes coordination of discharges difficult, even with structured daily interprofessional rounds. The purpose of this case-control study was to evaluate a discharge notification form that predicts next-day discharges. The main measures of the study, which took place in GIM wards at two academic teaching hospitals, were the completion and accuracy of the discharge forms, length of stay, discharge times, post-discharge admissions, and emergency department visits. Seventy-six of 200 patients studied had information completed on the discharge notification form. The overall effect appeared to move discharges earlier in the day, while having no effect on length of stay. Patients whose information was completed on the discharge notification form were less likely to have an emergency department visit within 30 days post-discharge. The use of a discharge notification form appears to move discharges earlier in the day, without increasing length of stay. Further refinement and evaluation is necessary to increase usage and assess the impact on outcomes of care.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".