An Emergency Department–Based Nurse Discharge Coordinator for Elder Patients: Does It Make a Difference?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of an emergency department (ED)-based nurse discharge plan coordinator (NDPC) on unscheduled return visits within 14 days of discharge, satisfaction with discharge recommendations, adherence with discharge instructions, and perception of well-being of elder patients discharged from the ED. METHODS: Patients aged 75 years and older discharged from the ED of the Sir Mortimer B. Davis-Jewish General Hospital were recruited in a pre/post study. During the pre (control) phase, study patients (n = 905) received standard discharge care. Patients in the post (intervention) phase (n = 819) received the intervention of an ED-based NDPC. The intervention included patient education, coordination of appointments, patient education, telephone follow-up, and access to the NDPC for up to seven days following discharge. RESULTS: Patients in the two groups were similar with respect to gender and age. However, the patients managed by the ED NDPC appeared to be, at baseline, less autonomous, frailer, and sicker. The unadjusted relative risk for unscheduled return visits within 14 days of discharge was 0.79 (95% confidence interval [95% CI] = 0.62 to 1.02). A relative risk reduction of 27% (95% CI = 0% to 44%) for unscheduled return visits was observed for up to eight days postdischarge, and a relative risk reduction of 19% (95% CI = -2% to 36%) for unscheduled return visits was observed for up to 14 days postdischarge. Significant increases in satisfaction with the clarity of discharge information and perceived well-being were also noted. CONCLUSIONS: An ED-based NDPC, dedicated specifically to the discharge planning care of elder patients, reduces the proportion of unscheduled ED return visits and facilitates the transition from ED back home and into the community health care network.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".