Impact of a geriatric emergency management nurse on thirty-day emergency department revisits: a propensity score matched case-control study
Bibliographic record
Abstract
Abstract Objectives Geriatric Emergency Management (GEM) nurses aim to reduce adverse outcomes by addressing unique needs of older adults seen in emergency departments (EDs), but evidence to demonstrate their impact on ED care transitions is mixed. We evaluated the impact of implementing a GEM nurse model in a local ED on thirty-day revisits using propensity score matching to control for relevant patient characteristics. Methods A case-control design was used to analyze older adult patients who were triaged to a stretcher at an ED in Lévis, Québec, from October 2018 to September 2019. We used propensity score matching to compare patients who received the GEM nurse intervention with control patients who did not receive the intervention. This intervention involved a targeted geriatric ED assessment including history, physical exam, chart review, communication with caregivers and home care services, and the creation of an intervention and care transition plan to support safe discharge from the ED. We followed both the EQUATOR network’s brief guidelines for reporting a propensity score analysis and the STROBE guideline. Results Out of 21,024 patients visiting the ED over a one-year period, 7,952 were eligible for analysis, with pre-matching differences showing GEM patients were older and more frequent ED users. Propensity score matching resulted in 724 patients with no significant differences in baseline characteristics between groups. Using a Cox regression analysis, we found a non-significant 6% decrease in the risk of ED revisit within 30 days for the GEM group (HR = 0.94, p = .692). Conclusions The GEM nursing intervention targeting better care transition plans personalized to the needs of each patient did not significantly impact thirty-day revisits to the ED. Further work is needed to determine the most effective specific components of such interventions to maximize future positive impact on the care transitions of older patients. Clinician’s capsule What is known about the topic? Geriatric emergency management (GEM) nurses heterogeneously contribute to reducing emergency department revisits among older adults but may improve the quality of care. What did this study ask? How would the implementation of a Geriatric Emergency Management (GEM) nurse intervention in a local emergency department (ED) impact 30-day revisits among older adults? What did this study find? Despite not reaching statistical significance, we observed a 6% reduction in revisit rates. Why does this study matter to clinicians? We should refine and support GEM nurse practices, along with shifting outcome measures from service-level metrics to patient-centered metrics like quality of life and symptom burden.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".