Safety assessment of a redirection program using an electronic application for low-acuity patients visiting an emergency department
Bibliographic record
Abstract
BACKGROUND: Emergency departments (EDs) are operating at or above capacity, which has negative consequences on patients in terms of quality of care and morbi-mortality. Redirection strategies for low-acuity ED patients to primary care practices are usually based on subjective eligibility criteria that sometimes necessitate formal medical assessment. Literature investigating the effect of those interventions is equivocal. The aim of the present study was to assess the safety of a redirection process using an electronic clinical support system used by the triage nurse without physician assessment. METHODS: A single cohort observational study was performed in the ED of a level 1 academic trauma center. All low-acuity patients redirected to nearby clinics through a clinical decision support system (February-August 2017) were included. This system uses different sets of medical prerequisites to identify patients eligible to redirection. Data on safety and patient experience were collected through phone questionnaires on day 2 and 10 after ED visit. The primary endpoint was the rate of redirected patients returning to any ED for an unexpected visit within 48 h. Secondary endpoints were the incidence of 7-day return visit and satisfaction rates. RESULTS: A total of 980 redirected low-acuity patients were included over the period: 18 patients (2.8%) returned unexpectedly to an ED within 48 h and 31 patients (4.8%) within 7 days. No hospital admission or death were reported within 7 days following the first ED visit. Among redirected patients, 81% were satisfied with care provided by the clinic staff. CONCLUSION: The implementation of a specific electronic-guided decision support redirection protocol appeared to provide safe deferral to nearby clinics for redirected low-acuity patients. EDs are pivotal elements of the healthcare system pathway and redirection process could represent an interesting tool to improve the care to low-acuity patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".