Implementing wait-time reductions under Ontario government benchmarks (Pay-for-Results): a Cluster Randomized Trial of the Effect of a Physician-Nurse Supplementary Triage Assistance team (MDRNSTAT) on emergency department patient wait times
Bibliographic record
Abstract
BACKGROUND: Internationally, emergency departments are struggling with crowding and its associated morbidity, mortality, and decreased patient and health-care worker satisfaction. The objective was to evaluate the addition of a MDRNSTAT (Physician (MD)-Nurse (RN) Supplementary Team At Triage) on emergency department patient flow and quality of care. METHODS: Pragmatic cluster randomized trial. From 131 weekday shifts (8:00-14:30) during a 26-week period, we randomized 65 days (3173 visits) to the intervention cluster with a MDRNSTAT presence, and 66 days (3163 visits) to the nurse-only triage control cluster. The primary outcome was emergency department length-of-stay (EDLOS) for patients managed and discharged only by the emergency department. Secondary outcomes included EDLOS for patients initially seen by the emergency department, and subsequently consulted and admitted, patients reaching government-mandated thresholds, time to initial physician assessment, left-without being seen rate, time to investigation, and measurement of harm. RESULTS: The intervention's median EDLOS for discharged, non-consulted, high acuity patients was 4:05 [95th% CI: 3:58 to 4:15] versus 4:29 [95th% CI: 4:19-4:38] during comparator shifts. The intervention's median EDLOS for discharged, non-consulted, low acuity patients was 1:55 [95th% CI: 1:48 to 2:05] versus 2:08 [95th% CI: 2:02-2:14]. The intervention's median physician initial assessment time was 0:55 [95th% CI: 0:53 to 0:58] versus 1:21 [95th% CI: 1:18 to 1:25]. The intervention's left-without-being-seen rate was 1.5% versus 2.2% for the control (p = 0.06). The MDRNSTAT subgroup analysis resulted in significant decreases in median EDLOS for discharged, non-consulted high (4:01 [95th% CI: 3:43-4:16]) and low acuity patients (1:10 95th% CI: 0:58-1:19]), as well as physician initial assessment time (0:25 [95th% CI: 0:23-0:26]). No patients returned to the emergency department after being discharged by the MDRNSTAT at triage. CONCLUSIONS: The intervention reduced delays and left-without-being-seen rate without increased return visits or jeopardizing urgent care of severely ill patients. TRIAL REGISTRATION NUMBER: NCT00991471 ClinicalTrials.gov.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".