Using Nurse Practitioners to Optimize Patient Flow in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Using nurse practitioners (NPs) in pediatric emergency departments (PEDs) is commonplace in the United States, yet little is known on the impact of NPs on patient flow measures in these environments. This study quantifies the impact of NPs on 2 common measures of patient flow. METHODS: We conducted a retrospective cohort study using administrative data from an academic tertiary care PED. Mean shift length of stay (LOS) and the daily proportion of patients leaving without being seen (LWBS) by a clinician were compared between shifts with and without NPs on duty, matched for external variables affecting the level of activity in the department. Multivariate regression analyses were also conducted to further adjust for covariates such as the total number of PED care providers, patient acuity distribution, and total volume seen in the ED. RESULTS: Despite a slightly reduced total number of providers present on shifts with NPs on duty, a modest but statistically significant reduction in mean shift LOS (-19.11 minutes [95% confidence interval (CI), -31.01 to -7.22]) and daily proportion of LWBS (-1.11% [95% CI, -1.97% to -0.26%]) was observed for shifts with NPs compared with shifts without NPs on duty. Regression analyses showed that incremental NPs on shift were associated with a decreased LOS (-18.76 minutes [95% CI, -24.51 to -13.02]) as well as a reduced odds of LWBS (odds ratio, 0.56; 95% CI, 0.37-0.87). CONCLUSIONS: Nurse practitioners have a modest impact on patient flow measures in a PED and are a valuable resource to optimize patient flow.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".