Improving handoff efficiency from emergency department to intensive care
Bibliographic record
Abstract
The emergency department to intensive care unit nurse handoff process was found to be inefficient in a Midwest community hospital, resulting in prolonged admission times. The purpose of this project was to determine if implementation of a standardized bedside nurse handoff process would affect admission efficiency. Efficiency of nurse handoff, efficiency of emergency department to intensive care unit admissions, and rates of intensive care unit patient boarding in the emergency department were examined. A task force composed of staff nurses developed a standardized bedside nurse handoff process following guidelines from the literature. This new handoff process incorporated the evidence-based concepts of bedside report, standardization, and electronic medical record. Stakeholder and staff buy-in were obtained, and the process was implemented. Outcomes were evaluated six months prior to- and one-year post-implementation of the standardized bedside handoff process. Analysis of one-year post-implementation data revealed an improvement in average handoff time by 15 minutes, an improvement in average admission time by 17 minutes, and a reduction in intensive care unit patient boarding by 19.5%. By improving efficiency of the nurse handoff process, and therefore the admission process, the findings of this project have the potential to reduce patient boarding and improve the quality of patient care. This quality improvement project also contributes to a gap in the current body of evidence pertaining to interdepartmental nurse handoffs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".