Standardized Reporting System Use During Handoffs Reduces Patient Length of Stay in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) shift handoffs are potential sources of delay in care. We aimed to determine the impact that using standardized reporting tool and process may have on throughput metrics for patients undergoing a transition of care at shift change. METHODS: We performed a prospective, pre- and post-intervention quality improvement study from September 1 to November 30, 2015. A handoff procedure intervention, including a mandatory workshop and personnel training on a standard reporting system template, was implemented. The primary endpoint was patient length of stay (LOS). A comparative analysis of differences between patient LOS and various handoff communication methods were assessed pre- and post-intervention. Communication methods were entered a multivariable logistic regression model independently as risk factors for patient LOS. RESULTS: The final analysis included 1,006 patients, with 327 comprising the pre-intervention and 679 comprising the post-intervention populations. Bedside rounding occurred 45% of the time without a standard reporting during pre-intervention and increased to 85% of the time with the use of a standard reporting system in the post-intervention period (P < 0.001). Provider time (provider-initiated care to patient care completed) in the pre-intervention period averaged 297 min, but decreased to 265 min in the post-intervention period (P < 0.001). After adjusting for other communication methods, the use of a standard reporting system during handoff was associated with shortened ED LOS (OR = 0.60, 95% CI 0.40 - 0.90, P < 0.05). CONCLUSIONS: Standard reporting system use during emergency physician handoffs at shift change improves ED throughput efficiency and is associated with shorter ED LOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".