Improving Timely Sepsis Care through Staff Education within the Emergency Department
Bibliographic record
Abstract
Problem: A sepsis protocol and bundle has been implemented in an urban Emergency Department to help screen patients and treat sepsis efficiently and effectively. The benchmarks from the bundle are not being met consistently every month and are below the targeted 90%. Context: A microsystem assessment and a gap survey sent out to nurses, helped determine that there is room to improve nurses’ knowledge and confidence about sepsis and the sepsis bundle workflow. Sepsis is one of the most expensive and burdensome conditions in U.S. hospitals. Literature supports staff education to improve sepsis bundle compliance. Intervention: A video was created and was sent out via the nurse manager to all the nurses in the unit. The video is a slide deck that was recorded with a voice over, including information about signs and symptoms of sepsis and the sepsis protocol. In addition, information posted in the staff break room was updated about sepsis, the protocol, and current compliance. Measures: Data from the first quarter of 2023 and the last quarter of 2022 was obtained for first vital to lactic acid results within 60 minutes, lactic acid results to antibiotic administration within 60 minutes, and antibiotic order to administration within 35 minutes. Results: The Post-Intervention results have not been obtained for this project due to time constraint. The recommendation is to obtain and analyze the 2023 second quarter data, and then compare it to the 2023 first quarter data and the 2022 last quarter data to determine if the benchmarks have been met consistently by 90% for each month in the 2023 second quarter. Conclusions: Providing nurses with the knowledge to help identify sepsis rapidly, as well as becoming more familiar with the sepsis protocol, helps them confidently enact the bundle. Second quarter data will evaluate if this project has improved the workflow and has saved time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".