Enhancing sepsis care through improved first-dose antibiotic turnaround time for hospital-based rehabilitation patients
Bibliographic record
Abstract
Background: Timely administration of the first-dose intravenous antibiotic is a key quality measure in hospital accreditation and sepsis care. This study aimed to identify barriers to first-dose intravenous antibiotic administration in an inpatient rehabilitation unit at a leading academic medical center and implement targeted interventions to improve the 60-minute turnaround rate. Methods: In 2024, quality outcomes specialists used the Kaizen Rapid Cycle methodology to conduct a weekly review of all STAT intravenous antibiotic orders on the rehabilitation unit. Orders exceeding the 60-minute turnaround time were analyzed through root cause analysis (RCA) to identify delays. Findings from the RCA guided the development of interventions to improve compliance. Results: At the start of 2024, the 60-minute antibiotic turnaround rate was 33.3%. After implementing key interventions, the turnaround rate increased to 68.4% by the end of March and remained stable throughout 2024. These interventions included stocking frequently used antibiotics in Pyxis, enhancing communication and notification for STAT antibiotic orders, maintaining ongoing surveillance, and providing individualized feedback to the frontline nurses. Conclusions: Identifying and addressing barriers to timely antibiotic administration led to significant improvements in turnaround time. Enhancing communication, optimizing medication availability, and sustaining performance monitoring proved effective in improving compliance with sepsis care standards.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".