A Quality Improvement Initiative to Decrease the Rate of Solitary Blood Cultures in the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Best practice guidelines recommend that at least two sets of blood cultures (BCs) be sent when blood cultures are required. However, high rates of solitary BCs are still common in the emergency department (ED). The aim of this study was to evaluate the efficacy of different quality improvement initiatives aimed at reducing the rate of solitary blood cultures being sent to the laboratory on patients ultimately discharged from our ED. METHODS: This was a multicenter, multiphase, prospective study evaluating a comprehensive education-based intervention and a second intervention that combined a computerized forcing function (FF) along with a brief education-based intervention. The results were analyzed using segmented regression analysis, as well as statistical process control charts. RESULTS: The baseline rate of solitary sets of BCs was 41.1%. The education intervention reduced this rate to 30.3%. The introduction of a FF with a brief educational intervention further reduced the rate to 11.6%. This represents an absolute reduction of 29.5% from baseline (relative reduction of 71.8%). According to segmental regression analyses, the education intervention alone did not produce a statistically significant change when factoring possible background time-related trends (p = 0.071). However, the FF produced a statistically significant improvement (p < 0.0005), which was maintained for 6 months. CONCLUSION: The combination of a brief education-based intervention and a computerized FF was more effective than education alone in reducing solitary BC collection in our ED in this time series study. FFs can be a powerful tool in modifying behaviors and processes in the clinical setting.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".