A QUALITY IMPROVEMENT INITIATIVE TO DECREASE THE RATE OF SOLITARY SETS OF BLOOD CULTURES IN THE EMERGENCY DEPARTMENT
Bibliographic record
Abstract
Background Blood cultures (BCs) are commonly performed in the emergency department (ED). Proper collection is paramount for accurate results, which includes obtaining at least two sets of BCs. In our EDs, an unacceptably high proportion of patients had solitary sets of BCs sent for analysis. Objectives To reduce the rate of solitary sets of BCs being sent to the lab on patients discharged from the ED. Methods Using PDSA cycles, we evaluated two sequential interventions. The first intervention included didactic educational sessions and reminders in ED staff huddles. The second intervention added a forcing function (FF) at the point of computer order entry that automatically printed sticker labels for two sets of BCs, instead of the previous default of one. Providers could still send single sets by discarding unused labels. The bi-weekly solitary BC rates were analyzed using statistical process control charts and segmented regression analyses. Results The baseline rate of solitary BCs was 41.1%. The education intervention reduced this rate to 30.3%, and the FF reduced it further to 11.6% (total absolute reduction of 29.5% from baseline). With segmental regression analyses, education alone did not produce a statistically significant change when factoring time-related trends (P=0.071). However, the FF produced a statistically significant improvement (P<0.0005), which was sustained for 6 months. Conclusions The combination of an education intervention and a computerized FF was more effective than education alone in reducing solitary BCs in our ED. FFs can be a powerful tool in modifying behaviours and processes in the clinical setting. Table 1 Visit data during the duration of the study period (November 2014 to July 2016) Metric Site 1 Site 2 Combined Total ED Visits During Study Period 83 747 111 621 195 368 Total ED Ambulatory Visits During Study Period 67 040 97 538 164 578 Number of Visits That Had ANY Blood Cultures Sent 3 184 1 839 5 023 Blood Cultures (any number of sets) ordered per 100 ambulatory patients 4.74 1.88 3.05 Table 2 Rates of solitary blood cultures sent for patients discharged from the ED Time Period Site 1 Rate Site 2 Rate Combined Rate Baseline (November 2014-March 2015) 39.3% 43.4% 41.1% Post Education Intervention (March 2015 – January 2016) 32.5% 26.3% 30.3% Post EPR Intervention (January 2016 – July 2016) 12.5% 10.2% 11.6% Table 3 Segmented regression analysis Variable Coefficient P-value Time-Effect (duration of entire period) −0.0074 0.132 </jats
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".