Reducing unnecessary diagnostic phlebotomy in intensive care: a prospective quality improvement intervention
Bibliographic record
Abstract
BACKGROUND: Critically ill patients receive frequent routine and recurring blood tests, some of which are unnecessary. AIM: To reduce unnecessary routine phlebotomy in a 30-bed tertiary medical-surgical intensive care unit (ICU) in Toronto, Ontario. METHODS: This prospective quality improvement study included a 7-month preintervention baseline, 5-month intervention and 11-month postintervention period. Change strategies included education, ICU rounds checklists, electronic order set modifications, an electronic test add-on tool and audit and feedback. The primary outcome was mean volume of blood collected per patient-day. Secondary outcomes included the number blood tubes used and red cell transfusions. Balancing measures included the timing and types of blood tests, ICU length of stay and mortality. Outcomes were evaluated using process control charts and segmented regression. RESULTS: Patient demographics did not differ between time periods; total number of patients: 2096, median age: 61 years, 60% male. Mean phlebotomy volume±SD decreased from 41.1±4.0 to 34.1±4.7 mL/patient-day. Special cause variation was met at 13 weeks. Segmental regression demonstrated an immediate postintervention decrease of 6.6 mL/patient-day (95% CI 1.8 to 11.4 p=0.009), which was sustained. Blood tube consumption decreased by 1.4 tubes/patient-day (95% CI 0.4 to 2.4, p=0.005) amounting to 13 276 tubes (95% CI 4602 to 22 127 tubes) saved over 11 months. Red blood cell transfusions decreased from 10.5±5.2 to 8.3±4.4 transfusions/100 patient-days (incident rate ratio 0.56, 95% CI 0.35 to 0.88, p=0.01). There was no impact on length of stay (2 days, IQR 1-5) and mortality (18.1%±2.0%). CONCLUSION: Iterative improvement interventions targeting clinician test ordering behaviour can reduce ICU phlebotomy and may impact red cell transfusions. Frequent stakeholder consultation, incorporating stewardship into daily workflow, and audit and feedback are effective strategies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".