Improving Time to Antibiotics for Pediatric Oncology Patients With Fever and Suspected Neutropenia by Applying Lean Principles
Bibliographic record
Abstract
BACKGROUND: Fever in the setting of neutropenia is a potentially life-threatening complication of cancer treatment. A time of less than 60 minutes from presentation to antibiotic administration is therefore recommended. OBJECTIVE: To use Lean Six Sigma methodology, a quality improvement initiative, to improve time to antibiotics (TTA) for children with chemotherapy-induced febrile neutropenia presenting to the emergency department. METHODS: Lean Six Sigma is a quality improvement method that engages all impacted stakeholders and focuses on streamlining the process by removing process wastes. Stakeholders identified multiple process wastes in an in-depth study of 49 fever episodes in patients attending a tertiary care pediatric hospital, including patients waiting to be registered, waiting for laboratory technicians, delay in accessing central venous access device, waiting for absolute neutrophil count, and delayed antibiotics orders. We implemented multiple solutions: engaging patients in the process through predischarge tours of the emergency department, home application of topical anesthetic, nurse-initiated pathway, early access of central venous access device for all blood work, and planned antibiotic administration no later than 45 minutes after triage. We prospectively determined the impact of these interventions on TTA. RESULTS: The TTA significantly improved to a median of 59 minutes (interquartile range, 38.5-77.5 minutes) compared with the baseline of 99 minutes (interquartile range, 72.0-132.0 minutes; P < 0.0001). CONCLUSIONS: Lean methodology effectively identifies barriers and provides solutions to remove barriers and improve administration of antibiotics in febrile oncology patients. These can be widely applied, including in smaller institutions with minimal increased utilization of resources.
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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 | 0.000 | 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".