Identification of educational and infrastructural barriers to prompt antibiotic delivery in febrile neutropenia: A quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Antibiotic administration within 60 minutes of presentation for medical care may be used as a treatment target for febrile neutropenia (FN); however, anecdotal evidence suggests this target is often missed. Few studies have examined the prevalence or causes of delay. We describe the median time to antibiotic administration at our institution, predictors of delay, and barriers to prompt administration to inform quality improvement strategies. PROCEDURE: A random sample of 50 episodes of FN presenting to the emergency department (ED) between 2008 and 2009 were reviewed. Times between triage, MD assessment, lab results, and antibiotic administration were recorded. Patient and ED variables were examined as possible predictors of delay. In parallel, lean methodology was used to identify system inefficiencies. A trained moderator conducted group interviews with interdisciplinary representatives involved in the emergency care of neutropenic patients to identify process barriers to prompt antibiotics. RESULTS: The median time from triage to antibiotics was 216 minutes (interquartile range [IQR] = 151-274 minutes). The greatest delay occurred following the reporting of lab results (152 minutes, IQR = 84-210 minutes). Only fall season predicted a longer time to antibiotics (P = 0.03). The lean process identified unnecessary areas of delay between departments. CONCLUSIONS: Time to antibiotic administration exceeded 1 hour. The chart review and lean process suggested targets for educational and infrastructural interventions, including an ED pre-printed order sheet, targeted combined subspecialty education between emergency and hematology/oncology staff, and family education. A mixed methodology approach represents a model for improving process efficiency and meeting "best-practice" targets in medicine.
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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 | 0.000 |
| 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) | 0.001 | 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".