<i>Coming in Hot:</i> A quality improvement approach to improving care of febrile infants
Bibliographic record
Abstract
Background and Objectives: Significant practice variation exists in managing young infants with fever. Quality improvement strategies can aid in risk stratification and standardization of best care practices, along with a reduction of unnecessary interventions. The aim of this initiative was to safely reduce unnecessary admissions, antibiotics, and lumbar punctures (LPs) by 10% in low-risk, febrile infants aged 29 to 90 days presenting to the emergency department (ED) over a 12-month period. Methods: Using the Model for Improvement, a multidisciplinary team developed a multipronged intervention: an updated clinical decision tool (CDT), procalcitonin (PCT) adoption, education, a feedback tool, and best practice advisory (BPA) banner. Outcome measures included the proportion of low-risk infants that were admitted, received antibiotics, and had LPs. Process measures were adherence to the CDT and percentage of PCT ordered. Missed bacterial infections and return visits were balancing measures. The analysis was completed using descriptive statistics and statistical process control methods. Results: Five hundred and sixteen patients less than 90 days of age were included in the study, with 403 patients in the 29- to 90-day old subset of primary interest. In the low-risk group, a reduction in hospital admissions from a mean of 24.1% to 12.0% and a reduction in antibiotics from a mean of 15.2% to 1.3% was achieved. The mean proportion of LPs performed decreased in the intervention period from 7.5% to 1.8%, but special cause variation was not detected. Adherence to the CDT increased from 70.4% to 90.9% and PCT was ordered in 92.3% of cases. The proportion of missed bacterial infections was 0.3% at baseline and 0.5% in the intervention period while return visits were 6.7% at baseline and 5.0% in the intervention period. Conclusions: The implementation of a quality improvement strategy, including an updated evidence-based CDT for young infant fever incorporating PCT, safely reduced unnecessary care in low-risk, febrile infants aged 29 to 90 days in the ED. Purpose: To develop and implement a multipronged improvement strategy including an evidence-based CDT utilizing PCT to maximize value of care delivered to well-appearing, febrile infants presenting to EDs.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".