Reducing Inappropriate Antibiotic Prescribing for Adults With Acute Bronchitis in an Urgent Care Setting
Bibliographic record
Abstract
Acute bronchitis is a predominantly viral illness and, according to clinical practice guidelines, should not be treated with antibiotics. Despite clear guidelines, acute bronchitis continues to be the most common acute respiratory illness for which antibiotics are incorrectly prescribed. Although the national benchmark for antibiotic prescribing for adults with acute bronchitis is 0%, a preliminary record review before implementing the intervention at the project setting showed that 96% (N = 30) of adults with acute bronchitis in this setting were prescribed an antibiotic. This quality improvement project utilized a single-group, pre-post design. The setting for this project was a large urgent care network with numerous locations in central North Carolina. The purpose was to determine whether nurse practitioners and physician assistants, after participating in a multifaceted provider education session, would reduce inappropriate antibiotic prescribing for healthy adults with acute uncomplicated bronchitis. Twenty providers attended 1 of 4 training sessions offered in October and November 2015. The face-to-face interactive training sessions focused on factors associated with inappropriate antibiotic prescribing, current clinical practice guidelines, and patient communication skills. Retrospective medical record review of 217 pretraining and 335 posttraining encounters for acute bronchitis by 19 eligible participating providers demonstrated a 61.9% reduction in immediate antibiotic prescribing from 91.7% to 29.8%. Delayed prescribing, which accounted for a small percentage of the total prescriptions given, had a small but significant increase of 9.3% after training. Overall, this multifaceted, interactive provider training resulted in significant reductions in inappropriate prescriptions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".