The use of an IV to PO clinical intervention form to improve antibiotic administration in a community based hospital
Bibliographic record
Abstract
Antimicrobials are among the most commonly prescribed medications in acute care settings, with 50% of antimicrobial use deemed inappropriate. Antimicrobial stewardship programs (ASP) aim to optimize antibiotic use in order to improve patient clinical outcomes while minimizing unwanted effects of therapy including Clostridium difficile infection (CDI) and the emergence of resistant organisms. Antimicrobial Stewardship involves a coordinated set of interventions that ensure patients who require antimicrobial therapy receive the most appropriate agent at the correct dose, by the correct route for the correct duration. This project focused on the appropriate conversion from intravenous (IV) to oral (PO) antibiotics. The purpose of the project was to determine whether antibiotic prescribing patterns improved following the implementation of an IV to PO conversion clinical intervention form. A collaborative approach was used by an interprofessional team to review medical records and design a clinical intervention form that adheres to evidence-based guidelines to promote appropriate conversion for patients on intravenous antibiotics. Education efforts were made to inform health system staff about the goal of the intervention and to gather feedback for the improvement of the clinical intervention form. A five-week pilot intervention period trial was carried out with a specific focus on the fluoroquinolone class of antibiotics, specifically ciprofloxacin, moxifloxacin, and levofloxacin. Data from the intervention phase, including overall antibiotic use, ratio of intravenous:oral antibiotic time and compliance with the clinical intervention form were assessed. The ratio of intravenous to oral fluoroquinolone use changed from 39.4% IV: 60.6% PO before the intervention, to 34.7% IV: 65.3% PO during the intervention, indicating an overall increase in the usage of oral fluoroquinolones during the intervention phase. In patients eligible to receive the clinical intervention form, physician compliance with the form was 50%. IV duration decreased by 42% in patients with physician compliance. Further refinement of this form and the process for implementation will further enhance the conversion of intravenous to oral therapy. Based on these findings and the lessons learned, this process will be considered for further refinements, spread, and sustainability.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".