P-1816. Stepwise Implementation of an Antimicrobial Stewardship Prospective Audit and Feedback Intervention at a Large Academic Center in Canada
Bibliographic record
Abstract
Abstract Background Effective implementation strategies for establishing antimicrobial stewardship (AMS) program inpatient interventions are not well described. We describe a methodology for prospective audit and feedback (PAF) implementation at a large Canadian hospital and report its outcomes. Methods A PAF intervention targeting restricted antibiotics (carbapenems, daptomycin, linezolid, and tigecycline) was launched in March 2018. Stepwise implementation by inpatient programs was prioritized by collaboration readiness as immediate site-wide implementation was deemed unfeasible. New prescriptions were assessed by the antimicrobial stewardship team. Prescription, prescriber, intervention type, and acceptance data were collected through March 2024. Results Over a 2-year period, site wide stepwise implementation occurred in 16 discrete phases. Over the study period, 5185 prescriptions were evaluated; 3789 (73%) were empiric, and 1396 (27%) culture directed from which 3604 (70%) were optimally prescribed (evaluated by agent, regimen, and duration). Of empiric prescriptions, 636 (17%) were not guideline-concordant by indication, 1102 (29%) did not follow guidelines but was reasonable by expert opinion, and 2051 (54%) were guideline-concordant. ASP recommendations included: 3604 no change (70%), 864 change agent (17%), 535 change regimen (10%), 172 discontinue antibiotic (3%), and 10 other (0.2%). The recommendation acceptance rate was 91% (1581 cases evaluable). Infectious diseases (ID) were involved in 1466 (28%) prescriptions. With ID involvement, higher guideline-concordant empiric prescribing (64% vs 51%, p< 0.001) and regimen appropriateness (85% vs 65%, p< 0.001) was observed. ASP suggested no changes more often with ID involvement (84% versus 62%, p< 0.001). Conclusion Stepwise implementation allowed evolution and implementation of ASP processes and workflow. 30% of prescriptions audited resulted in intervention, suggesting a possible role for further education, guidelines, or restriction. ID involvement was associated with more optimal prescribing. A stepwise implementation of an antimicrobial stewardship PAF intervention is a viable strategy. Disclosures All Authors: No reported disclosures
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".