1988. Impact of a Novel Pharmacist Practice Model on Antimicrobial Usage and Hospital-acquired Clostridium difficile (HACDI) Rates
Bibliographic record
Abstract
Abstract Background The Antimicrobial Stewardship Program (ASP) was implemented at our 425-bed community hospital in June of 2012. The ASP team, 1 pharmacist, and 1 infectious diseases physician reviewed all intensive care patients on antimicrobials as well as patients on select broad-spectrum antibiotics. In 2016, ASP expansion was undertaken without additional staffing using a novel unit pharmacist model. The effectiveness of in-house antimicrobial stewardship (AMS) training programs for unit pharmacists is not well described. We report the impact of our model on antimicrobial usage and HACDI rates. Methods In 2016, an extensive AMS training and certification program was developed for all unit pharmacists. The program consisted of learning modules, didactic lectures, competency assessments, and individual teaching by the ASP team. In 2017, the practice model was rolled out and the ASP team met with each pharmacist biweekly to review prospective audit and feedback cases with a focus on ceftriaxone and fluoroquinolones. Antimicrobial usage was tracked by defined daily doses (DDD) per 1,000 patient-days, as defined by the World Health Organization. HACDI rates per 1,000 patient-days were defined by the Ontario Ministry of Health and Long Term Care. Results Since the model launched in 2017 until March of 2019, total antimicrobial usage was decreased by 22% (P < 0.001), fluoroquinolones by 21% (P = 0.01), and ceftriaxone by 53% (P < 0.001). HACDI rates decreased from 0.30 to 0.16 cases per 1,000 patient-days (47%, P = 0.12) (Figure 1). Since ASP implementation in 2012, total antimicrobial usage has been reduced by 35% (P < 0.01), fluoroquinolones by 73% (P < 0.001), clindamycin by 70% (P = 0.05) and rates of HACDI by 73% (P < 0.0001) (Figure 2). Pseudomonas susceptibility rates improved (2012 vs. 2017) for meropenem (86% to 93%), ciprofloxacin (73% to 90%), and piperacillin–tazobactam (80 to 92%), but did not reach statistical significance. Conclusion To the best of our knowledge, this is the first description in the literature of an in-house AMS training and certification program for unit pharmacists and its impact on clinical outcomes. This novel approach creates a sustainable and staffing neutral practice model that effectively reduces unnecessary antimicrobial usage and HACDI, resulting in improved patient safety. 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".