1046. Evaluating the outcomes of embedding Antimicrobial Stewardship order sets in the General Medicine Admission Electronic Order Set: A Retrospective Study
Bibliographic record
Abstract
Abstract Background The use of facility-specific guidelines and clinical decision-making tools are recommended by a number of organizations to improve the appropriateness of empiric antimicrobial prescribing; however, how to increase usage is not clear. We evaluated the impact of embedding antimicrobial stewardship (AS) electronic order sets (EOS) into the general medicine admission EOS in the context of an established AS program. Methods The standalone EOS for community-acquired pneumonia (CAP), urinary tract infection (UTI) and cellulitis were reviewed and simplified to only include the antibiotic section prior to embedding. The intervention was introduced on March 30, 2017 with pre-intervention period defined as January 1, 2016 to March 29, 2017 and post-intervention period as of March 30, 2017 to June 30, 2018. The primary outcome was the change in usage of embedded AS EOS compared with the corresponding standalone EOS using counts. In addition, other standalone AS EOS (i.e., Clostridioides difficile infection (CDI), etc) were used as a control. The secondary outcomes were the change in antibiotic usage de-emphasized in embedded EOS (i.e., ceftriaxone, ciprofloxacin, clindamycin, moxifloxacin) and predicted prescribing shifts to antibiotics in the embedded EOS (i.e., amoxicillin-clavulanate, azithromycin and sulfamethoxazole-trimethoprim) using Days of Therapy (DOT)/1000 patient-days (PD). Paired t-test was used to compare antibiotic usage pre- and post-intervention. Results The usage of standalone EOS remained similar pre- and post-intervention except for a 16-fold increased usage of CDI EOS. There were large increases in uptake of the embedded EOS compared with the standalone EOS: 11-fold () increase for CAP, 47-fold () increase for UTI and 24-fold () increase for cellulitis. In addition, there was a statistically significant decrease in ciprofloxacin (mean 16.6 DOT/1000-PD vs. 13.6 DOT/1000-PD, P = 0.026) and moxifloxacin usage (mean 9.3 DOT/1000-PD vs. 5.2 DOT/1000-PD) during the study time period. Conclusion Our study showed that simplifying AS EOS and embedding these into a more commonly used EOS is associated with a significant increase in EOS usage and uptake of AS recommended empiric antibiotics with a decrease in fluoroquinolone usage. 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".