Challenges of Antibiotic Stewardship on the Internal Medicine Ward
Bibliographic record
Abstract
Audit and feedback programs are considered to be one of the most effective antimicrobial stewardship (ASP) strategies to improve antibiotic prescribing; however, resource requirements are a limiting factor. In a controlled, quasi-experimental study we evaluated the impact of once weekly ASP rounds conducted with one Medicine teaching team, while two Medicine teams served as controls, at a tertiary care center from November 2014 until November 2015. We assessed process measures and changes in antibiotic utilization (reported as monthly defined daily doses per 1000 patient days from November 2013 to November 2015 using statistical process control charts). In total, 249 patients (40% of 627 patients on the team) were on anti-infectives on the day of the rounds and had been discussed, of which 18% (48/249) were already followed by infectious diseases. A total of 79 interventions were made. Reduced duration of therapy comprised over half of the recommendations (39/76, 51%). Discontinuing antibiotics on the same day was the second most common intervention (20/76, 26%). All stewardship suggestions were accepted by the Medicine team. However, no significant changes in antibiotic utilization were observed with a similar reduction in both groups for piperacillin-tazobactam and meropenem while at the same time, an increased use of first and third-generation cephalosporins was observed. While the process measures demonstrated a change in the treatment plan in 1 out of 3 patients reviewed, this did not translate into a significant change in antibiotic utilization as compared with the control groups. This may be related to the comparably small proportion of patients reviewed by ASP given that rounds occurred only once a week, and were cancelled 28 times within a one year period due to limited Medicine clinician availability. There also could have been cross-contamination between the two study arms with faculty and trainees who received the intervention while on the intervention team continuing their learned practice while on a control team. Process measures are an important means to measure the impact of ASPs, as antibiotic utilization is not a sensitive metric and may not reliably reflect improvement in antibiotic management at the individual patient level. 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".