Impact of Positive Feedback on Antimicrobial Stewardship in a Pediatric Intensive Care Unit: A Quality Improvement Project
Bibliographic record
Abstract
We hypothesized that antimicrobial stewardship (AMS) could be enhanced through positive feedback for the behaviors of healthcare professionals. This project aimed to reduce antimicrobial consumption in a Pediatric Intensive Care Unit (PICU) by >5%, with secondary aims to reduce broad-spectrum antimicrobial consumption, and processes related to AMS. Learning from Excellence is a positive feedback initiative conceptualized at our institution. METHODS: This project took place over 12 months (April 2017-March 2018) in a 31-bedded PICU. We identified and measured processes about AMS daily. Healthcare professionals, achieving success in these processes, received positive feedback via Learning from Excellence, during a 6 months intervention period. Selected reports were followed with appreciative inquiry interviews to reinforce positive feedback. We calculated antimicrobial consumption data from existing databases (antimicrobial doses dispensed divided by PICU bed-days). Health Care-Associated Infection (HCAI) rates were included as a balancing measure. RESULTS: Antimicrobial consumption was 6.5% lower during the intervention period compared with the matching period from the previous year. We reduced broad-spectrum antimicrobial (meropenem) consumption by 17.6%. Improvements in processes were mixed: a daily review of antimicrobials and documentation of antimicrobial prescription and administration significantly improved. Other processes failed to improve. HCAI rates did not change. CONCLUSIONS: Positive feedback can be used as a QI intervention to improve processes around AMS. This intervention may contribute to a reduction in antimicrobial consumption. Not all processes are impacted equally, and there may be a "dose-response" effect. Further evaluation would benefit from a trial study design in other settings.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".