The Development and Assessment of a Physician-Specific Antibiotic Usage and Spectrum Feedback Tool
Bibliographic record
Abstract
Measuring antimicrobial usage is a hallmark of antimicrobial stewardship programs. Service –level antimicrobial consumption data is easily obtained but offers limited value to individual clinicians. More specific data via spot audit is resource intensive to collect and may not reflect true practice. Additionally, though clinicians may prescribe antimicrobials with differing frequency, there may also be variability in the choice and spectrum of antimicrobials prescribed. We developed an individualized multidimensional tool using available prescribing and dispensing data to enhance peer comparison and feedback on antimicrobial prescribing. Development was conducted in a 442-bed academic acute care hospital in the division of General Internal Medicine (GIM), in Toronto, Canada. Physician-specific antibiotic consumption data (DDD/100 patient days and DOT/100 patient days) was obtained between February 15th and August 24th, 2016. Summative spectrum of activity was calculated using a metric assigning a value from 0 to 60 to each antimicrobial and obtaining a weighted average of total antimicrobial prescribing by clinician (spectrum scorephysician, modified from Madaras-Kelly et al 2014). Mean antimicrobial consumption was 39.1 ± 13.5 DDD/100 patient-days and 38.5 ± 8.4 DOT/100 patient-days. There was significant variability between the lowest and highest prescribers in both the DDD and DOT (3.3-fold difference DDD/100 patient days, 2.2-fold difference DOT/100 patient days). Mean spectrum score was 23.7 ± 1.8 (approximating Second generation cephalosporins). Variability was also pronounced in this group with the minimum prescriber being 19.5 (equivalent to cefazolin) and maximum being 26.7 (more broad than ceftriaxone). Feedback of this data were given individually to clinicians with other prescribers de-identified. Physicians found the data to be easy to understand and acceptable for further use. Individualized feedback of summative antimicrobial consumption and spectrum provides insight to clinicians. This data can be considered to promote peer comparison and reflection of antimicrobial prescribing. This tool may also be helpful for benchmarking antibiotic usage within and between institutions. 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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".