From Little Things Big Things Grow: The Development of an Auditing Program to Assess the Quality of Antimicrobial Prescribing
Bibliographic record
Abstract
Background: An important aspect of antimicrobial stewardship is the qualitative assessment of antimicrobial prescribing. Owing to lack of standardized tools and resources required to design, conduct and analyze qualitative audits, these assessments are rarely performed. Objective: We designed an audit tool that was appropriate for all Australian hospital types, suited to local user requirements and including an assessment of the appropriateness of antimicrobial prescribing. Methods: In 2011, a pilot survey was conducted in 32 Australian hospitals to assess the usability and generalizability of a qualitative audit tool. The tool was revised to reflect the respondents’ feedback. A second study was performed in 2012 in 85 hospitals. In 2013, following further feedback and refinement, an online auditing tool, the Hospital National Antimicrobial Prescribing Survey (NAPS), was developed. Early audits demonstrated that surgical prophylaxis had the highest rates of inappropriate prescribing. In 2016, the Surgical NAPS was developed to further investigate reasons for this, and the NAPS program was further expanded to audit antimicrobial prescribing practices in Australian aged-care homes (ie, the Aged Care NAPS). Results: Between January 1, 2013, and November 12, 2019, 523 Australian public and private hospitals (53.8%) utilized the Hospital NAPS; 215 (22.1%) have utilized the Surgical NAPS; and 774 of Australian aged-care homes (29.0%) have utilized the Aged Care NAPS. National reporting has identified key target areas for quality improvement initiatives at both local and national levels. The following initiatives have been outlined in 14 public reports: improved documentation; prolonged antimicrobial prophylaxis; compliance with prescribing guidelines; appropriateness of prescribing; access to evidence-based guidelines; and improved microbiology sampling. Conclusions: By utilizing the Plan-Do-Study-Act cycle for healthcare improvement and by involving end users in the design and evaluation, we have created a practical and relevant auditing program to assess both quantitative and qualitative aspects of antimicrobial prescribing in a wide range of settings. This voluntary program is now endorsed by the National Strategy for Antimicrobial Resistance Surveillance, partners with the Antimicrobial Use and Resistance in Australian Surveillance System, and is utilized by facilities to meet mandatory national accreditation standard requirements. With the success of the NAPS program in Australia, it has now been implemented in New Zealand, Canada, Malaysia, Fiji, and Bhutan, with plans for other countries to implement the program soon. Current research is being conducted to expand the program to include audits for family physicians, veterinarians, and remote indigenous communities, and for antifungal use. Disclosures: None Funding: None
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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 | 0.085 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".