Evaluating the impact of incorporating clinical practice guidelines for the management of infectious diseases into an electronic application (e-app)
Bibliographic record
Abstract
Abstract Objectives: To improve dissemination and accessibility of guidelines to healthcare providers at our institution, guidance for infectious syndromes was incorporated into an electronic application (e-app). The objective of this study was to compare empiric antimicrobial prescribing before and after implementation of the e-app. Design: This study was a before-and-after trial. Setting: A tertiary-care, public hospital in Halifax, Canada. Participants: This study included pediatric patients admitted to hospital who were empirically prescribed an antibiotic for an infectious syndrome listed in the e-app. Methods: Data were collected from medical records. Prescribing was independently assessed considering patient-specific characteristics using a standardized checklist by 2 members of the research team. Assessments of antimicrobial prescribing were compared, and discrepancies were resolved through discussion. Empiric antimicrobial prescribing before and after implementation of the e-app was compared using interrupted time-series analysis. Results: In total, 237 patients were included in the preimplementation arm and 243 patients were included in the postimplementation arm. Pneumonia (23.8%), appendicitis (19.2%), and sepsis (15.2%) were the most common indications for antimicrobial use. Empiric antimicrobial use was considered optimal in 195 (81.9%) of 238 patients before implementation compared to 226 (93.0%) 243 patients after implementation. An immediate 15.5% improvement (P = .019) in optimal antimicrobial prescribing was observed following the implementation of the e-app. Conclusions: Empiric antimicrobial prescribing for pediatric patients with infectious syndromes improved after implementation of an e-app for dissemination of clinical practice guidelines. The use of e-apps may also be an effective strategy to improve antimicrobial use in other patient populations.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".