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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".