Global experience of antimicrobial stewardship integration in electronic medical records: a scoping review
Bibliographic record
Abstract
Antimicrobialresistance impedesthe efficacy of currently available antimicrobial drugs for treatinginfectious diseases.Antimicrobial stewardship (AMS) practices contributetoin haltingantimicrobial resistance by ensuring appropriate antimicrobial prescription. Consequently, many hospitals nowadaysutilize electronic medical records(EMR) as a platform to enhanceAMS practices among their healthcare professionals. The impact of EMR-based AMS modules needsto be ascertained.This scoping review aims to describe the impact of AMSPrograms implemented through EMRon a global scale.A scoping review was conducted using the methodological framework of Arksey and O’Malley, in addition to the Joanna Briggs Institute. A comprehensive search was conducted on PubMed and Google Scholar databases for literature published between 2014 and 2021 using the keywords "antimicrobial", "stewardship", and "electronic medical records". Two reviewers independently screened the titles and abstracts of articles based on the "Population-Concept-Context" framework, using predefined inclusion and exclusion criteria.Atotal of 20studies were included. Most of these studies were conducted in the United States (n=12) and the remaining studies were conducted in various countries,including the United Kingdom, Australia, Indonesia, Egypt, Canada, the Netherlands, Saudi Arabia, and South Korea. The studies demonstrated the global impact of integrating the AMS program in the EMR. The review declaresthat EMR adoption can provide healthcare providerswith efficient processes given the availabilityof clinical data that guide appropriateantimicrobial use. Additionally, implementinginformation technologies for AMS facilitates adherence tonational and international clinical practice guidelines and standardizesantimicrobial usage for optimal patient care.
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 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 | 0.038 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".