818. Understanding Current Practices of an Electronic Antimicrobial Resistant Organism (ARO) Screening Tool Used in Healthcare Facilities in Alberta, Canada
Bibliographic record
Abstract
Abstract Background Increasing antibiotic resistance is a global problem. By detecting individuals colonized with antimicrobial resistant organisms (AROs) through hospital admission screening, enhanced precautions can be implemented to interrupt transmission during their hospitalization. The objective is to understand current practices of ARO screening in Alberta hospitals by evaluating completeness of an electronic ARO screening tool. Methods A retrospective cohort study using electronic medical record (EMR) data from Connect Care (Epic Systems Corp.) at one adult and one pediatric tertiary hospital in Edmonton, Alberta between January 1, and December 31, 2020 was conducted. All hospital admissions were included. Outcomes included proportion of admissions with a completed ARO tool and a comparison of the best practice actions triggered (e.g. ARO screening swab and/or isolate) between those with and without a completed ARO tool. Test of proportions was used, with a two-sided α of 0.05 and was conducted using STATA/SE 16.1. Results During the study period, there were 32,600 total admissions, of which 16,342 (50.1%) had the ARO tool completed. The overall compliance was greater in the adult hospital (53.9%) compared to the pediatric hospital (36.3%) (< 0.001). For both hospitals, the compliance rate in December 2020 was 30.0% lower than in January 2020, with a continuous decline during the 12 months. An isolation order or screening swab was ordered in 40.6% of admissions without an ARO tool completed compared to 56.5% of admissions with the ARO tool completed. Based on responses in the completed ARO tool, 81.2% of patient admissions had a microbiologic swab ordered when indicated, of which only 4.7% were positive. However, only 35.9% of patient admissions had additional precautions ordered when indicated. Conclusion ARO screening is a required organizational practice and although standardized across all Alberta hospitals and embedded within an EMR, compliance was poor. Further work is needed to understand point-of-care risk assessment processes to inform future interventions aimed at early identification and decreased transmission of ARO in hospitals. Disclosures All Authors: No reported disclosures
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".