Antimicrobial Resistant Organism Admission Screening Adherence Using a Clinical Information System in a Provincial Healthcare System
Bibliographic record
Abstract
Background: Targeted admission screening of high-risk patients for antimicrobial resistant organisms (AROs) is a key component of infection prevention and control. However, adherence with screening is suboptimal, risking a negligible impact on the prevention of ARO transmission. Clinical decision support tools in clinical information systems (CIS) may improve ARO screening adherence. This study evaluated the adherence of ARO admission screening using a tool in the provincial CIS in Alberta, Canada and the relationship between adherence and hospital ARO rates. Methods: A population-based, sequential cross-sectional study was completed on all admissions to acute care and acute rehabilitation facilities where ARO admission screening occurs on any unit, and where the CIS was implemented in Alberta between January 1, 2020 and March 31, 2024 (n=100). Mental health facilities/units, continuing care, newborns Results: There were 97 (97% of eligible facilities) facilities that implemented the CIS across seven launch periods included. Overall adherence ranged from 43% to 65%. After controlling for bed size and health zone, adherence decreased by the number of months each facility was active on the CIS (aIRR 0.987, 95%CI 0.986-0.987). There was no seasonality in trends. There was a negative relationship between adherence and overall MRSA infection rate (rs = -0.68) and after adjusting for bed size, health zone, and number of months active on the CIS (aIRR 0.99, 95% CI 0.986-0.994). Analysis could not be completed for CPO due to small numbers. Conclusions: While increased ARO admission screening adherence was associated with lower overall MRSA infection rates, the IRR was close to one and may not be clinically significant. With adherence decreasing over time, further work is needed to understand barriers to ARO admission screening and implement strategies to support healthcare providers in completing appropriate surveillance for AROs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".