Abstract 189: Implementing Systematic Screening Driven By Quality Improvement Education (QIE) For Atrial Fibrillation During The Covid-19 Pandemic: Insight From Primary Care Setting In The United States
Bibliographic record
Abstract
Background: Asymptomatic patients with atrial fibrillation (AF) pose challenges to diagnosis. Early diagnosis can reduce morbidity and mortality. Systematic screening in primary care may result in early intervention. Objectives: We sought to examine the implementation outcomes of a systematic, team-based quality improvement education (QIE) intervention for AF screening in primary care during the COVID-19 pandemic. Methods: QIE intervention was implemented in academic-based (n=4) and community-based (n=2) practices to address COVID-19 challenges. Surveys administered by site identified existing approaches and provider teams developed screening protocol based on targeted education, deploying a mobile ECG device (Kardiamobile™). Patient charts were reviewed (Dec 2020 - May 2021) to determine eligibility, i.e., patients aged 65–74 (with prior stroke/TIA or two other risk factors) or aged ≥75 (with one other risk factor) without prior AF. Patient EHR data were examined for demographic/clinical data and screening outcome. Provider interviews (n=12) and validation from representative patients (n=2) accounted for sustainability of outcomes. Results: A total of 1,221 patients were evaluated for AF risk, with 408 eligible for screening. Of these, 277 (68%) were female and CHA 2 DS 2 -VASc varied -score=3 (45%); score=4 (24%); score=5+ (31%). Patients (n=7; 2%) who screened positive for AF were referred or started on anticoagulation, like other primary care studies. Figure 1 shows how systematic screening was re-imagined and implemented Satisfaction and engagement increased among providers and patients - attributed, in part, to benefits of team-based planning and targeted education. Conclusion: An AF screening program was adapted to improve patient care despite COVID-19 related challenges. A QIE toolkit was launched to assist primary care practices with implementing streamlined, sustainable, and patient-engaging strategies to reduce stroke.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".