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 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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".