Computerized clinical decision support to improve stroke prevention therapy in primary care management of atrial fibrillation: a cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Despite guidelines supporting antithrombotic therapy use in atrial fibrillation (AF), under-prescribing persists. We assessed whether computerized clinical decision support (CDS) would enable guideline-based antithrombotic therapy for AF patients in primary care. METHODS: This cluster randomized trial of CDS versus usual care (UC) recruited participants from primary care practices across Nova Scotia, following them for 12 months. The CDS tool calculated bleeding and stroke risk scores and provided recommendations for using oral anticoagulants (OAC) per Canadian guidelines. RESULTS: From June 14, 2014 to December 15, 2016, 203 primary care providers (99 UC, 104 CDS) with access to high-speed Internet were recruited, enrolling 1,145 eligible patients (543 UC, 590 CDS) assigned to the same treatment arm as their provider. Patient mean age was 72.3 years; most were male (350, 64.5% UC, 351, 59.5% CDS) and from a rural area (298, 54.9% UC, 315, 53.4% CDS). At baseline, a higher than anticipated proportion of patients were receiving guideline-based OAC therapy (373, 68.7% UC, 442, 74.9% CDS; relative risk [RR] 0.97 (95% confidence interval [CI], 0.87-1.07; P = .511)). At 12 months, prescription data were available for 538 usual care and 570 CDS patients, and significantly more CDS patients were managed according to guidelines (415, 77.1% UC, 479, 84.0% CDS; RR 1.08 (95% CI, 1.01-1.15; P = .024)). CONCLUSION: Notwithstanding high baseline rates, primary care provider access to the CDS over 12 months further optimized the prescribing of OAC therapy per national guidelines to AF patients potentially eligible to receive it. This suggests that CDS can be effective in improving clinical process of care. TRIAL REGISTRATION: Clinical Trials NCT01927367. https://clinicaltrials.gov/ct2/show/NCT01927367?term=NCT01927367&draw=2&rank=1.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".