Abstract 290: Lessons Learned From Integrated Management Program Advancing Community Treatment Of Atrial Fibrillation (IMPACT-AF): A Pragmatic Clinical Trial Of Computerized Decision Support In Primary Healthcare
Bibliographic record
Abstract
Background: Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) was a pragmatic, cluster randomized trial assessing the clinical relevance and effectiveness of a clinical decision support (CDS) tool in the primary care setting of Nova Scotia, Canada. Key challenges encountered included CDS development and implementation (2013-2018), study recruitment (2014-2016) and data analysis (2018 to present). Methods: Clinical and health informatics researchers developed CDS software designed to help primary care providers (PCP) deliver individualized AF patient care based on national guidelines. Features included prioritized automated alerts signaling material changes in patient clinical or biochemical profiles requiring expedited treatment changes. Challenges documented over the trial duration are presented here as lessons learned. Findings: 1) Resources must be allocated for feasibility testing and software development/updates. CDS development took twice as long as projected. Network access and broadband speeds were key impediments to successful uptake. Although modified after pilot testing with an initial cohort of intervention providers, user feedback at study completion suggested the CDS was not sufficiently user-friendly and did not create efficiencies in the clinical management of AF for patients (i.e., repetitive alerts). 2) Integration across e-platforms is crucial. Intellectual property and other technical issues prohibited integration of the CDS within providers’ existing electronic medical records and desired provincial e-health platforms. PCPs cited double data entry/login as impediments to participation/reasons for withdraw. Challenges with data integration across platforms prevented facile and timely data access, analysis and reporting. 3) Study recruitment is resource intensive. In total, 204 PCPs and 1,204 patients participated, representing 25% of all eligible PCPs and 13% of all persons living with AF in Nova Scotia, respectively. The most effective PCP recruitment strategy was in-office, small group lunch and learns. PCPs with past research experience/those who led patient consent processes were top patient recruiters. The study office played a pivotal role in achieving recruitment targets. Conclusions: A rapid growth in healthcare data is leading to the widespread development of CDS software to analyse it. Our experience found practical issues to address if such applications are to succeed. CDS tools that fully integrate multiple critical co-morbid guideline recommendations across eHealth platforms should be pursued. Feasibility testing to assess the practical utility of any healthcare CDS software prior to its implementation is recommended. Lastly, adequate resources are necessary to support successful recruitment of PCPs for future pragmatic clinical trials.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".