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
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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