ArticleAmerican journal of preventive cardiology2024
Primary care clinician engagement in implementing a machine-learning algorithm for targeted screening of familial hypercholesterolemia.
Article in American journal of preventive cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Implementing Genetic Cascade Testing for Familial Hypercholesterolemia in Primary Care: Patient and General Practitioner Experiences.International journal of behavioral medicine · 2026Article
- Novel strategies to FIND people living with genetic dyslipidemias: The family heart foundation flag, identify, network, and deliver (FIND) familial hypercholesterolemia collaborative learning network.American journal of preventive cardiology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the impact of a multi-pronged educational approach on the knowledge, attitudes, and behaviors regarding Familial Hypercholesterolemia (FH) management at a large academic medical center with the aim of empowering primary care clinicians (PCC) to diagnose and treat FH. Methods: A comprehensive educational program for PCCs on FH management was developed and piloted from July 2022 to March 2024. Components of our intervention included: 1. Implementation of a novel clinical decision support tool in the electronic medical record for FH management, 2. Development and dissemination of an interactive educational website focused on FH and its management, 3. Delivery of virtual instructional sessions to increase awareness of the tool, provide education on its use, and obtain support from institutional leadership, and 4. Direct outreach to a pilot subset of PCCs whose patients had been detected using the validated FIND FH® machine learning algorithm. Participating clinicians were surveyed at baseline before the intervention and after the educational session. Results: 70 PCC consented to participate in the study with a survey completion rate of 79 % ( Conclusion: Empowering PCCs in management of FH serves as a key strategy in addressing this underdiagnosed and undertreated potentially life-threatening condition. A systems-based approach to addressing these aims may include leveraging EMR-based clinical decision support models and cross-disciplinary educational partnerships with medical specialists.
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