Many patients with chronic cardiometabolic conditions remain below recommended treatment targets despite established clinical guidelines. This session presents a practical care model that combines AI-supported patient identification, automated intake, ambient documentation and team-based care to help primary care practices proactively identify and manage care gaps. Through real-world implementation examples, attendees will learn how digital tools, physician extenders and coordinated referral pathways can support more consistent guideline-based care, improve practice efficiency and expand access for patients with chronic disease.
Learning Objectives
By the end of this session, participants will be able to:
- Describe how AI-driven EMR screening tools can identify patients with unmet cardiometabolic care gaps without requiring full EMR interoperability and prioritize them for outreach and intervention.
- Explain how a structured four-step physician extender model, supported by LLM-based guideline recommendations and ambient AI documentation, increases physician capacity while maintaining care quality and closing chronic disease care gaps.
- Identify practical strategies for integrating eConsult and specialist referral pathways into a primary care chronic disease workflow to support coordinated, guideline-directed therapy for complex patients.
Speaker:
- Abubaker Khalifa, MD, Co-Founder, Inflective AI
Presenter Bios:
Abubaker Khalifa, MD
TBA