Physicians spend a disproportionate amount of time hunting for clinically relevant information buried in fragmented EHR interfaces. This overload slows care, increases cognitive burden, and contributes to burnout. Traditional solutions—like manually built condition-specific views—are slow, rigid, and costly to maintain.

This session presents a scalable, AI-powered alternative: an inference engine that transforms trusted clinical guidelines into curated, specialty- and condition-specific patient data views. By combining document distillation, named entity recognition, and semantic parsing, the system identifies relevant clinical elements (e.g., medications, labs, symptoms) and maps them to standard medical ontologies to ensure consistency and interoperability.

What once took weeks of manual expert input now takes hours—and the views continuously improve through structured physician feedback. You’ll learn how this approach supports multidisciplinary teams, reduces cognitive load, and enables workflow-native decision support. Attendees will leave with a blueprint for building scalable, adaptive data curation pipelines that bring signal to the surface in real-world clinical environments.