Скачать или смотреть Cutting Through the Noise: Scalable Patient Data Curation with AI

Svetlana Makarova, AI Principal Product Manager at Mayo Clinic presents “Cutting Through the Noise: Scalable Patient Data Curation with AI.”

Timestamps:
00:00 Introduction — Mayo Clinic's AI Data Curation Vision
01:34 The Clinical Data Overload Problem
02:38 It’s a Curation Problem, Not an Information Problem
05:18 The Scale Challenge — Many-to-Many Needs Across Specialties
06:28 The Five Guiding Questions for Contextual Curation
08:28 Architecture — The Three Pillars of the Curation Pipeline
10:22 Practical Flow — Entity Extraction, Normalization, and Knowledge Graph
12:44 Knowledge Graph — Composing Specialty-Aware Views with Rationale
14:27 Key Innovation — Automated Prompt Tuning and Feedback Loops
16:04 Implementation Steps — From Use Case Selection to Deployment
18:48 The Impact — Speed, Scale, and Personalization

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. 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.


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