Savant Bio provides an AI‑powered platform that converts raw clinical records into registry‑grade, actionable data for research, quality improvement, and patient care. The system automatically abstracts real‑world data, enriches it for population health analytics, and generates guideline‑aligned insights that have been shown to outperform human experts in longitudinal record analysis. By delivering precise, validated abstractions—such as etiology determinations from imaging reports—it accelerates study timelines and supports evidence‑based decision making.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations struggle to convert unstructured clinical records into high-quality, research‑ready datasets, leading to slow study timelines, limited quality‑improvement insights, and suboptimal care decisions.
Solution
Savant Bio offers an AI‑driven platform that automatically extracts and normalizes information from diverse clinical documents—such as rheumatology notes and CT chest reports—into registry‑grade datasets. The system continuously analyzes patient records over time, applying guideline‑aligned abstraction rules that can be customized for specific therapeutic areas. By outperforming human abstractors, the platform delivers expert‑level precision while reducing manual effort and error rates. Integrated into existing workflows, it provides validated, actionable data that supports population health analytics, research studies, and quality‑improvement initiatives. Users can view extracted insights through an intuitive interface and export them for downstream analytics or reporting.
Target Audience
Primary customers are health systems, research institutions, and pharmaceutical companies that need high‑quality real‑world data for clinical studies, quality metrics, and care optimization.
Features
- AI-powered natural language processing that parses unstructured notes, imaging reports, and other clinical documents
- Continuous temporal analysis of patient records to capture longitudinal changes and outcomes
- Customizable, guideline‑aligned abstraction templates for specialties such as rheumatology and pulmonology
- Validation layer that benchmarks AI output against expert abstractors to ensure registry‑grade accuracy
- Seamless integration via APIs and workflow connectors to embed extraction into existing EHR and data pipelines
- Export of structured, standardized datasets for population health analytics, research registries, and quality‑improvement reporting