Patient memory you can trust—every claim cited
CrossCures turns fragmented EHR artifacts into a longitudinal timeline and chart brief with source citations and uncertainty flags when data is missing or ambiguous.
Demo uses synthetic/de-identified data.
Deployed with physicians at Mass General Hospital • 12-month roadmap collaboration with Mass General Brigham • Quarterly advisory board with Mayo Clinic • Pilot interest from Beth Israel Deaconess Medical Center and Boston Children's Hospital
The Challenge
Clinicians spend hours piecing together fragmented patient histories from disparate EHR systems.
Fragmented Data
Patient information scattered across multiple systems, notes, and formats makes comprehensive review time-consuming and error-prone.
Missing Context
Critical details buried in unstructured notes or lost between care transitions lead to incomplete clinical pictures.
Trust Deficit
Black-box AI summaries without source citations create liability concerns and require extensive verification.
How It Works
Three steps to transform fragmented records into trusted clinical intelligence.
Ingest
Connect to EHR systems via FHIR or native integrations. CrossCures ingests clinical notes, lab results, medications, and care plans.
Structure with Citations
Foundation models extract clinical entities and relationships, preserving source references. Every claim links back to its originating document.
Deliver Brief
Generate longitudinal timelines and narrative summaries with citation markers. Uncertainty flags highlight missing or ambiguous data.
What Sets Us Apart
Provenance-First
Every clinical claim includes source citations. No black-box assertions.
Longitudinal Memory
Timeline view spans years of care, not just recent encounters.
Uncertainty-Aware
Explicit flags when data is missing, ambiguous, or contradictory.
Workflow-Ready
Integrates into existing clinical workflows. Requires clinician review.