Flatiron Health

Flatiron Health Architectural Assessment

Flatiron Health’s architecture is centered on a Unified Oncology Data Layer that functions as a centralized aggregator for clinical data. Unlike federated models, Flatiron utilizes a Cloud-Native Ingestion Pipeline to pull data from distributed oncology practices into a multi-tenant environment for centralized processing and normalization 🧠. This architecture enables the transformation of raw clinical inputs into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) 📑.

Operational Scenarios

• Clinical Abstraction: Input: Unstructured Pathology Reports and Clinician Notes -> Process: NLP-assisted Human-in-the-loop Abstraction -> Output: Structured TNM Staging and Biomarker Variables 📑.
• RWE Generation: Input: Longitudinal EHR Records -> Process: De-identification, Normalization, and Mapping -> Output: Research-Grade Dataset for Regulatory Submission 📑.
• Clinical Trial Matching: Input: Real-time Patient Records -> Process: Rule-based eligibility screening against NCCN guidelines -> Output: Pre-screened Patient Cohorts for OncoTrials 📑.

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Security & Compliance Architecture

The platform implements a multi-layered security framework focused on the protection of Protected Health Information (PHI). Data is processed through a proprietary de-identification engine to ensure compliance with the HIPAA Expert Determination method before being utilized for research 📑.

• Access Control: Employs role-based access controls (RBAC) and audit logging for all data mediation activities 🧠.
• Data Sovereignty: Utilizes isolated logical abstraction layers to maintain data integrity for multi-institutional research collaborations 📑.

Interoperability & Integration Layer

Flatiron utilizes a Healthcare Interoperability Adapter pattern to interface with external EHR systems. This facilitates bidirectional data flow, particularly through the 'Clinical Pipe' module which automates the transfer of data from EMRs to electronic data capture (EDC) systems 📑.

• Protocol Support: Connectivity via HL7 FHIR and RESTful APIs for real-time data ingestion 📑.
• Oncology Standard Mapping: Native support for AJCC staging and RECIST criteria in the data normalization phase 📑.

Evaluation Guidance

Technical evaluators should verify the specific latency and throughput of the 'Clinical Pipe' during high-volume data transfers. Organizations must request documentation regarding the specific de-identification algorithms used for unstructured text to ensure alignment with institutional risk tolerance 🌑. Validate the manual verification ratios in the abstraction process to determine the effective human-in-the-loop overhead 🌑.