Palantir Foundry for Health

Palantir Foundry for Health System Architecture Assessment

As of January 2026, Foundry for Health has evolved into a dynamic Agentic Operating System powered by Palantir AIP. The architecture's primary differentiator is the Healthcare Ontology, a semantic layer that maps fragmented EHR, lab, and imaging data into standardized 'Objects' (e.g., Patient, Procedure, Bed) 📑. This abstraction ensures that LLM-based agents in the AIP layer interact with governed, versioned data via a strict Logic Layer rather than raw, unstructured schemas 🧠.

Core Data Fabric & Semantic Ontology

The system maintains a rigorous data lineage from ingestion to action, ensuring every diagnostic or operational decision is auditable.

• Multi-modal Interoperability: Native ingestion of HL7 FHIR R5, OMOP CDM, and DICOM formats, utilizing automated pipeline versioning to prevent data drift 📑.
• Governance & Privacy: Implements 'Purpose-Based Access Control' (PBAC), where data access is automatically restricted based on the specific intent of the query rather than static user roles 📑.
• AIP Logic Layer: Agents execute actions via a library of Functions—pre-coded, deterministic units of logic that prevent LLM hallucinations by restricting AI to predefined operational bounds 📑.

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Operational Scenarios

• Elective Recovery Optimization: Input: Surgical waiting lists (CSV) + Staff availability (EHR) → Process: AIP Agentic logic cross-references patient acuity with theatre capacity using the Ontology's 'Bed' and 'Staff' objects → Output: Optimized weekly theatre schedule with automated patient notification drafts 📑.
• Clinical Drug Shortage Management: Input: Real-time inventory levels (ERP) + Clinical prescribing trends (EHR) → Process: Predictive supply chain model identifies 14-day depletion risk for critical oncological agents → Output: Automated procurement request and suggested alternative care pathways for affected patients 📑.

Evaluation Guidance

Technical evaluators should verify the following architectural characteristics:

• Ontology Compute Overhead: Benchmark the resource consumption when re-indexing large-scale (1B+ row) clinical datasets into the 'Patient' object model 🧠.
• AIP Functional Constraints: Review the library of available 'Functions' to ensure the agentic layer cannot execute non-deterministic actions in clinical workflows 📑.
• Real-time Sync Latency: Organizations should validate the synchronization lag of the 'Hermes' engine when propagating data from localized edge hospitals to a centralized regional command center 🌑.