NeuroTwin Patient Brain Model
LLM
Neuro-Psych LLM Co-Pilot
Evidence-backed reasoning routed through the POD LLM Gateway.
EVAL
Evaluation & Simulation
Continuous accuracy, calibration and guideline scoring.
SEC
Blockchain Security
Every call policy-checked, every output auditable.
OPS
Clinical Control Plane
Agent identity, data access and workflow monitoring.
5 layers
one governed AI stack
80%+
AI accuracy + Agentic RAG
NeuroTwin
on every patient decision
Firewall
blockchain-secured deployment
LAYER 1 · CLINICAL APPLICATION AGENTS
How clinicians, patients & families use AI
Purpose-built agents for the full arc of brain care: Decode · Decide · Deliver. NeuroPrecision Dx, OpenMind Co-Pilot, Piper Companion, Trial-Match and Billing Access.
▲ orchestrated by ▲
LAYER 2 · ORCHESTRATION & CONTROL PLANE
Clinical Workflow Orchestration the POD Master Agent
Owns the workflow, routes work to specialized sub-agents, brings context and loop engineering to bear: MCP + Tools, Graph + Agentic RAG, and closed-loop recalibration.
▲ reads & writes ▲
LAYER 3 · UNIFIED PATIENT RECORD
AI Patient CaseVault current & complete
A longitudinal, multi-modal patient view — genomics, clinical history, behavioral signals and IoT vitals — always current and workflow-ready.
▲ interprets & integrates ▲
LAYER 4 · POD AI INTEGRATIONS
Best-of-breed AI, governed centrally
The POD neuro-psych LLM family alongside frontier models, evidence engines and clinical scribes — all routed through a governed LLM Gateway.
▲ sources from ▲
LAYER 5 · EHR, LAB & GENOMIC FOUNDATION
Reached in place, behind your firewall
Athena, NextGen, Epic, labs, whole-exome genomics, Mayo RWE and Piper IoT are pulled natively no re-keying, no data leaving your environment.
The POD AI Trust Stack
Cross-cutting oversight agents inspect every layer, top to bottom.
Evaluation Agents
Continuous accuracy, calibration and guideline scoring on live dashboards.
Judge & Jury Agents
Independent cross-examination of every output to suppress hallucination.
Governance Agents
HIPAA compliance, guideline adherence, clinical policy and bias checks.
Observation & Security
Blockchain-secured records, decision tracing and policy-enforced access.
Every agent registered · every call policy-checked · every output auditable.
AI Orchestration for Brain Health
AI Agents
Variant-interpretation, drug-response, therapy-ranking, trial-matching and monitoring agents each orchestrating scoped sub-agents up the stack.
Data Sources & Tools
EHR, labs, whole-exome genomics, prior trials and IoT device feeds reached in place via governed MCP servers behind the firewall.
RAG + Knowledge Graphs
Graph + Agentic RAG over the neuro-psych ontology and patient-graph models drives POD AI accuracy beyond 80% and grounds every decision.
Loop Engineering
Genomics → reasoning → treatment → outcome → recalibration. Each closed loop makes the next recommendation sharper.
Not merely retrieving evidence to support a decision — modeling the patient’s brain, predicting the response, and grounding it in evidence. That is the leap from decision support to decision intelligence.
The Data Platform
1
Ingest from every source
POD ingests structured and unstructured data from Athena, NextGen, Epic, whole-exome PGx, labs, prior trials, family history and Piper IoT signals.
2
Interpret & label
The neuro-psych LLM family reads across years of record; annotation agents label every element against a purpose-built neuro-psych ontology.
3
Drive multi-agent workflows
The unified, cited CaseVault feeds the NeuroTwin, OpenMind, trial-matching and monitoring agents from one longitudinal source.
4
Learn from outcomes
Outcomes feed back into the patient graph and agent evaluation layer so every additional patient improves the engines that serve the next.
Why the flywheel is defensible
POD’s advantage compounds because every loop produces new structured clinical variables, richer patient graphs, and stronger evaluation data not just more documents in a vector store.
Evaluation & Continuous AI Training
Neuro-Psych Rules
Does the response conform to neurology and psychiatry clinical rules and the evidence guideline base for this condition?
Patient Compliance
Will the recommendation be feasible for this patient’s care plan, risk profile, family context and adherence pattern?
Treatment Accuracy
Does the plan align with genomics, current medications, prior trials, phenotype and expected response trajectory?
Security
Is the output compliant with agent policy, data-access scope, privacy requirements and adversarial attack defenses?
Continuous evaluation
Live scoring on accuracy, calibration, guideline adherence and agreement with ground truth.
Simulate before deploy
Responses are tested against clinical goals before they reach clinicians, patients or families.
Continuous training
Evaluation data feeds back into agent policy, loop engineering and model improvement workflows.
Compliance
HIPAA and runtime policy checks before output exposure.
Guideline Adherence
Clinical rule and evidence-base scoring for every response.
Clinical Policy
Health-system specific restrictions and escalation requirements.
Bias Detection
Equity and safety checks across recommendations and access paths.
Continuous Operations & Security
Agent Identity & Registration
Every agent POD Health or registered third party must be registered and carry a runtime-enforced policy contract.
Blockchain Records & Data Access
Immutable decision traceability and policy-scoped data access keep every clinical recommendation auditable.
Attack Defense
Adversarial prompts, policy breaches and non-compliant tools are detected, quarantined and blocked before clinical exposure.
A realtime cockpit for clinician + AI collaboration, throughput, accuracy and bottleneck resolution.
Clinician + AI collaboration
Throughput & accuracy
Bottleneck resolution
Bring governed, explainable and auditable AI into precision brain health without moving data outside the health-system firewall.