Platform

The Patient Intelligence Layer for modern healthcare

Holimedical sits above systems of record — EHRs, claims, labs, devices — and turns them into one living, explainable model of each patient.

Hospitals
Labs
Pharmacies
Wearables
Insurers
Specialists

Patient Intelligence Layer

Longitudinal Health Graph
Ontology Engine
Risk & Insight Engine
AI Copilot

Infrastructure: Naas AI ontology, knowledge graph & agent orchestration · FHIR-native · HIPAA-ready

Care Teams

Risk stratification · coding support · preventive care

Patients

Health profile · digital twin · AI companion

ACOs & Systems

Population health · shared savings optimization

Architecture

Six layers, one system

Data Connectivity

FHIR R4-native ingestion from EHRs (Epic, Cerner, Athena), HL7v2 feeds, claims (X12 837/835), lab interfaces, pharmacy networks, and consumer wearables. Built for the post-information-blocking, TEFCA-enabled interoperability era.

Longitudinal Health Graph

Every record is resolved into a unified patient graph: conditions, medications, labs, encounters, devices, social determinants. Entity resolution and temporal modeling turn documents into a queryable model of the patient.

Ontology Engine

Powered by Naas AI. Medical ontologies (SNOMED CT, LOINC, RxNorm, ICD-10) ground every node in shared clinical semantics — making insights explainable, auditable, and portable across systems.

Intelligence & Agents

Risk stratification, care gap detection, HCC coding opportunity mining, preventive care recommendations, and pre-visit summarization — orchestrated as governed agents with humans in the loop, never autonomous diagnosis.

Workflow Delivery

Insights are delivered where work happens: physician panel views, pre-visit briefs, ACO population dashboards, and patient-facing digital twin experiences.

Trust & Compliance

HIPAA-ready architecture, end-to-end encryption, full audit trails, role-based access, and SOC 2 roadmap. Explainability is a design constraint, not an afterthought.

The Health Graph

Every patient becomes a living graph

Instead of hundreds of PDFs and siloed tables, each patient is modeled as connected entities with temporal context. The graph is what makes risk explainable: every alert traces back through edges to source records.

SNOMED CTLOINCRxNormICD-10-CMFHIR R4TEFCA-ready
PatientType 2 DiabetesHypertensionMetforminLisinoprilHbA1c 8.2%eGFR 62Annual WellnessCGM StreamCKD Risk ↑

See it with real workflows

The live demo runs the full product experience on a synthetic 50-patient panel.