A layered, microservices-based architecture with federated data fabric and AI-native design — built to scale from 5 pilot sites to 100+ institutions without re-architecture.
Layered, microservices-based architecture with federated data fabric and AI-native design — each tier is independently scalable.
FHIR R4 native ingestion with AI-powered NLP to unlock the 80% of clinical information locked in unstructured notes. Supports HL7 v2 ADT/ORU/ORM messages, CDA documents, CSV flat files, and direct EHR adapters. Automated OMOP CDM mapping enables immediate compatibility with existing research networks. Real-time streaming and batch ingestion pipelines operate in parallel with data quality scoring and automated provenance tracking at every record.
Local data nodes deployed at each institution ensure raw patient data never traverses institutional boundaries. ε-differential privacy (ε ≤ 1.0) governs all federated learning aggregations with mathematically provable privacy guarantees. Cross-silo federated learning allows the network to train shared models on distributed data. GDPR and HIPAA compliant node architecture with encrypted data manifests and secure aggregation protocols enable collective intelligence without collective exposure.
The cognitive core of the platform. Diagnostic clinical decision support with full explainability via LIME and SHAP. Therapeutic matching through multi-omic profiling aligns patients to molecularly-matched therapies. Prognostic models incorporate survival analysis and time-to-event modeling. Deep learning for genomic signatures enables variant interpretation at scale. Digital twin patient simulation supports treatment planning and risk stratification. Natural language generation produces narrative clinical summaries. A versioned model registry ensures reproducibility and regulatory auditability across every model deployed to the network.
SMART on FHIR EHR-embedded applications launch natively within Epic, Cerner, and Oracle Health workflows — no separate login, no context switching. Network-wide dashboards and Statistical Process Control (SPC) charts surface quality and outcomes data in real time. The research workbench provides biostatisticians and investigators a secure analytics environment. AI-powered clinical trial matching surfaces eligible NCT studies at the point of care. Patient-reported outcome (PRO) capture integrations collect structured data directly from patients. Automated care gap alerts drive proactive outreach across the network.
HIPAA/HITECH compliance is the architectural foundation, not an afterthought. OAuth 2.0 and SAML 2.0 SSO integration connects to every institutional identity provider. Immutable audit trails anchored via blockchain-style hashing capture every data access, model inference, and clinical recommendation. AI bias detection and model fairness metrics run continuously against every deployed model. The model registry maintains version lineage with full explainability (LIME/SHAP) surfaced at the point of care. Role-based access control and data use agreements govern what each institutional participant can see and query across the network.
From static guidelines to dynamic knowledge. The platform continuously ingests, appraises, synthesizes, applies, and generates clinical evidence.
PubMed, Cochrane Library, ClinicalTrials.gov, FDA drug label updates, WHO guidelines, and network-generated real-world evidence (RWE) streams feed a continuous ingestion pipeline. Automated de-duplication, citation tracking, and publication-type classification ensure the knowledge base remains current without manual curation overhead.
AI-assisted GRADE scoring (Grading of Recommendations, Assessment, Development and Evaluation) automates the structured appraisal of each new publication, with a clinical expert review layer for high-impact evidence. Confidence scoring, bias risk assessment, and study quality classification are applied to every source, enabling downstream synthesis to weight evidence appropriately.
A dynamic knowledge graph continuously maps disease–biomarker–therapy–outcome relationships across the entire appraised evidence base. Network meta-analysis, indirect treatment comparisons, and Bayesian evidence synthesis aggregate findings across heterogeneous data sources — including randomized trials, observational studies, and network-generated real-world evidence — to produce ranked, context-aware recommendations.
Point-of-care clinical decision support is surfaced directly within the EHR via SMART on FHIR. Recommendations are ranked by evidence strength and contextualized to the individual patient's molecular profile, phenotype, comorbidities, and prior treatment history. Clinicians see not just what to consider, but why — with evidence citations, confidence levels, and SHAP-based explanations for every AI-assisted recommendation.
The network does not merely consume evidence — it produces it. Pragmatic embedded trials, N-of-1 studies, and registry linkage generate prospective real-world evidence directly from clinical care at network sites. This evidence is fed back into the Capture stage, closing the continuous learning loop and progressively reducing the evidence-to-practice gap toward the platform's target of under two years.
Standards-native integration with every major EHR, data warehouse, and clinical research platform.
Migration is not just a transfer — it's an upgrade. Every dataset is enriched with modern terminologies, NLP extraction, quality scoring, and AI-readiness indexing.
Automated source system inventory runs schema analysis, data quality assessment, volume estimation, and field-level data profiling across all candidate source systems. Identifies data gaps, encoding inconsistencies, missing value patterns, and referential integrity violations. Outputs a comprehensive Data Quality Report with a prioritized remediation plan before a single record is migrated.
AI-assisted FHIR R4 resource mapping with confidence scoring at the field level. Mappings above the confidence threshold are applied automatically; low-confidence mappings are routed to a human-in-the-loop validation queue for clinical informaticist review. Comprehensive terminology mapping to SNOMED CT, LOINC, RxNorm, and ICD-10/ICD-11 modernizes legacy code sets in place.
The FHIR R4 conversion pipeline executes the validated mappings at scale. NLP extraction runs against historical clinical notes to surface structured concepts previously buried in free text — diagnoses, symptoms, medications, procedures, and findings. Terminology modernization, AI-readiness indexing, and de-identification pipelines for federated learning cohorts are applied to every record during transformation.
Schema validation confirms structural conformance of every FHIR resource produced. Semantic consistency checks verify that translated concepts preserve clinical meaning. Quantitative reconciliation compares record counts, date ranges, and referential integrity between source and target. Clinical subject matter expert review is structured for domain-specific validation — ensuring that a pediatric gastroenterologist, not just an informaticist, confirms IBD data integrity.
A parallel run period operates both source and Hive systems simultaneously, with automated divergence monitoring flagging any discrepancy between outputs. Clinician user acceptance testing follows a structured sign-off protocol. Live cutover is executed with rollback capability maintained for 30 days. Source data is archived for 12 months post-migration, available for audit and reconciliation queries without requiring source system access.
| Source System | Estimated Timeline |
|---|---|
| Hive Networks (new deployment) | 6 weeks |
| PEDSnet (OMOP CDM) | 8–10 weeks |
| ImproveCareNow | 8–10 weeks |
| EHR Direct (Epic / Cerner) | 10–12 weeks |
| REDCap / Custom Registry | 8–12 weeks |
| Molecular / Genomic Data | 6–8 weeks |
Security is not a layer on top — it is woven into the architecture. Every component is designed to meet the highest standards in healthcare data governance.
Full HIPAA Security Rule compliance across all platform components and deployment environments. HITECH breach notification procedures with documented response timelines. Annual risk assessments using the NIST Cybersecurity Framework. Business Associate Agreements (BAAs) executed with all sub-processors and cloud infrastructure providers.
Differential privacy with ε ≤ 1.0 is applied to all federated learning aggregations, providing mathematically provable bounds on individual-level information leakage. No raw patient data leaves institutional boundaries under any network operation. Secure multi-party computation is employed for sensitive analyses requiring cross-institutional coordination.
Immutable audit trails capture every data access event, model inference call, and clinical recommendation delivery — timestamped, user-attributed, and hash-anchored for tamper evidence. LIME and SHAP-based model explainability is surfaced to clinicians at the point of care alongside every AI-assisted recommendation, satisfying both regulatory and ethical requirements for algorithmic transparency.
Annual SOC 2 Type 2 audits cover the Security, Availability, and Confidentiality trust service criteria. An independent penetration testing program and continuous vulnerability management identify and remediate weaknesses before they can be exploited. Reports are available to network members under NDA upon request.
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