Every patient encounter generates learning that benefits future patients. We embed AI at every layer — from data ingestion through clinical action — to transform how clinical knowledge is created and applied.
Healthcare generates 30% of the world's data, yet clinical knowledge takes an average of 8 years to reach the bedside — and most of it never arrives at all. Clinicians are overwhelmed by data but starved of actionable insight.
Learning Health Networks have proven that networked collaboration produces measurable outcomes. We add the missing layer: intelligence.
ImproveCareNow: 100+ centers, 30K+ patients, 82% remission in pediatric IBD. 990 physicians across 5 countries demonstrate what networked collaboration achieves.
Existing LHNs lack AI, molecular data integration, clinical decision support, federated analytics, and automated evidence synthesis. We build what's missing.
Raw patient data never leaves institutions. Federated learning with differential privacy allows the network to learn collectively while each institution retains full data sovereignty.
Genomic, transcriptomic, proteomic, and metabolomic data alongside clinical phenotype — a unified multi-omic platform that no existing LHN provides.
An AI-native system where machine intelligence is woven into every layer, from data ingestion through clinical action.
Clinical care and knowledge generation are inseparable. Every patient interaction feeds AI models that continuously improve diagnostic and therapeutic recommendations across the entire network.
AI synthesizes evidence, surfaces patterns, and ranks recommendations — while maintaining full clinical autonomy. Physicians lead; the intelligence layer informs.
Institutions retain complete data sovereignty while contributing to shared intelligence. The network grows smarter with every cell, without any institution sacrificing control.
Layered, microservices-based architecture with federated data fabric and AI-native design.
FHIR R4 native ingestion with AI-powered NLP to unlock unstructured clinical notes. Supports HL7 v2, CDA, CSV, and direct EHR adapters.
Local data nodes at each institution. No raw data movement. Differential privacy and cross-silo federated learning enable collective intelligence without data exposure.
Diagnostic CDS, therapeutic matching, prognostic models, multi-omic deep learning, and digital twin simulation — the cognitive core of the platform.
SMART on FHIR EHR-embedded apps, network dashboards, research workbench, and AI-powered clinical trial matching at the point of care.
HIPAA foundation with OAuth 2.0, immutable audit trails, AI bias detection, model registry, and full explainability for every clinical recommendation.
A domain-agnostic core architecture serving interconnected clinical domains simultaneously, sharing intelligence across every cell.
Phenotype clustering and diagnostic odyssey reduction for patients who have been lost to the system. Natural history modeling, N-of-1 trials, and GA4GH Phenopackets integration.
Tumor genomic profiling, molecularly-matched therapy selection, resistance prediction, and AI-powered clinical trial matching aligned to the mCODE standard.
Flare prediction, treatment optimization, remote patient monitoring, and care pathway personalization with patient-reported outcome tracking integrated at every touchpoint.
Drug repurposing through multi-omic signal detection, safety surveillance, synthetic control arms for adaptive trials, and pharmacogenomic integration for individualized therapy.
From static guidelines to continuous evidence at the point of care. The network generates its own real-world evidence in a continuous cycle.
PubMed, Cochrane, ClinicalTrials.gov, FDA, and network real-world evidence streams
AI-assisted GRADE scoring with expert clinical review and quality confidence metrics
Knowledge graph maps disease–biomarker–therapy relationships across the entire network
Point-of-care CDS ranked and contextualized to each patient's molecular profile
The network produces its own real-world evidence, feeding the cycle continuously
Traditional improvement cycles wait months to collect enough data. Hive's AI layer measures every PDSA cycle in real time — across every site simultaneously — so teams know what's working before the cycle even closes.
AI continuously monitors network-wide outcome variance and proactively surfaces improvement opportunities. Teams define measures, targets, and hypotheses — with baseline run charts pre-populated from network data.
EHR integration captures process measures automatically as the change rolls out. Real-time adoption dashboards track implementation fidelity site-by-site. AI alerts if rollout deviates from protocol before problems compound.
Statistical Process Control charts update with every new data point. AI distinguishes common-cause variation from true improvement signals — no more waiting for a full cycle to know if a change is working. Peer benchmarking shows how your site compares to the network.
When significance is reached, AI generates spread playbooks based on what high-performing sites did differently. Network-wide standardization with local adaptation tracking ensures the right change reaches every patient, not just the sites that ran the pilot.
Statistical Process Control charts update with each new patient encounter — no batching, no waiting.
Compare your improvement cycle against all peer sites simultaneously to identify best practices.
AI distinguishes common-cause variation from true improvement signals — alerting teams the moment a change reaches significance.
Proven changes propagate across the network via AI-generated playbooks, compressing months of spread work to days.
Automated inventory and data quality assessment of all source systems
AI-assisted FHIR mapping with confidence scoring and human validation
FHIR R4 conversion, NLP extraction, terminology modernization
Schema, semantic, and quantitative checks with full reconciliation reporting
Clinician UAT, divergence detection, 12-month archive of source data
From concept to a self-sustaining, internationally deployed learning health network.
First-generation LHN tools digitized the whiteboard. We build the intelligence layer. The gap isn't incremental — it's architectural.
| Capability | Legacy LHN Tools | Hive Networks |
|---|---|---|
| AI / Machine Learning | None | NLP, federated learning, digital twins, predictive models |
| Molecular Diagnostics | None | Full multi-omic: NGS, liquid biopsy, pharmacogenomics |
| Clinical Decision Support | None | SMART on FHIR, real-time, molecularly contextualized |
| Data Architecture | Centralized warehouse | ✓ Federated with differential privacy |
| Analytics | SPC charts, dashboards | ✓ Deep learning, multi-omic, biomarker discovery |
| Evidence Synthesis | Manual / none | PubMed/Cochrane monitoring, automated knowledge graph |
| Clinical Trials | None | AI trial matching, synthetic controls, pragmatic trials |
| Interoperability | Generic APIs | ✓ FHIR R4 native, SMART on FHIR, 21st Century Cures |
"They digitized the LHN whiteboard.
We are building the LHN brain."
The question is not whether AI-powered Learning Health Networks will transform clinical care, but whether you will be among those who build it.
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