A domain-agnostic core architecture that serves interconnected clinical specialties simultaneously — sharing intelligence, infrastructure, and evidence across every cell of the network.
300 million people worldwide live with a rare disease. The average diagnostic odyssey spans 5–7 years and 8 or more physicians. Hive Networks collapses that timeline by connecting patient phenotypes across institutions — finding patterns no single site could detect alone.
AI-powered unsupervised clustering identifies patient subgroups across the network using HPO (Human Phenotype Ontology) terms, enabling discovery of novel disease subtypes that no single institution could surface alone.
Federated similarity search matches undiagnosed patients to diagnosed cohorts across institutions, surfacing candidate diagnoses with supporting evidence — compressing years of diagnostic wandering into days.
Longitudinal data from rare disease registries builds probabilistic natural history models that predict disease trajectory and inform trial eligibility, generating evidence where none previously existed.
Single-patient experimental designs with Bayesian statistical frameworks allow evidence generation for diseases too rare for conventional RCTs — turning every treated patient into a source of publishable evidence.
Native support for GA4GH Phenopacket schema enables interoperability with international rare disease registries including SOLVE-RD and the Undiagnosed Diseases Network, extending the network's reach globally.
Cross-institutional patient similarity scores connect patients with matching phenotypes and genotypes, enabling recruitment for natural history studies, compassionate use programs, and early access trials.
HPO-encoded phenotype capture at point of care, structured alongside genomic and clinical data for downstream analysis and cross-institutional comparison.
Similarity algorithms run locally at each institution; only aggregated match scores cross institutional boundaries — preserving patient privacy while enabling network-scale discovery.
Matched cohorts feed automated evidence synthesis pipelines for disease characterization, treatment response analysis, and natural history documentation.
Clinicians receive ranked differential diagnoses, relevant case matches from across the network, and real-time trial eligibility notifications — surfaced within their existing EHR workflow.
Cancer care is increasingly defined by molecular profile, not tumor location. Hive Networks integrates next-generation sequencing, liquid biopsy, and multi-omic data with real-world clinical outcomes — enabling molecularly-matched therapy selection and predictive resistance modeling at scale.
NGS panel integration (SNV, CNV, fusion, TMB, MSI) with automated variant annotation against ClinVar, OncoKB, and COSMIC databases — delivering a clinically actionable genomic report within the EHR workflow.
Molecularly-ranked therapy recommendations using FDA approvals, NCCN guidelines, and real-world evidence from the network — updated continuously as the network accumulates outcome data from participating sites.
Longitudinal multi-omic tracking identifies emerging resistance mechanisms and surfaces second-line options before clinical failure — giving oncologists lead time to adapt treatment strategy proactively.
AI-powered eligibility screening against ClinicalTrials.gov using molecular biomarkers, prior treatment history, and performance status — automatically surfacing relevant open trials to the care team.
ctDNA and cfDNA data ingestion for treatment monitoring, minimal residual disease (MRD) detection, and early relapse prediction — enabling non-invasive longitudinal tumor monitoring at any point in the care journey.
Native support for Minimal Common Oncology Data Elements (mCODE) FHIR profile ensures interoperability with national cancer data initiatives including ASCO's CancerLinQ and the National Cancer Institute's CRDC.
Tumor NGS + germline WES + RNA-seq + liquid biopsy data ingested and harmonized into a unified molecular profile within the patient's longitudinal record.
Automated annotation against curated knowledge bases (ClinVar, OncoKB, COSMIC, FDA drug labels) with clinical evidence grading and actionability classification.
Network-wide real-world outcomes combined with clinical guidelines to rank therapeutic options by molecular match score and evidence strength — updated as new data accumulates.
Ranked recommendations surfaced within the oncologist's EHR workflow via SMART on FHIR — no new interfaces, no context switching, no lost time.
The ImproveCareNow network demonstrated the power of networked learning in pediatric IBD — achieving 82% remission rates across 100+ centers. Hive Networks applies the same federated intelligence model to oncology, where molecularly-diverse patient populations demand network-scale data to detect meaningful biomarker-outcome associations. A single institution treating 200 patients with a rare KRAS variant cannot generate statistical confidence. A network of 100 institutions treating 20,000 such patients can.
Chronic disease management is failing at scale. Fragmented data, reactive care, and limited personalization leave patients cycling through ineffective treatments. Hive Networks enables predictive, proactive, personalized care — learning from every patient interaction to improve the next.
Time-series machine learning models trained on longitudinal EHR, PRO, and wearable data predict disease flares 2–4 weeks ahead of clinical presentation — enabling intervention before the patient deteriorates.
Comparative effectiveness analysis across the network identifies optimal treatment sequences for individual patient phenotypes — moving beyond one-size-fits-all guidelines to truly personalized care pathways.
Passive and active RPM data (wearables, app-reported PROs, connected devices) integrated into the longitudinal patient record, enriching predictive models with between-visit signal that EHR data alone cannot provide.
AI-generated, evidence-ranked care pathways adapt to the patient's molecular profile, comorbidities, and prior treatment response — continuously updated as the patient's condition evolves and the network learns.
Patient-reported outcomes (PROMIS, disease-specific PROs) collected longitudinally and integrated into clinical decision support — capturing the patient's experience of disease, not just the clinician's observation of it.
Automated identification of patients overdue for monitoring, labs, or follow-up — surfaced to care coordinators via the network dashboard with patient-level prioritization by risk score and gap severity.
EHR, PRO, wearable, and claims data ingested and harmonized in near real-time into the patient's longitudinal record — creating a complete, continuous picture of disease burden.
Patients clustered by disease trajectory, risk profile, and treatment response patterns across the network — enabling comparison to similar patients and identification of optimal care strategies.
Flare risk, hospitalization risk, and treatment response predictions generated continuously for each patient — with model confidence scores and contributing factor explanations for clinical transparency.
Risk alerts, care gap notifications, and personalized care pathway recommendations surfaced to the care team — with enough context to act confidently without leaving the EHR.
Drug development is broken. 90% of candidates fail in clinical trials, often because the right patient population was never identified. Hive Networks transforms real-world clinical data into a drug development engine — accelerating discovery, reducing trial failure, and enabling precision pharmacology.
Multi-omic signal detection identifies approved drugs with unexplored mechanism matches for new indications, dramatically reducing development timelines by leveraging existing safety data and manufacturing infrastructure.
Federated pharmacovigilance across the network identifies adverse event patterns and drug-drug interactions invisible to single-institution reporting — generating safety insights far earlier than post-market surveillance alone.
Real-world patient cohorts from the network serve as synthetic control arms for single-arm trials, reducing trial size requirements and accelerating regulatory submissions per FDA Real-World Evidence guidance.
PGx data (CYP2D6, CYP2C19, DPYD, TPMT, and 50+ additional loci) integrated into therapy recommendations to optimize dosing, minimize adverse events, and identify patients at risk of treatment failure.
Network-wide multi-omic analysis identifies novel biomarkers for patient stratification, treatment response prediction, and disease monitoring — generating hypotheses at a scale impossible within any single institution.
AI-powered patient matching against ClinicalTrials.gov eligibility criteria with automated site feasibility assessment across the network — cutting median recruitment timelines by identifying eligible patients before the trial site opens.
Multi-omic and clinical data mined continuously for drug-phenotype associations, adverse event patterns, safety signals, and novel biomarker candidates across the entire federated network.
AI synthesizes detected signals into ranked drug repurposing hypotheses with supporting real-world evidence, biological plausibility scores, and estimated development pathway timelines.
Synthetic control arms and trial-eligible patient cohorts assembled from federated network data — enabling prospective trial planning with realistic enrollment projections per site.
Network-generated real-world evidence packaged per FDA Real-World Evidence guidance for IND submissions, supplemental NDA applications, and label extension requests.
Every patient treated within the Hive network is a data point in the world's largest ongoing pragmatic trial. When a new therapy is approved and deployed across 100 network sites, the real-world outcomes data begins flowing immediately — generating post-market safety and effectiveness evidence that individual sites could never produce independently. This continuous real-world evidence engine supports label extensions, informs guideline updates, and generates the signal that drives the next generation of clinical hypotheses.
Schedule a domain-specific clinical consultation with our team. We'll assess your current data infrastructure, identify the highest-impact starting point, and outline a deployment pathway tailored to your institution's goals.