v1.0 DRAFTCONFIDENTIALkwiKBio Health Gateway
kwiKBio Health Gateway
Architectural Specification
This document defines the transition of kwiKBio from a biomedical discovery platform into a patient-centric, interactive Health Gateway. It covers the data ingestion model, ARS engine integration, security posture, and the full end-to-end loop connecting wearable signals to research outputs and vendor services.
Engine
ARS · FastScience v7
Author
Hermes / HydroJoule
1. Strategic Transition — Research Tool → Health Gateway
kwiKBio was built as a biomedical research acceleration tool: ingest unstructured data, extract RDF knowledge graphs, run SLAM causal mining, surface novel connections. The Health Gateway extends this architecture toward the individual patient — making the same research intelligence that serves scientists available to the person managing their own body.
1.1 Core Product Shift
- From: Researcher uploads lab notes → ARS extracts claims → SciCrush pool
- To: Patient connects wearables + EHR → ARS maps to research corpus → personalized guidance
- Continuity: The same RDF graph engine, SLAM mining, and US 11282088 patent underlie both products
- Key addition: Real-time wearable data streams, FHIR-compliant health record ingestion, private LLM inference
1.2 Target User
- Health-conscious individuals seeking research-grade insights without clinical barriers
- Patients managing chronic conditions who want to bridge their personal data to current literature
- FS! Fellows and kwiKBio power users seeking longitudinal health tracking + research matching
- Physicians wanting a second-opinion tool against the ARS corpus for patient counseling
2. Data Ingestion Model
The Health Gateway accepts three classes of health data, each mapped to a standardized internal representation before ARS processing.
2.1 Wearables API
- Supported sources: Apple HealthKit, Google Health Connect, Fitbit, Garmin, Oura, WHOOP
- Protocol: OAuth 2.0 device authorization → polling refresh every 15 min
- Primary signals: resting HR, HRV (RMSSD), SpO₂, sleep stages, active energy, VO₂ max estimate
- Internal format: time-series JSON → RDF triples (schema:HealthObservation + FHIR Observation profile)
- Anomaly detection: Z-score gate on 30-day rolling baseline; flags surface to chat AI
2.2 FHIR EHR Integration
- Standard: HL7 FHIR R4 (SMART on FHIR for OAuth-authorized access)
- Accepted resources: Patient, Observation, DiagnosticReport, MedicationStatement, Condition, Procedure
- Upload path: PDF (OCR → structured extraction via LLM), CSV (column-mapped ingestion), direct FHIR JSON
- Provenance: every extracted triple carries source document hash + extraction confidence score
- Linkage: patient sub-graph automatically cross-referenced against kwiKBio research corpus via ARS SLAM
2.3 Genomic VCF (Roadmap)
- Accepted format: VCF 4.2 (23andMe, AncestryDNA, clinical WGS/WES exports)
- Processing: variant annotation against ClinVar, gnomAD; pharmacogenomics via PharmGKB
- ARS integration: genetic risk factors surface as weighted edges in personal knowledge graph
- Privacy: VCF never leaves the user's local inference node; only anonymized risk signals are transmitted
3. ARS Engine Integration
The Automated Research System (US Patent 11282088, FastScience v7) forms the intelligence backbone. All personal health data is projected into the same RDF/Turtle graph structure used for the research corpus, enabling direct causal mining between personal signals and peer-reviewed findings.
3.1 Query Module
- Natural language health question → SPARQL-equivalent graph traversal
- Personalization layer: query weighted by patient sub-graph proximity
- Output: ranked research claims with provenance, confidence, and relevance score
- Latency target: <800ms for cached sub-graph; <3s for full corpus traversal
3.2 SLAM Causal Mining
- Sub-Linear Association Mining surfaces non-obvious causal chains between health signals and outcomes
- Example: wearable HRV decline → stress marker → cortisol dysregulation → sleep architecture disruption (3-hop chain)
- Personal graph proximity boosts relevance scoring for matched chains
- New chains surface proactively to the chat AI as "unsolicited insights"
3.3 Clinical Trial Matching
- Integration: ClinicalTrials.gov API + private SciCrush pool
- Matching criteria: diagnosis codes (ICD-10), biomarker ranges, demographic eligibility, location
- Output: match card with trial ID, eligibility confidence, enrollment status, PI contact
- Opt-in: patient must explicitly approve trial referral before any data transmission
4. Security Posture & Privacy Architecture
Health data carries the highest privacy burden. The kwiKBio Health Gateway is designed with a zero-trust, local-first architecture.
4.1 HIPAA Compliance Framework
- PHI handling: all Protected Health Information encrypted at rest (AES-256) and in transit (TLS 1.3)
- Access control: RBAC with audit logging for every PHI access event
- Data minimization: only de-identified aggregate signals transmitted to cloud ARS; raw PHI never leaves edge
- BAA: Business Associate Agreement available for clinical deployments
- Breach notification: 60-day notification pipeline per HIPAA §164.400
4.2 Zero-Knowledge Storage
- Personal knowledge graph stored encrypted on user's device or a user-controlled storage endpoint
- Server-side: only encrypted ciphertext stored; decryption keys never leave client
- Genomic data: never transmitted; processed locally via WASM-compiled variant annotator
- Wearable tokens: OAuth refresh tokens stored in OS secure enclave (iOS Keychain / Android Keystore)
4.3 Local Inference Option
- All chat AI inference can run fully local via Ollama (Llama 3.1 / Mistral) on user device
- Cloud inference path (OpenAI/Anthropic) available but opt-in; de-identifies inputs before transmission
- ARS query: graph traversal runs on edge; only query vectors (not raw data) optionally sent to cloud
5. End-to-End Architecture Loop
The full Health Gateway loop connects personal signals to research discovery to actionable guidance, completing the kwiKBio virtuous cycle.
| Layer | Component | Function | Output |
|---|
| 1 · Ingest | Data Bridge | Wearables API + FHIR EHR + VCF upload | Normalized health observations |
| 2 · Model | ARS Engine | RDF triple extraction + SHACL validation | Personal knowledge sub-graph |
| 3 · Mine | SLAM | Causal chain discovery across personal + research graphs | Ranked association chains |
| 4 · Match | Query Module | NL health question → graph traversal + trial matching | Research-backed guidance + trials |
| 5 · Publish | Protocol Library | Actionable protocols + SciCrush contribution | Personalized health protocol |
| 6 · Transact | Contracting Engine | Vendor referrals + trial enrollment | Vendor Research Services revenue |
5.1 Vendor Research Services Integration
- Matched user profiles (de-identified, consent-gated) can be submitted to VRS partners
- Partners: supplement brands, pharmaceutical research orgs, digital therapeutics, clinical CROs
- Revenue model: per-referral fee + data licensing (aggregate anonymized cohort analytics)
- Contracting Engine automates NDA, consent forms, and revenue share via smart contract
6. Build Roadmap
Phase 1 — MVP (current)
- ✅ Health portal landing page + interactive chat UI (this build)
- ✅ Mock wearable, records, and research data streams
- ✅ SVG multi-element logo lockup (💧🔥❤️🙏)
- 🔲 Connect to live Ollama inference on Aqua for chat responses
- 🔲 Real HealthKit / Health Connect OAuth integration
Phase 2 — Data Integration
- 🔲 FHIR R4 PDF ingestion pipeline (LLM extraction → RDF)
- 🔲 Real-time wearable polling + anomaly detection
- 🔲 Personal ARS sub-graph construction from health data
- 🔲 ClinicalTrials.gov API match integration
Phase 3 — Research Bridge
- 🔲 SLAM causal mining on personal + research graph
- 🔲 Proactive insight delivery via kwiKBio Health chat
- 🔲 VRS referral engine + consent workflow
- 🔲 Genomic VCF ingestion (WASM local processing)
- 🔲 FS! Fellows health cohort dashboard
kwiKBio Health™ · Specification v1.0 Draft · Built by Hermes ⚡ on Aqua (187.77.210.56) · HydroJoule Swarm · ← Back to Health Portal