PHKG Business Models & Market

Business models, value propositions, and market analysis for Personal Health Knowledge Graphs, health data curation, and the AIDAVA-adjacent ecosystem. Where is the money? Where will it be in 5 years?

Market Size (Current)

Market	Size	Source
Healthcare Data Monetization	$1.76B (growing)	openPR
Knowledge Graph Market	$6.9B by 2030	MarketsandMarkets
Clinical Analytics	Growing 2025-2030	MarketsandMarkets
Europe Healthcare Analytics	Growing 2025-2030	MarketsandMarkets
Genomic Data Platforms	Growing through 2034	Fortune Business Insights
Global Digital Health	Multi-billion, growing 18%+ CAGR	Various

Existing Business Models

1. Health Data Curation as a Service

What: Curate, clean, and harmonize clinical data from heterogeneous sources into standardized formats (FHIR, OMOP, SNOMED).

Who does this today:

• AIDAVA — research prototype, not yet commercial
• Datavant — US, de-identification and data linking ($7B valuation 2021)
• Verana Health — ophthalmology data curation from EHR
• TriNetX — federated clinical data network for trials
• IQVIA — clinical data services (public, $35B market cap)
• Snowflake — health data cloud platform
• VEIL.AI — health data quality platform

Revenue model: Per-record curation fees, subscription platform fees, or data-as-a-service

Value: Hospitals spend 20-40% of IT budgets on data integration. Automating 45% (AIDAVA's current rate) saves millions per hospital.

EU-specific opportunity: EHDS will require hospitals to make data available in standardized formats by 2029-2030. Hospitals need curation tools NOW.

2. Health Data Intermediary Platform

What: Platform enabling patients to control, curate, and share their health data. Regulated under EU Data Governance Act.

Who does this today:

• MIDATA Cooperative — Swiss data cooperative, patient-owned health data
• Egnosis — Romanian health data intermediary
• MIDATA model: patients store data, consent to sharing, receive value
• Solid Project (Tim Berners-Lee) — personal data pods

Revenue model:

• B2B: Hospitals pay for patient data access for research (anonymized)
• B2B2C: Pharmaceutical companies pay for curated patient cohorts
• Platform fees: % of data sharing transactions
• Subscription: patients pay for data management

Value: By 2030, EU citizens will control their data (EHDS vision). Intermediaries facilitate this. Market for consent management alone is growing rapidly.

Regulatory tailwind: EU Data Governance Act (2022), EHDS regulation (2024), EU AI Act — all require trustworthy data intermediaries.

3. Knowledge Graph Infrastructure

What: Graph databases and semantic platforms for health data integration.

Who does this today:

• Ontotext (GraphDB) — enterprise knowledge graphs, health vertical
• Neo4j — general graph DB, healthcare growing
• Franz Inc. (AllegroGraph) — semantic graph DB for life sciences
• Stardog — knowledge graph platform for pharma
• Amazon Neptune — managed graph DB
• TigerGraph — scalable graph analytics

Revenue model: License + support, cloud consumption, managed service

Value: $6.9B market by 2030. Health is fastest-growing vertical.

4. FHIR/Interoperability Services

What: Build, deploy, and manage FHIR APIs and health data exchange.

Who does this today:

• Firely (FHIR server) — Dutch, HL7 FHIR standard authors
• Smile CDR — Canadian FHIR platform
• Google Cloud Healthcare API — FHIR-native
• Microsoft Azure Health Data Services — FHIR + DICOM
• 1upHealth — US FHIR platform

Revenue model: API calls (consumption), platform subscription, implementation services

Value: FHIR is mandatory in US (CMS Final Rule), becoming mandatory in EU (EHDS). Every hospital, insurer, and health app needs FHIR connectivity.

5. Clinical Data for Research/Pharma

What: Curated, de-identified clinical data sold to pharmaceutical companies for drug development, clinical trial design, real-world evidence.

Who does this today:

• IQVIA — world's largest health data company
• Flatiron Health (Roche) — oncology real-world data
• TriNetX — federated clinical trial network
• Tempus — genomic + clinical data for cancer
• UK Biobank — research data (500K participants)
• OHDSI — open-source observational data network

Revenue model: Per-dataset licensing, subscription access, per-query pricing, revenue share on drug approvals

Value: Pharmaceutical companies pay $50K-$500K+ per curated dataset. A single well-curated oncology dataset can be worth millions. Data quality directly impacts drug approval success.

6. Patient Engagement / PHR

What: Personal health record platforms that engage patients in maintaining their own data.

Who does this today:

• Apple Health Records — iPhone-based PHR
• Google Health — PHR + search
• Epic MyChart — dominant US patient portal
• Patientory — blockchain-based PHR
• 1upHealth — patient-facing FHIR apps

Revenue model: Freemium (patient), B2B (hospital licenses), B2B2C (pharma access to consenting patients)

Value: Patient-engaged data is 3-5x more complete than EHR-only data. More data = better research = better outcomes.

Emerging Business Models (2026-2030)

7. EHDS Compliance Tooling

What: Tools helping hospitals comply with EHDS data availability requirements.

Opportunity: Every EU hospital must make health data available in standardized formats by 2029-2030. This is a MASSIVE compliance market — similar to GDPR but for health data.

Revenue model: Compliance software subscriptions, implementation consulting, ongoing data quality monitoring

Size estimate: 27 EU countries x thousands of hospitals x EUR 50K-500K per hospital = multi-billion market

Timing: EHDS implementation timeline:

• 2025-2026: National transposition
• 2027-2029: Infrastructure build-out
• 2029-2030: Mandatory data availability
• Startups building NOW will have 2-3 year head start

8. Health Data Quality Labels

What: Quality certification for health datasets — like organic labels for food.

Who's building this:

• QUANTUM project (EU-funded, i-HD participating) — "data quality and utility label for EHDS"
• Swiss Personalized Health Network — FAIRification of health data

Revenue model: Certification fees, quality monitoring subscriptions, insurance/risk pricing

Value: High-quality data sells for 5-10x premium. Quality labels enable market pricing.

9. Health Data Cooperatives

What: Patient-owned cooperatives that pool and monetize member health data.

Who's doing this:

• MIDATA Cooperative — Swiss model, patients as cooperative members
• DECODE (Barcelona) — data sovereignty experiment
• Data For Patients (DFP Research) — AIDAVA partner

Revenue model: Members share in revenue from data sales. Cooperative takes % for platform costs.

Value: Aligned incentives — patients benefit directly from sharing. Addresses trust gap in health data.

10. PHKG as Infrastructure

What: Personal Health Knowledge Graph as underlying infrastructure for ALL health applications.

Analogy: PHKG is to health data what the relational database was to business data in the 1980s. Once the infrastructure exists, thousands of applications build on top.

Potential applications:

• Clinical decision support (doctor sees complete patient history)
• Insurance underwriting (automated, consent-based)
• Drug interaction checking (cross-system)
• Preventive health (pattern detection over years)
• Clinical trial matching (automated eligibility)
• Population health (anonymized aggregate analytics)
• Personalized medicine (treatment based on full history)

Revenue model: Platform fee per patient per year (like cloud infrastructure)

Five-Year Outlook (2026-2031)

2026-2027: Foundation

• EHDS regulation transposed into national law
• First EHDS compliance tools emerge
• AIDAVA delivers G2 prototype (project ends Aug 2026)
• Knowledge graph market hits $5B+
• Healthcare data monetization exceeds $2B
• Patient data intermediaries get regulated framework

2027-2028: Build-out

• Hospitals begin EHDS infrastructure investment
• First commercial PHKG platforms launch
• FHIR becomes universal health data language
• Health data cooperatives gain traction (MIDATA model scales)
• AI-curated clinical datasets become standard for pharma R&D
• Quality labels for health data established

2028-2029: Acceleration

• EHDS compliance deadlines approach — panic buying of tools
• PHKG infrastructure enables new applications (trial matching, predictive health)
• Patient-engaged data platforms reach critical mass
• Health data marketplaces operate at scale
• First health data cooperatives pay dividends to members
• Big tech (Google, Apple, Microsoft) deepen health data positions

2029-2030: Mandate

• EHDS mandatory data availability kicks in
• Every EU hospital must provide standardized data
• PHKG becomes standard infrastructure (like EHR in 2010s)
• Clinical data curation is largely automated (80%+ vs AIDAVA's 45% today)
• Health data quality determines research competitiveness
• Patient data sovereignty becomes norm

2030-2031: Maturity

• Health data interoperability solved in EU
• PHKG infrastructure enables precision medicine at scale
• Data-driven preventive health reduces healthcare costs 10-20%
• Health data cooperatives compete with traditional data brokers
• Personalized health management based on complete longitudinal records
• Research breakthroughs from previously siloed data

Competitive Landscape

Who's positioned to win:

Category	Leaders	Challengers	Opportunity
Data Curation	IQVIA, Datavant	AIDAVA (research), VEIL.AI	EU-specific, EHDS-compliant curation
Knowledge Graphs	Neo4j, Ontotext	Stardog, TigerGraph	Health-specific vertical KG platform
Interoperability	Google, Microsoft, Epic	Firely, Smile CDR	EU-focused, small hospital segment
Data Intermediaries	MIDATA (niche)	Egnosis, DFP Research	First to scale in EU wins
Clinical Data	IQVIA, Flatiron	TriNetX, OHDSI	EU data with GDPR/EHDS compliance
PHR	Apple, Epic	None dominant in EU	EU-compliant, patient-owned PHR

Implications for a Startup

If building in this space today:

1. Start with EHDS compliance — hospitals will pay for this NOW
2. Use knowledge graphs — they're the right architecture for longitudinal data
3. Build for the intermediary model — Data Governance Act creates this category
4. Focus on EU — regulatory tailwind is massive, US market already crowded
5. Automate curation — AIDAVA proved 45% is possible, target 80%+
6. Engage patients — they become data stewards under EHDS
7. Quality is the moat — better data quality = premium pricing

See Also

• AIDAVA
• AIDAVA Documents
• Knowledge Graphs in Health
• Interoperability
• Soft Funding Norway
• EU Regulation