의료 데이터 수익화 분야 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in healthcare data monetization market is expected to grow from USD 229.40 million in 2025 to USD 261.07 million in 2026 and is forecasted to reach USD 519.61 million by 2031 at 14.76% CAGR over 2026-2031.

This report is Segmented by Monetization Model (Direct Data Monetization, Indirect Data Monetization), Solution Type (Data-As-A-Service (DaaS), and Others), Data Type (Clinical Data, and Others), Deployment Mode (Cloud, and Others), End-User (Pharmaceutical and Biotechnology Companies, and Others), and Geography (North America, Europe, Asia-Pacific, and Others). Forecasts are in Value (USD).

Global AI In Healthcare Data Monetization Market Trends and Insights

AI-Driven Clinical and Operational Value Extraction

In the AI in healthcare data monetization market, AI-driven value extraction is widening the set of assets that can be commercialized because it finally makes large volumes of unstructured notes usable at scale. Clinical notes still represent the majority of EHR content, yet older analytics pipelines could not reliably convert them into standardized outputs that buyers could use for licensing, evidence generation, or reimbursement analysis. Tempus AI reported in June 2026 that its multimodal foundation model work spans more than 45 million de-identified patient journeys and more than 500 petabytes of data, which shows how large these new curation pools have already become. Once AI reduces the cost gap between narrative records and structured fields, pathology notes, imaging reports, care plans, and longitudinal summaries can all enter the commercial inventory that providers and platforms manage. IQVIA reinforced this direction in March 2026 when it launched IQVIA.ai with NVIDIA, deploying more than 150 intelligent agents across clinical, commercial, and real-world workflows, with 19 of the top 20 pharmaceutical companies already using IQVIA agents in their work.The same extraction logic is also spreading into hospital operations, where AI can surface coding errors, prior authorization friction, and revenue leakage patterns that are then packaged as monetizable insight products or embedded analytics services.

Real-World Evidence Demand from Payer and Pharma Workflows

In the AI in healthcare data monetization market, demand for real-world evidence is changing the commercial role of data from a passive archive into an active component of payer, pharma, and launch planning workflows. Buyers increasingly want linked datasets that can support coverage discussions, formulary reviews, regulatory submissions, and post-launch tracking without rebuilding methods across separate vendors. That preference favors data suppliers that can connect administrative, clinical, and outcomes records inside one governed environment rather than selling isolated files with limited reuse. As this model spreads, revenue in the AI in healthcare data monetization market moves closer to study design, evidence orchestration, and decision support instead of stopping at raw dataset delivery. The practical effect is that data owners with strong linkage quality and reproducible workflows can command higher value, because buyers are paying for end-to-end evidence readiness as much as they are paying for access to records.

Re-Identification Risk in De-Identified Health Datasets

The AI in healthcare data monetization market faces a direct structural restraint because de-identified records no longer provide the simple legal comfort that earlier commercialization models assumed. A 2025 study in Scientific Reports found that current sanitization methods still left 74% of original information inferable with advanced removal tools, and even differentially private synthetic data showed 48% re-identification rates before full privacy settings were applied. That kind of evidence forces data owners, review boards, and buyers to treat de-identification as a continuing risk-management exercise instead of a completed preprocessing step. Commercially, this raises the review cost of each dataset, narrows the margin on direct licensing products, and slows the release schedule for new assets that were expected to move quickly into buyer workflows. Until trust, auditability, and defensible risk scoring improve further, this issue will continue to limit how quickly the AI in healthcare data monetization market can expand direct-access and high-granularity product offerings.

Other drivers and restraints analyzed in the detailed report include:

1. Privacy-Preserving Data Collaboration Networks
2. Expansion of Federated Learning and Secure Data Clean Rooms
3. Fragmented Consent Management Across Data Contributors

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Direct data monetization held 56.76% of the AI in healthcare data monetization market share in 2025, and is expected to grow at a 16.11% CAGR through 2031, making it the dominant commercial model across current revenue streams. The segment stays large because health systems and specialty vendors can sell curated datasets through subscription, licensing, or controlled-access structures that are already familiar to pharma and payer buyers. Those buyers still prefer direct arrangements in cases where data provenance, reproducibility, and chain-of-custody documentation need to stand up to regulatory or payer review. This makes direct channels especially resilient in evidence generation and formal research workflows, where source control matters more than broad platform flexibility or marginal software savings.

Indirect data monetization remains smaller in current revenue terms, but it reveals where the AI in healthcare data monetization market is gradually moving as buyers pay for outcomes, insight delivery, and governed access rather than simple data transfer. In this model, value is created when data powers embedded AI services, reimbursement support, evidence workflows, or performance-linked commercial arrangements that do not require a traditional dataset sale. As value-based care, outcomes research, and clean-room workflows expand, the AI in healthcare data monetization industry is likely to see indirect models gain importance faster than their current share suggests.

Data monetization platforms held 44.33% of revenue in 2025, giving them the largest solution footprint across the AI in healthcare data monetization market. Their lead came from first-mover control of exchange rails, buyer relationships, and workflow integration long before open API standards began to mature across the wider healthcare technology stack. The segment also benefits from the fact that buyers often want a single coordination layer for access control, contract management, data discovery, and analytical handoff. That combination of operational convenience and embedded trust has allowed platforms to remain the default front end for many commercial transactions in this market.

Data-as-a-service (DaaS) is projected to grow at 16.76% CAGR through 2031, which makes it the fastest-expanding solution type in the AI in healthcare data monetization market. Demand is shifting in this direction because digital health buyers want API-first access, modular procurement, and consumption-based pricing instead of committing to broad platform ownership from the start. analytics-as-a-Service (AaaS) and insight-as-a-Service (IaaS) sit between full platforms and raw data access, and both are gaining relevance as buyers increasingly pay for interpretation, workflow speed, and decision-ready outputs. This means the revenue center of the AI in healthcare data monetization industry is gradually shifting from ownership of software environments toward reliable access to governed data and analytical intelligence delivered in smaller units.

Complete Report Scope:

• By Monetization Model
  • Direct Data Monetization
  • Indirect Data Monetization
• By Solution Type
  • Data-as-a-Service (DaaS)
  • Analytics-as-a-Service (AaaS)
  • Insight-as-a-Service (IaaS)
  • Data Monetization Platforms
• By Data Type
  • Clinical Data
  • Claims and Financial Data
  • Pharmaceutical and R&D Data
  • Patient-Generated Data
  • Operational and Administrative Data
• By Deployment Mode
  • Cloud-Based
  • On-Premise
  • Hybrid
• By End-User
  • Pharmaceutical and Biotechnology Companies
  • Healthcare Providers
  • Healthcare Payers
  • Medical Technology Companies
  • Research Organizations and CROs
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America captured 50.56% of the AI in healthcare data monetization market share in 2025 and remained the largest regional contributor to global revenue. The region's lead rests on deep health IT infrastructure, mature payer-provider exchange networks, and a commercial real-world data ecosystem that is already connected to large pharmaceutical buyer bases. The 21st Century Cures Act and newer FHIR-based access rules continue to lower the marginal cost of aggregation, which intensifies competition at the infrastructure layer even as it broadens the revenue pool for the AI in healthcare data monetization market.

Asia-Pacific is projected to expand at 17.21% CAGR through 2031, which gives the AI in healthcare data monetization market its fastest regional growth profile. The region is moving quickly because China, India, and Japan are pushing government-backed digital health programs that convert large-scale registries, identifiers, and electronic records into assets suitable for secondary use. India's Ayushman Bharat Digital Mission is building a large federated health ID base, while South Korea's national claims databases and Australia's My Health Record system add depth to the regional pool of linkable data. Japan is also emerging as an entry point for international platforms through partnership models, which helps foreign AI data firms reach local buyers without building every part of the infrastructure themselves.

Europe is undergoing a governance-led buildout in the AI in healthcare data monetization market after EHDS Regulation (EU) 2025/327 came into force in March 2025 with EUR 810 million, or USD 849 million, committed to harmonized secondary-use infrastructure. Germany shows the scale of that effort, with 82 institutions registered with the National Health Data Lab in February 2026 and a target of more than 300 active research projects by the end of 2026 using pseudonymized data from 75 million statutorily insured people. Middle East and Africa and South America remain earlier-stage contributors, with GCC investment in national information platforms and Brazil's LGPD laying the governance foundation for future revenue expansion rather than near-term global leadership.

1. Accenture
2. Aetion, Inc.
3. Clarify Health Solutions, Inc.
4. Datavant, Inc.
5. Evidation Health, Inc.
6. Flatiron Health, Inc.
7. H1, Inc.
8. HealthVerity, Inc.
9. Innovaccer
10. Inovalon Holdings, Inc.
11. IQVIA
12. Komodo Health, Inc.
13. Medable, Inc.
14. Microsoft
15. Optum
16. Oracle
17. Salesforce, Inc.
18. SAP
19. SAS Institute
20. Snowflake Inc.
21. Tempus AI, Inc.
22. Verana Health, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI-Driven Clinical and Operational Value Extraction
  • 4.2.2 Real-World Evidence Demand from Payer and Pharma Workflows
  • 4.2.3 Privacy-Preserving Data Collaboration Networks
  • 4.2.4 Expansion of Federated Learning and Secure Data Clean Rooms
  • 4.2.5 Monetization of Longitudinal and Cross-Platform Patient Journeys
  • 4.2.6 Reimbursement Pressure Driving Data-Backed Efficiency Gains
• 4.3 Market Restraints
  • 4.3.1 Re-Identification Risk in De-Identified Health Datasets
  • 4.3.2 Fragmented Consent Management Across Data Contributors
  • 4.3.3 Interoperability Gaps Between Legacy EHR and Analytics Stacks
  • 4.3.4 High Governance Cost for Continuous Model Validation and Auditability
• 4.4 Supply/Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Monetization Model
  • 5.1.1 Direct Data Monetization
  • 5.1.2 Indirect Data Monetization
• 5.2 By Solution Type
  • 5.2.1 Data-as-a-Service (DaaS)
  • 5.2.2 Analytics-as-a-Service (AaaS)
  • 5.2.3 Insight-as-a-Service (IaaS)
  • 5.2.4 Data Monetization Platforms
• 5.3 By Data Type
  • 5.3.1 Clinical Data
  • 5.3.2 Claims and Financial Data
  • 5.3.3 Pharmaceutical and R&D Data
  • 5.3.4 Patient-Generated Data
  • 5.3.5 Operational and Administrative Data
• 5.4 By Deployment Mode
  • 5.4.1 Cloud-Based
  • 5.4.2 On-Premise
  • 5.4.3 Hybrid
• 5.5 By End-User
  • 5.5.1 Pharmaceutical and Biotechnology Companies
  • 5.5.2 Healthcare Providers
  • 5.5.3 Healthcare Payers
  • 5.5.4 Medical Technology Companies
  • 5.5.5 Research Organizations and CROs
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 India
    • 5.6.3.4 Australia
    • 5.6.3.5 South Korea
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East and Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East and Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, Recent Developments)
  • 6.3.1 Accenture
  • 6.3.2 Aetion, Inc.
  • 6.3.3 Clarify Health Solutions, Inc.
  • 6.3.4 Datavant, Inc.
  • 6.3.5 Evidation Health, Inc.
  • 6.3.6 Flatiron Health, Inc.
  • 6.3.7 H1, Inc.
  • 6.3.8 HealthVerity, Inc.
  • 6.3.9 Innovaccer, Inc.
  • 6.3.10 Inovalon Holdings, Inc.
  • 6.3.11 IQVIA Holdings Inc.
  • 6.3.12 Komodo Health, Inc.
  • 6.3.13 Medable, Inc.
  • 6.3.14 Microsoft Corporation
  • 6.3.15 Optum, Inc.
  • 6.3.16 Oracle
  • 6.3.17 Salesforce, Inc.
  • 6.3.18 SAP SE
  • 6.3.19 SAS Institute Inc.
  • 6.3.20 Snowflake Inc.
  • 6.3.21 Tempus AI, Inc.
  • 6.3.22 Verana Health, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment