The De-identified Health Data Market Size is valued at USD 7.29 Bn in 2023 and is predicted to reach USD 13.77 Bn by the year 2031 at an 8.5% CAGR during the forecast period for 2024-2031.
De-identification is an organizational method used to eliminate personal information from data that is gathered, utilized, stored, and shared with other organizations. Rather than being a single strategy, it encompasses a set of approaches, algorithms, and tools applied to various types of data with differing levels of effectiveness. More aggressive de-identification algorithms typically provide stronger privacy protection, though they can reduce the dataset's overall usefulness. For enterprises, government agencies, and other organizations that seek to make data accessible to external parties, de-identification is especially crucial. It safeguards individuals' privacy by preventing the unauthorized disclosure of their personal health information. This allows de-identified data to be used for diverse research purposes without compromising patient confidentiality and facilitates the sharing of health data for collaborative research and analysis.
Section 164.514(a) of the HIPAA Privacy Rule provides the standard for de-identification of protected health information, stating that health information is not considered individually identifiable if it does not identify an individual and if the covered entity has no reasonable basis to believe it could be used to identify one. Similarly, the General Data Protection Regulation (GDPR) in the European Union sets strict standards for data protection, including the processing of personal data, which can include de-identified health data. Both regulatory frameworks, HIPAA and GDPR, impose stringent requirements for managing personal health information. De-identification serves as a key method for organizations to comply with these regulations while still making health data available for research and collaboration. As healthcare systems become increasingly interconnected, the need for collaboration between institutions, researchers, and pharmaceutical companies grows. De-identified data facilitates the exchange of health information across organizations, supporting joint research, drug development, and clinical trials without compromising patient confidentiality.
The de-identified health data market is segmented based on the type of data, end-use, application. Based on the type of data, the market is divided into clinical, genomic, patient demographics, prescription data, claims data, behavioral data, wearable and sensor data, survey and patient-reported data, imaging data, laboratory data, hospital and provider data, social determinants of health (SDOH) data, pharmacogenomic data, biometric data, operational and financial data, epidemiological data, healthcare utilization data, others. Based on the end-use, the market is divided pharmaceutical companies, biotechnology firms, medical device manufacturers, healthcare providers, insurance companies/ healthcare payers, research institutions, government agencies and others. Based on the application, the market is divided into clinical research and trials, public health, precision medicine, health economics and outcomes research (HEOR), population health management, drug discovery and development, healthcare quality improvement, insurance underwriting and risk assessment, market access and commercial strategy, business intelligence and operational efficiency, telemedicine and remote monitoring, patient engagement and support programs, others.
Based on the type of data, the market is divided into clinical, genomic, patient demographics prescription data, claims data, behavioral data, wearable and sensor data, survey and patient-reported data, imaging data, laboratory data, hospital and provider data, social determinants of health (SDOH) data, pharmacogenomic data, biometric data, operational and financial data, epidemiological data, healthcare utilization data, others. Among these, the clinical data segment is expected to have the highest growth rate during the forecast period. Clinical data includes a wide range of information such as medical histories, diagnoses, treatments, procedures, outcomes, and lab results. This data provides a detailed view of patient health, making it highly valuable for research, healthcare optimization, and decision-making. Clinical data is essential for advanced healthcare analytics, machine learning, and AI-driven models aimed at improving diagnostics, predicting health outcomes, and optimizing healthcare delivery. The use of this data fuels advancements in personalized medicine and value-based care.
Based on the application, the market is divided into clinical research and trials, public health, precision medicine, health economics and outcomes research (HEOR), population health management, drug discovery and development, healthcare quality improvement, insurance underwriting and risk assessment, market access and commercial strategy, business intelligence and operational efficiency, telemedicine and remote monitoring, patient engagement and support programs, others. Among these, the clinical research and trials segment dominates the market. Clinical research and trials require access to vast amounts of health data to generate statistically significant results. De-identified health data provides researchers with large, diverse patient datasets while protecting individual privacy. This is essential for understanding disease progression, treatment effectiveness, and potential side effects across different populations. Pharmaceutical and biotech companies are major consumers of de-identified health data, as they are constantly conducting research for drug discovery and clinical trials. The continuous demand for de-identified data by these industries ensures that the clinical research and trials segment remains dominant.
North America leads in the development and adoption of advanced healthcare analytics, machine learning, and AI technologies. These innovations require large volumes of health data to train algorithms and develop predictive models. De-identified health data is critical to feeding these technologies, driving further demand in the region. The rise of precision medicine in North America, especially through initiatives like the All of Us Research Program in the U.S., requires vast amounts of diverse and de-identified health data to understand genetic, environmental, and lifestyle factors that influence health. North America's leadership in precision medicine drives the demand for de-identified datasets to enable tailored treatments and interventions.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 7.29 Bn |
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Revenue Forecast In 2031 |
USD 13.77 Bn |
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Growth Rate CAGR |
CAGR of 8.5% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type Of Data, By End-Use, By Application |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; South East Asia |
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Competitive Landscape |
IQVIA, Oracle (Cerner Corporation), Merative (Truven Health Analytics), Optum, Inc. (UnitedHealth Group), ICON plc, Veradigm LLC (Formerly known as Allscripts), IBM, Flatiron Health (F. Hoffmann-La Roche Ltd), Premier, Inc., Shaip, Komodo Health, Inc., Evidation Health, Inc., Medidata, Clarify Health Solutions, Satori Cyber Ltd., Kitware, BioData Consortium, Akrivia Health, iMerit |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing and Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global De-identified Health Data Market Snapshot
Chapter 4. Global De-identified Health Data Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2024-2031
4.8. Global De-identified Health Data Market Penetration & Growth Prospect Mapping (US$ Mn), 2023-2031
4.9. Competitive Landscape & Market Share Analysis, By Key Player (2023)
4.10. Use/impact of AI on De-identified Health Data Industry Trends
Chapter 5. De-identified Health Data Market Segmentation 1: By Type of Data, Estimates & Trend Analysis
5.1. Market Share by By Type of Data, 2023 & 2031
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following By Type of Data:
5.2.1. Clinical Data
5.2.2. Genomic Data
5.2.3. Patient Demographics
5.2.4. Prescription Data
5.2.5. Claims Data
5.2.6. Behavioral Data
5.2.7. Wearable and Sensor Data
5.2.8. Survey and Patient-Reported Data
5.2.9. Imaging Data
5.2.10. Laboratory Data
5.2.11. Hospital and Provider Data
5.2.12. Social Determinants of Health (SDoH) Data
5.2.13. Pharmacogenomic Data
5.2.14. Biometric Data
5.2.15. Operational and Financial Data
5.2.16. Epidemiological Data
5.2.17. Healthcare Utilization Data
5.2.18. Others
Chapter 6. De-identified Health Data Market Segmentation 2: By Application, Estimates & Trend Analysis
6.1. Market Share by Application, 2023 & 2031
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following Applications:
6.2.1. Clinical Research and Trials
6.2.2. Public Health
6.2.3. Precision Medicine
6.2.4. Health Economics and Outcomes Research (HEOR)
6.2.5. Population Health Management
6.2.6. Drug Discovery and Development
6.2.7. Healthcare Quality Improvement
6.2.8. Insurance Underwriting and Risk Assessment
6.2.9. Market Access and Commercial Strategy
6.2.10. Business Intelligence and Operational Efficiency
6.2.11. Telemedicine and Remote Monitoring
6.2.12. Patient Engagement and Support Programs
6.2.13. Others
Chapter 7. De-identified Health Data Market Segmentation 3: By End user, Estimates & Trend Analysis
7.1. Market Share by End user, 2023 & 2031
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following End users:
7.2.1. Pharmaceutical Companies
7.2.2. Biotechnology Firms
7.2.3. Medical Device Manufacturers
7.2.4. Healthcare Providers
7.2.5. Insurance Companies/ Healthcare Payers
7.2.6. Research Institutions
7.2.7. Government Agencies
7.2.8. Others
Chapter 8. De-identified Health Data Market Segmentation 6: Regional Estimates & Trend Analysis
8.1. Global De-identified Health Data Market, Regional Snapshot 2023 & 2031
8.2. North America
8.2.1. North America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
8.2.1.1. US
8.2.1.2. Canada
8.2.2. North America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by By Type of Data, 2024-2031
8.2.3. North America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.2.4. North America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by End user, 2024-2031
8.3. Europe
8.3.1. Europe De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
8.3.1.1. Germany
8.3.1.2. U.K.
8.3.1.3. France
8.3.1.4. Italy
8.3.1.5. Spain
8.3.1.6. Rest of Europe
8.3.2. Europe De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by By Type of Data, 2024-2031
8.3.3. Europe De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.3.4. Europe De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by End user, 2024-2031
8.4. Asia Pacific
8.4.1. Asia Pacific De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
8.4.1.1. India
8.4.1.2. China
8.4.1.3. Japan
8.4.1.4. Australia
8.4.1.5. South Korea
8.4.1.6. Hong Kong
8.4.1.7. Southeast Asia
8.4.1.8. Rest of Asia Pacific
8.4.2. Asia Pacific De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by By Type of Data, 2024-2031
8.4.3. Asia Pacific De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.4.4. Asia Pacific De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts By End user, 2024-2031
8.5. Latin America
8.5.1. Latin America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
8.5.1.1. Brazil
8.5.1.2. Mexico
8.5.1.3. Rest of Latin America
8.5.2. Latin America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by By Type of Data, 2024-2031
8.5.3. Latin America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.5.4. Latin America De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by End user, 2024-2031
8.6. Middle East & Africa
8.6.1. Middle East & Africa De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.6.1.1. GCC Countries
8.6.1.2. Israel
8.6.1.3. South Africa
8.6.1.4. Rest of Middle East and Africa
8.6.2. Middle East & Africa De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by By Type of Data, 2024-2031
8.6.3. Middle East & Africa De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by application, 2024-2031
8.6.4. Middle East & Africa De-identified Health Data Market Revenue (US$ Million) Estimates and Forecasts by End user, 2024-2031
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IQVIA
9.2.1.1. Business Overview
9.2.1.2. Key Product/Service Offerings
9.2.1.3. Financial Performance
9.2.1.4. Geographical Presence
9.2.1.5. Recent Developments with Business Strategy
9.2.2. Oracle
9.2.3. Merative
9.2.4. Optum, Inc.
9.2.5. ICON plc
9.2.6. Veradigm LLC
9.2.7. IBM
9.2.8. Flatiron Health
9.2.9. Premier, Inc.
9.2.10. Shaip
9.2.11. Komodo Health, Inc.
9.2.12. Evidation Health, Inc.
9.2.13. Medidata
9.2.14. Clarify Health Solutions
9.2.15. Satori Cyber Ltd.
9.2.16. Kitware
9.2.17. BioData Consortium
9.2.18. Akrivia Health
9.2.19. iMerit
9.2.20. Other Market Players
Global De-identified Health Data Market- By End Use
Global De-identified Health Data Market – By Application
Global De-identified Health Data Market – Type of Data
Global De-identified Health Data Market – By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
