De-identified Healthcare Data Market Outlook

According to our latest research, the global de-identified healthcare data market size reached USD 3.4 billion in 2024. The market is expanding at a robust CAGR of 15.2% and is forecasted to attain a value of USD 10.9 billion by 2033. This remarkable growth is primarily driven by the increasing demand for privacy-compliant data solutions that enable research, analytics, and innovation without compromising patient confidentiality. The adoption of stringent data privacy regulations and the rapid digitization of healthcare records are further fueling the market’s momentum.

One of the primary growth factors for the de-identified healthcare data market is the rising emphasis on patient privacy and security. The implementation of regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe has necessitated robust data de-identification processes. These regulations mandate the removal of personally identifiable information from healthcare datasets, making de-identified data a critical resource for organizations aiming to comply with legal requirements while still leveraging valuable insights for research and analytics. As healthcare organizations increasingly digitize patient records and data sharing becomes more prevalent, the demand for effective de-identification solutions continues to surge, driving market growth.

Another significant driver is the exponential growth in healthcare data volume, propelled by the widespread adoption of electronic health records (EHRs), wearable devices, and genomics. The sheer scale and diversity of healthcare data present both opportunities and challenges for healthcare stakeholders. De-identified data allows organizations to harness this vast information pool for applications such as clinical research, drug development, population health management, and artificial intelligence (AI) model training. Pharmaceutical and biotechnology companies, in particular, are leveraging de-identified datasets to accelerate drug discovery, optimize clinical trials, and identify patient cohorts, thereby shortening development timelines and reducing costs. This trend is expected to intensify as precision medicine and data-driven healthcare models gain traction globally.

Technological advancements are also playing a pivotal role in shaping the de-identified healthcare data market. The emergence of sophisticated de-identification software, advanced encryption algorithms, and secure data sharing platforms has enhanced the ability of organizations to anonymize and utilize healthcare data effectively. Artificial intelligence and machine learning tools are being increasingly deployed to automate the de-identification process, improving scalability and accuracy. Furthermore, partnerships between healthcare providers, technology vendors, and research institutions are fostering innovation and facilitating the adoption of best practices in data privacy. As these technologies continue to evolve, they are expected to lower operational barriers and expand the market’s reach across various healthcare segments.

From a regional perspective, North America holds the largest share of the de-identified healthcare data market, accounting for over 42% of global revenue in 2024. This dominance is attributed to the region’s advanced healthcare infrastructure, strong regulatory framework, and high adoption of digital health technologies. Europe follows closely, driven by stringent data privacy laws and robust investments in healthcare IT. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digital transformation, increasing healthcare expenditure, and growing awareness of data privacy issues. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as governments and healthcare organizations prioritize data-driven healthcare initiatives.

Component Analysis

The de-identified healthcare data market by component is segmented into software, services, and platforms. Software solutions form the backbone of the market, providing automated tools for data masking, anonymization, and encryption. These solutions are in high demand due to their ability to efficiently process vast volumes of healthcare data while ensuring compliance with regulatory standards. A

This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey. This repository contains all of the submitted deidentified data instances each accompanied by a detailed abstract describing how the deidentified data were generated. We conduct an extensive standardized evaluation of each deidentified instance using a host of fidelity, utility, and privacy metrics, using out tool, SDNist. We?ve packaged the data, abstracts, and evaluation results into a human- and machine-readable archive.

IntroductionThe study aimed to evaluate visualization-based training’s effects on lung auscultation during clinical clerkship (CC) in the Department of Respiratory Medicine on student skills and confidence.MethodsThe study period was December 2020–November 2021. Overall, 65 students attended a lecture on lung auscultation featuring a simulator (Mr. Lung™). Among them, 35 (visualization group) received additional training wherein they were asked to mentally visualize lung sounds using a graphical visualized lung sounds diagram as an example. All students answered questions on their self-efficacy regarding lung auscultation before and after four weeks of CC. They also took a lung auscultation test with the simulator at the beginning of CC (pre-test) and on the last day of the third week (post-test) (maximum score: 25). We compared the answers in the questionnaire and the test scores between the visualization group and students who only attended the lecture (control group, n = 30). The Wilcoxon signed-rank test and analysis of covariance were used to compare the answers to the questionnaire about confidence in lung auscultation and the scores of the lung auscultation tests before and after the training.ResultsConfidence in auscultation of lung sounds significantly increased in both groups (five-point Likert scale, visualization group: pre-questionnaire median 1 [Interquartile range 1] to post-questionnaire 3 [1], p<0.001; control group: 2 [1] to 3 [1], p<0.001) and was significantly higher in the visualization than in the control group. Test scores increased in both groups (visualization group: pre-test 11 [2] to post-test 15 [4], p<0.001; control group: 11 [5] to 14 [4], p<0.001). However, there were no differences between both groups’ pre and post-tests scores (p = 0.623).ConclusionVisualizing lung sounds may increase medical students’ confidence in their lung auscultation skills; this may reduce their resistance to lung auscultation and encourage the repeated auscultation necessary to further improve their long-term auscultation abilities.

The De-Identified Health Data Market size is expected to reach a valuation of USD 17.23 billion in 2033 growing at a CAGR of 9.50%. The De-Identified Health Data market research report classifies market by share, trend, demand, forecast and based on segmentation.

This data was generated from a ten-country baseline evaluation of rural healthcare facilities. The evaluation was designed and led by The Water Institute at the University of North Carolina. The study conducted in Uganda in 2014 evaluated World Vision Uganda's WaSH program areas, as well as comparison areas, to assess water, sanitation, and hygiene practices in rural healthcare facilities.

This data was generated from a ten-country baseline evaluation of rural healthcare facilities. The evaluation was designed and led by The Water Institute at the University of North Carolina. The study conducted in Kenya in 2014 evaluated World Vision Kenya's WaSH program areas, as well as comparison areas, to assess water, sanitation, and hygiene practices in rural healthcare facilities. Note: the Kenya healthcare facility dataset is unweighted, as all of the health facilities on the original sampling frame were surveyed.

This data was generated from a ten-country baseline evaluation of rural healthcare facilities. The evaluation was designed and led by The Water Institute at the University of North Carolina. The study conducted in Malawi in 2014 evaluated World Vision Malawi's WaSH program areas, as well as comparison areas, to assess water, sanitation, and hygiene practices in rural healthcare facilities. Note: the Malawi healthcare facility dataset uses the same cluster-based weights as the household evaluation, rather than simple weighting based on strata, since healthcare facilities were selected for surveys based on proximity to randomly selected household cluster.

This data was generated from a ten-country baseline evaluation of rural schools. The evaluation was designed and led by The Water Institute at the University of North Carolina. The study conducted in Rwanda in 2014 evaluated World Vision Rwanda's WaSH program areas, as well as comparison areas, to assess water, sanitation, and hygiene practices in rural schools.

Truth in Public Comments (TiPC) Research Project

The Truth in Public Comments (TIPC) Project will analyze public comments submitted in response to the proposed repeal of Net Neutrality by the Federal Communications Commission (FCC). We aim to use data science, big data analysis, and on-the-ground journalism to separate authentic public submissions from fake comment campaigns. We will report our findings before the final December 14th FCC vote on Net Neutrality repeal. Our long-term vision is to expose the abuse of public comment systems, to fight PR hacking campaigns that manipulate public opinion, and to restore public confidence in the regulatory process.

Click here to read the Preliminary Report

Data

Data was downloaded from the FCC's API, a total of 65GB in the original form. Using a variety of technics, that data was culled down to a sample size of ~450,000 emails that were used in the survey. The data uploaded here is the deidentified dataset (emails addresses and names removed) of the emails used in that survey.

Acknowledgements

The preliminary report was drafted by Charles Belle, Gina Cooper, Jeffery Kao, and Sarah Rigdon.

Preliminary Report & Media

The Preliminary Report is accessible here`. Media coverage (for more background) can be found at The Parallax, Forbes, and TechDirt.

What's Next?

We are still in the early stages. Our first steps will be the publication of a Final Report. But our goal is for the TiPC Project to become a long-term fixture to better inform data-driven policymaking via the public comment process for Federal, State, and local agencies.

Ideas for developing this project are welcome, as are new areas to explore the data. The data will also be published on Tableau Public.

Photo Attribution

Photo by Christopher Burns on Unsplash

Recent developments include: In February 2024, Veradigm published its first Veradigm Insights Report: Cardiovascular Conditions in 2024, analyzing de-identified real-world data from 53 million cardiovascular patients. The report assesses the prevalence of cardiovascular disease (CVD) and related conditions across all U.S. states, with demographic breakdowns based on age, ethnicity, and sex. , In July 2021, Verana Health and Komodo Health partnered to integrate Komodo’s Healthcare Map into Verana’s de-identified EHR datasets, spanning over 325 million patient journeys. This collaboration aims to provide life sciences researchers with detailed insights into patient pathways, encompassing treatment histories, hospitalizations, and socioeconomic factors. The partnership is expected to enhance research efforts in ophthalmology, neurology, and urology by combining clinical outcomes with real-world patient data, supporting more informed treatment development. , In September 2024, ICON announced a collaboration with Intel to utilize de-identified data from its clinical research platform alongside Intel's AI technology. This partnership enhances patient recruitment and streamlines clinical trial processes by deriving insights from de-identified patient data. The initiative aims to advance precision medicine and improve efficiencies in drug development and outcomes by integrating ICON's clinical trial expertise with Intel's AI capabilities. .

This data package shows the information on hospital discharges at patient-level data with basic record details without showing protected health information (PHI) and was made not identifiable. The data is classified by Health Service Area and county.

This repository contains the dataset files and the code used for feature engineering in the paper titled "Open Data, Private Learners: A De-Identified Dataset for Learning Analytics Research" submitted to the Nature Scientific data journal.

General information

The dataset contains de-identified messaging meta-data from 78 WhatsApp and 7 Facebook data donations. The dataset was collected in an online study using the data donation platform Dona. After donating their messaging data, the study participants viewed visual summaries of their messaging data and evaluated this visual feedback. The responses to the evaluation questions and the sociodemographic data of the participants are also included in the dataset.

The data was collected from August 2022 to June 2024.

For more information on Dona, the associated publications and updates, please visit https://mbp-lab.github.io/dona-blog/.

File description

donation_table.csv - contains general information about the donations including

donation_id: donation identifier

donor_id: the ID of the donor to distinguish the messages sent by them from those sent by contacts

source: the messaging platform from which the data is donated (WhatsApp or Facebook)

external_id: ID used to connect messaging data with the survey data

messages_table.csv - contains the donated messages including

conversation_id: chat identifier

sender_id: sender identifier

datetime: time of the message, UNIX time for Facebook and device time for WhatsApp

word_count: word count of the messages achieved by splitting the text based on whitespace

donation_id: donation identifier (also listed in donation_table.csv)

messages_filtered_table.csv - same structure as messages_table.csv except that chats with no considerable interactions were removed. This was defined as chats where donor's word count contribution was less than 10% or more than 90%.

survey.xlsx → contains survey responses of the participants.

survey_table_coding.xlsx → contains the mapping between the column names in survey.xlsx and their meaning, including the original survey questions and response options. Different sheets of the Excel file detail the survey questions and responses in one of the study languages (English, German, Armenian).

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

PATRON is a human ethics approved program of research incorporating an enduring de-identified repository of Primary Care data facilitating research and knowledge generation. PATRON is a part of the 'Data for Decisions' initiative of the Department of General Practice, University of Melbourne. 'Data for Decisions' is a research initiative in partnership with general practices. It is an exciting undertaking that makes possible primary care research projects to increase knowledge and improve healthcare practices and policy. Principal Researcher: Jon EmeryData Custodian: Lena SanciData Steward: Douglas BoyleManager: Rachel CanawayMore information about Data for Decisions and utilising PATRON data is available from the Data for Decisions website.

Imaging Study De-Identification Services Market Outlook

According to our latest research, the global Imaging Study De-Identification Services market size reached USD 412.5 million in 2024, reflecting robust expansion fueled by rising data privacy demands. The market is projected to grow at a CAGR of 16.4% from 2025 to 2033, reaching an estimated USD 1,478.2 million by 2033. The key growth factor underpinning this trajectory is the increasing adoption of digital imaging in healthcare, alongside stringent regulatory frameworks such as HIPAA and GDPR that mandate the protection of patient information.

The primary driver for the Imaging Study De-Identification Services market is the exponential growth in medical imaging data, propelled by technological advancements in imaging modalities and the digital transformation of healthcare systems globally. As hospitals and diagnostic centers transition to electronic health records (EHRs) and Picture Archiving and Communication Systems (PACS), the volume of imaging studies containing sensitive patient information has surged. This growth necessitates efficient de-identification services to safeguard patient privacy and enable compliant data sharing. Additionally, the utilization of artificial intelligence and machine learning in radiology research has escalated the demand for large, anonymized datasets, further amplifying the need for reliable de-identification solutions.

Another significant growth factor is the increasing emphasis on clinical research and collaborative studies across institutions and borders. The ability to share imaging data without compromising patient confidentiality is crucial for multi-center trials, epidemiological studies, and the development of AI-driven diagnostic tools. Regulatory agencies worldwide are enforcing strict data privacy regulations, compelling healthcare organizations to adopt de-identification services. The integration of automated de-identification solutions, which offer scalability and accuracy, is rapidly gaining traction, enhancing the efficiency of data sharing and research processes. This trend is particularly prominent in regions with advanced healthcare infrastructure and a high prevalence of research activities.

The emergence of hybrid de-identification models, which combine the strengths of automated and manual approaches, is also contributing to market expansion. These solutions address the limitations of fully automated systems by incorporating human oversight for complex cases, ensuring both compliance and data integrity. As healthcare providers and research organizations increasingly recognize the value of de-identified imaging data for secondary uses such as AI training, population health management, and regulatory submissions, the demand for tailored de-identification services continues to rise. This shift is further supported by the growing awareness of data breaches and the associated financial and reputational risks.

From a regional perspective, North America remains the dominant market for Imaging Study De-Identification Services, driven by a mature healthcare ecosystem, stringent regulatory requirements, and early adoption of digital health technologies. Europe follows closely, benefiting from robust data protection laws and active research collaborations. The Asia Pacific region is witnessing the fastest growth, fueled by expanding healthcare infrastructure, rising investments in medical research, and increasing awareness of data privacy. Latin America and the Middle East & Africa are also experiencing gradual adoption, supported by government initiatives and international partnerships aimed at improving healthcare data management and compliance.

Service Type Analysis

The Service Type segment within the Imaging Study De-Identification Services market is categorized into Automated De-Identification, Manual De-Identification, and Hybrid De-Identification. Automated De-Identification services have emerged as the leading segment, owing to their ability to process vast volumes of imaging data efficiently and accurately. These solutions leverage advanced algorithms and artificial intelligence to identify and redact patient identifiers from imaging studies, significantly reducing the risk of human error and ensuring compliance with regulatory standards. The scalability of automated systems makes them particularly attractive for large hospitals, research networks, and organizations handling multi-center studies

Title: Minimal Data Set for the Reproduction of Findings in "Elayan et al., Cohort Profile: The ENTWINE iCohort Study, a Multinational Longitudinal Web-Based Study of Informal Care". Study Summary: The data sets provided herein are derived from the ENTWINE iCohort Study, a multinational web-based cohort study employing an intensive longitudinal design. The study integrates a two-wave panel survey (baseline and 6-month follow-up) with optional weekly diary assessments. The cohort comprises caregivers and care recipients from nine countries: the United Kingdom, the Netherlands, Italy, Sweden, Israel, Germany, Greece, Poland, and Ireland. The study aimed to examine the influence of personal, psychological, social, economic, and geographic factors on caregiving experiences. Participants were eligible if they met the following criteria: 1) residency in a participating country; 2) capability to respond to surveys in English, Swedish, German, Dutch, Italian, Greek, Hebrew, or Polish; 3) access to the internet and ability to use it; 4) at least 18 years of age; 5) self-declared cognitive and physical capacity to complete the surveys; 6) either providing care to an adult (aged ≥ 18 years) with a chronic health condition, disability, or other care need, or receiving care from an adult due to similar conditions. The detailed methodology and results of the study can be found in the associated manuscript. For the complete survey questionnaires, please refer to: Morrison V, Zarzycki M, Vilchinsky N, Sanderman R, Lamura G, Fisher O, et al. A Multinational Longitudinal Study Incorporating Intensive Methods to Examine Caregiver Experiences in the Context of Chronic Health Conditions: Protocol of the ENTWINE-iCohort. Int J Environ Res Public Health. 2022;19. doi: 10.3390/ijerph19020821 Data files: The repository contains the following data files: "cg_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Caregiver Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cg_minimal_dataset_codebook". "cr_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Care Recipient Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cr_minimal_dataset_codebook".

The 2017 National Financial Well-Being in America Survey, conducted for the CFPB Offices of Financial Education and Financial Protection for Older Americans, was an online survey conducted to measure the financial well-being of adults in the United States. These data were created as a foundation for internal and external research into financial well-being and are relevant to work being done by researchers in the Office of Research who have access to the (deidentified) data.

De-identified qualitative data from survey

This study investigates the complex interrelationships between peer support, mental distress, self-care abilities, health perceptions, and daily life activities among cancer patients and survivors while considering the evolving nature of these experiences over time. A cross-sectional survey design is employed, utilizing de-identified data from the National Cancer Institute’s 2022 nationally representative dataset, which comprises responses from 1234 participants, including 134 newly diagnosed patients undergoing cancer treatment. Partial least squares structural equation modeling is employed for data analysis. The results reveal that peer support significantly reduces mental distress and positively influences the perception of self-care abilities and health perceptions among cancer patients and survivors. Additionally, the study finds that mental distress negatively affects daily life activities and self-care abilities. This means that when cancer patients and survivors experience high levels of mental distress, they may struggle with everyday tasks and find it challenging to care for themselves effectively. The research also shows that mental distress tends to decrease as time passes since diagnosis and health perceptions improve, highlighting the resilience of cancer patients and survivors over time. Furthermore, the study uncovers significant moderating effects of age, education, and income on the relationships between daily life activity difficulties, perception of self-care ability, and perception of health. In conclusion, this research provides a comprehensive understanding of the intricate associations between the variables of interest among cancer patients and survivors. The findings underscore the importance of peer support and targeted interventions for promoting well-being, resilience, and quality of life in this population, offering valuable insights for healthcare providers, researchers, and policymakers. Identifying moderating effects further emphasizes the need to consider individual differences when designing and implementing support systems and interventions tailored to the unique needs of cancer patients and survivors.