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

The purest type of electronic clinical data which is obtained at the point of care at a medical facility, hospital, clinic or practice. Often referred to as the electronic medical record (EMR), the EMR is generally not available to outside researchers. The data collected includes administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, patient insurance, etc.

Individual organizations such as hospitals or health systems may provide access to internal staff. Larger collaborations, such as the NIH Collaboratory Distributed Research Network provides mediated or collaborative access to clinical data repositories by eligible researchers. Additionally, the UW De-identified Clinical Data Repository (DCDR) and the Stanford Center for Clinical Informatics allow for initial cohort identification.

About Dataset:

333 scholarly articles cite this dataset.

Unique identifier: DOI

Dataset updated: 2023

Authors: Haoyang Mi

In this dataset, we have two dataset:

1- Clinical Data_Discovery_Cohort: Name of columns: Patient ID Specimen date Dead or Alive Date of Death Date of last Follow Sex Race Stage Event Time

2- Clinical_Data_Validation_Cohort Name of columns: Patient ID Survival time (days) Event Tumor size Grade Stage Age Sex Cigarette Pack per year Type Adjuvant Batch EGFR KRAS

Feel free to put your thought and analysis in a notebook for this datasets. And you can create some interesting and valuable ML projects for this case. Thanks for your attention.

Clinical Data De-Identification Pipelines Market Outlook

According to our latest research, the global clinical data de-identification pipelines market size reached USD 680 million in 2024, with a robust growth trajectory driven by stringent data privacy regulations and the increasing adoption of digital health records. The market is expected to expand at a CAGR of 15.6% from 2025 to 2033, with the forecasted market size projected to reach USD 2.1 billion by 2033. This growth is primarily attributed to the rising emphasis on patient data security, the proliferation of healthcare data, and the need to facilitate compliant data sharing for research and analytics.

The rapid digitalization of healthcare systems worldwide has resulted in an unprecedented surge in electronic health records (EHRs), clinical trial data, and patient registries. As healthcare organizations increasingly leverage these vast datasets for research, analytics, and population health management, the risk of data breaches and unauthorized disclosures has escalated. This scenario has intensified the demand for robust clinical data de-identification pipelines, which ensure that personally identifiable information (PII) is systematically removed or masked before data is shared or analyzed. Regulatory frameworks such as HIPAA in the United States, GDPR in Europe, and similar mandates in other regions have made de-identification not just a best practice but a legal requirement, further propelling the adoption of advanced software and services in this market.

Another significant growth driver for the clinical data de-identification pipelines market is the expanding landscape of clinical research and precision medicine. Pharmaceutical and biotechnology companies, as well as academic and research institutes, are increasingly reliant on large-scale, multi-source datasets to accelerate drug discovery, understand disease mechanisms, and personalize treatment protocols. However, these research initiatives necessitate stringent privacy safeguards to maintain patient confidentiality while enabling meaningful data analysis. The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification pipelines has enhanced the accuracy and efficiency of data anonymization processes, thereby supporting the dual objectives of compliance and research innovation.

Strategic partnerships and collaborations among healthcare providers, technology vendors, and research organizations have also played a pivotal role in shaping the clinical data de-identification pipelines market. Leading technology firms are investing in the development of scalable, interoperable solutions that can seamlessly integrate with existing healthcare IT infrastructure. Moreover, the emergence of cloud-based deployment models has made de-identification solutions more accessible to smaller healthcare entities and research organizations, democratizing access to advanced privacy tools. This trend is particularly pronounced in regions with rapidly evolving healthcare ecosystems, such as Asia Pacific and Latin America, where digital health initiatives are gaining momentum.

From a regional perspective, North America continues to dominate the clinical data de-identification pipelines market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of a mature healthcare IT infrastructure, strong regulatory oversight, and significant investments in clinical research. Europe follows closely, benefiting from stringent data protection laws and a vibrant research community. Meanwhile, Asia Pacific is emerging as the fastest-growing market, fueled by large-scale government initiatives to digitize healthcare, rising awareness about patient privacy, and the increasing participation of regional players in global clinical research networks. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare modernization efforts gather pace.

Component Analysis

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The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.

Objective(s): Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. We provide an instructional video to describe a standardized de-identification framework that can be adapted and refined based on specific context and risks. Data Description: Training video, presentation slides. Related Resources: The data de-identification algorithm, dataset, and data dictionary that correspond with this training video are available through the Smart Triage sub-Dataverse. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Clinical Data De-Identification Pipelines Market Outlook

According to our latest research, the global clinical data de-identification pipelines market size reached USD 425.8 million in 2024. The market is experiencing robust momentum, with a recorded CAGR of 17.9% driven by the increasing adoption of advanced data privacy solutions across the healthcare sector. By 2033, the market is projected to achieve a value of USD 1,541.3 million, underscoring the escalating need for secure data handling and compliance with stringent regulatory frameworks. The primary growth factor for this sector is the rising volume of healthcare data and the critical necessity to protect patient privacy while enabling data-driven research and innovation.

The surge in healthcare digitization, coupled with the proliferation of electronic health records (EHRs), has significantly contributed to the growth of the clinical data de-identification pipelines market. Healthcare organizations are increasingly leveraging digital platforms to store, share, and analyze sensitive patient data, which in turn amplifies the risk of data breaches and unauthorized access. This scenario has heightened the demand for robust de-identification solutions, ensuring that personal health information (PHI) is rendered anonymous before being used for research, analytics, or sharing with third parties. Regulatory mandates such as HIPAA in the United States and GDPR in Europe further reinforce the need for effective data de-identification, driving both innovation and adoption in this market.

Another critical growth driver is the expanding landscape of clinical research and real-world evidence (RWE) generation. Pharmaceutical and biotechnology companies, as well as academic research institutions, rely heavily on access to vast amounts of patient data to accelerate drug development, conduct population health studies, and improve clinical outcomes. However, the sensitive nature of this data necessitates sophisticated de-identification pipelines that can efficiently strip personally identifiable information (PII) while preserving the integrity and utility of the dataset. This balance between data utility and privacy protection is fueling investments in next-generation de-identification software and services, further propelling market expansion.

The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification pipelines is also playing a pivotal role in market growth. Advanced algorithms enable more accurate and automated identification and removal of sensitive information from unstructured clinical narratives, images, and structured datasets. This technological evolution not only enhances the scalability and reliability of de-identification processes but also addresses the growing complexity of healthcare data formats. As a result, organizations can more confidently share anonymized datasets for collaborative research, secondary analytics, and public health monitoring, all while maintaining compliance with global privacy standards.

From a regional perspective, North America continues to dominate the clinical data de-identification pipelines market, accounting for the largest share in 2024. The region’s leadership is attributed to a robust healthcare infrastructure, widespread adoption of health IT solutions, and stringent regulatory requirements surrounding data privacy. Europe follows closely, propelled by comprehensive data protection laws and strong investments in healthcare digitalization. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by burgeoning healthcare IT adoption, increasing clinical research activities, and rising awareness about patient data privacy. Latin America and the Middle East & Africa are emerging as promising markets, supported by gradual improvements in healthcare technology and regulatory frameworks.

Component Analysis

The clinical data de-identification pipelines market by component is segmented into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses a wide array of solutions designed to automate the identification and removal of sensitive data from clinical records, including structured databases, unstructured clinical notes, and even medical images. These software platforms are increasingly leveraging AI and natural language processing (NLP) to enhance accuracy, adaptability, and speed, making them indispensabl

Objective: To investigate clinical trialists’ opinions and experiences of sharing of clinical trial data with investigators who are not directly collaborating with the research team. Design and setting: Cross sectional, web based survey. Participants: Clinical trialists who were corresponding authors of clinical trials published in 2010 or 2011 in one of six general medical journals with the highest impact factor in 2011. Main outcome measures: Support for and prevalence of data sharing through data repositories and in response to individual requests, concerns with data sharing through repositories, and reasons for granting or denying requests. Results: Of 683 potential respondents, 317 completed the survey (response rate 46%). In principle, 236 (74%) thought that sharing de-identified data through data repositories should be required, and 229 (72%) thought that investigators should be required to share de-identified data in response to individual requests. In practice, only 56 (18%) indicated that they were required by the trial funder to deposit the trial data in a repository; of these 32 (57%) had done so. In all, 149 respondents (47%) had received an individual request to share their clinical trial data; of these, 115 (77%) had granted and 56 (38%) had denied at least one request. Respondents’ most common concerns about data sharing were related to appropriate data use, investigator or funder interests, and protection of research subjects. Conclusions: We found strong support for sharing clinical trial data among corresponding authors of recently published trials in high impact general medical journals who responded to our survey, including a willingness to share data, although several practical concerns were identified.

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. .

De-Identification Solutions for Medical Images Market Outlook

According to our latest research, the global De-Identification Solutions for Medical Images market size was valued at USD 425.8 million in 2024, with a robust growth trajectory projected at a CAGR of 13.6% from 2025 to 2033. By the end of 2033, the market is anticipated to reach USD 1,314.7 million. This remarkable expansion is primarily fueled by the increasing adoption of advanced imaging technologies in healthcare, stringent regulatory mandates for patient data privacy, and the rising prevalence of medical imaging data in clinical research and diagnostics. As per our latest research, the market is witnessing a dynamic shift towards cloud-based and AI-powered de-identification solutions, enabling healthcare organizations to meet compliance requirements while fostering innovation in medical imaging analytics.

One of the foremost growth drivers for the De-Identification Solutions for Medical Images market is the exponential rise in digital healthcare data, particularly from radiology, pathology, and cardiology departments. The proliferation of high-resolution imaging modalities such as MRI, CT, and PET scans has resulted in massive data volumes that require secure handling and anonymization. Healthcare providers and research organizations are increasingly recognizing the importance of de-identification to protect patient privacy, comply with regulations such as HIPAA, GDPR, and local data protection laws, and enable the secondary use of medical images for research, AI training, and collaborative studies. This trend is further amplified by the growing integration of electronic health records (EHRs) with imaging systems, necessitating robust and scalable de-identification solutions to mitigate the risk of data breaches and unauthorized disclosures.

Another significant factor propelling market growth is the rapid advancement of artificial intelligence and machine learning algorithms in the field of medical imaging. AI-driven de-identification tools are now capable of automating the anonymization process with high accuracy, reducing manual intervention, and ensuring consistent compliance with regulatory standards. These solutions not only streamline workflow efficiency but also enhance data utility for research and innovation. The increasing adoption of cloud-based platforms is further supporting the deployment of scalable de-identification services, enabling healthcare organizations to process and share large datasets seamlessly while maintaining stringent data privacy controls. This technological evolution is also facilitating the participation of smaller healthcare facilities and research institutes in global data-sharing initiatives, thereby broadening the market base.

The surge in clinical trials, multi-center research collaborations, and the emergence of precision medicine are also contributing to the robust demand for de-identification solutions for medical images. Pharmaceutical companies, contract research organizations (CROs), and academic institutes are increasingly leveraging de-identified imaging datasets to accelerate drug discovery, validate diagnostic algorithms, and conduct population health studies. The emphasis on interoperability and data standardization across healthcare systems is driving the adoption of sophisticated de-identification tools that can support multiple imaging formats and workflows. Furthermore, the COVID-19 pandemic has underscored the importance of secure data sharing for public health research, further catalyzing investments in advanced de-identification technologies.

From a regional perspective, North America continues to dominate the De-Identification Solutions for Medical Images market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of a well-established healthcare infrastructure, stringent regulatory oversight, and a high concentration of leading market players are key factors supporting market leadership in North America. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization of healthcare, increasing investments in medical imaging, and rising awareness of data privacy. Europe remains a significant market owing to robust data protection regulations and a strong focus on research and innovation. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by healthcare modernization initiatives and growing participation in global health research networks.

Data De-Identification Platform Market Outlook

According to our latest research, the global Data De-Identification Platform market size reached USD 714.2 million in 2024, driven by the escalating need for data privacy and regulatory compliance across industries. The market is experiencing robust expansion, registering a CAGR of 18.7% from 2025 to 2033. By 2033, the market is forecasted to attain USD 3,276.9 million, reflecting the surging adoption of advanced data privacy solutions and the increasing volume of sensitive data handled by organizations worldwide. This remarkable growth trajectory is primarily fueled by stricter data protection laws, rising data breach incidents, and the imperative for organizations to leverage data analytics without compromising personal information.

The primary growth factor for the Data De-Identification Platform market is the intensification of global data privacy regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other region-specific mandates. Organizations are increasingly mandated to ensure that personally identifiable information (PII) is adequately protected or anonymized before use in analytics, research, or sharing with third parties. This regulatory landscape compels enterprises to integrate sophisticated de-identification platforms into their data management workflows. Furthermore, as digital transformation accelerates across sectors, the volume and variety of data being collected and processed have grown exponentially, creating new challenges and opportunities for data privacy management. The need to balance data utility with privacy has made automated, scalable de-identification solutions a top priority for businesses aiming to remain compliant and competitive.

Another significant driver is the rising frequency and sophistication of data breaches and cyberattacks, which have heightened organizational awareness regarding the risks associated with storing and processing sensitive information. As enterprises increasingly migrate to cloud environments and adopt big data analytics, the attack surface expands, making robust data de-identification tools essential for mitigating exposure. These platforms enable organizations to anonymize or pseudonymize data, reducing the risk of re-identification even in the event of a breach. The growing adoption of artificial intelligence (AI) and machine learning (ML) further necessitates de-identification, as these technologies often require access to large datasets that must be stripped of personal identifiers to ensure ethical and legal compliance. This confluence of factors is propelling the demand for advanced, user-friendly, and highly configurable de-identification platforms.

Moreover, the proliferation of data-driven business models in sectors such as healthcare, BFSI, government, retail, and IT & telecom is amplifying the need for secure data sharing and collaboration. In healthcare, for instance, the use of patient data for research, clinical trials, and population health management demands rigorous de-identification to protect patient privacy while enabling valuable insights. Similarly, financial institutions and government agencies are leveraging data to enhance service delivery and operational efficiency, necessitating robust privacy controls. The increasing recognition of data as a strategic asset, coupled with the imperative to safeguard individual privacy, is fostering a culture of proactive data governance and driving investments in de-identification technologies.

The integration of Data De-identification AI is revolutionizing the way organizations handle sensitive information. By leveraging AI technologies, businesses can automate the process of identifying and anonymizing personal data, ensuring compliance with stringent privacy regulations. This approach not only enhances data security but also allows for more efficient data processing and analysis. AI-driven de-identification tools can dynamically adapt to new data patterns, providing organizations with a robust mechanism to protect personal information while still extracting valuable insights. As AI continues to evolve, its role in data de-identification is expected to become even more pivotal, driving innovation and setting new standards in data privacy management.

From a regional perspective, North America currently dominates the Data De-Identification P

Data are derived from the Resistance Testing for Management of HIV Virologic Failure in Sub-Saharan Africa (REVAMP) clinical trial. The de-identified dataset includes include randomization allocation, baseline participant characteristics and primary and secondary outcomes.

Website which allows data from completed clinical trials to be distributed to investigators and public. Researchers can download de-identified data from completed NIDA clinical trial studies to conduct analyses that improve quality of drug abuse treatment. Incorporates data from Division of Therapeutics and Medical Consequences and Center for Clinical Trials Network.

Pathology Image De-Identification Market Outlook

According to our latest research, the global market size for Pathology Image De-Identification reached USD 248.7 million in 2024, and is projected to grow at a robust CAGR of 14.2% during the forecast period. By 2033, the market is anticipated to attain a value of USD 695.1 million. This remarkable growth is fueled by the surging adoption of digital pathology, stringent data privacy regulations, and the increasing integration of artificial intelligence in healthcare research and diagnostics.

One of the primary growth drivers for the Pathology Image De-Identification market is the escalating digitization of pathology workflows globally. As healthcare institutions transition from traditional glass slides to high-resolution digital images, the volume of sensitive patient data being generated and stored electronically has surged. This digital transformation is not only enhancing diagnostic accuracy and collaboration but is also creating significant challenges around maintaining patient confidentiality. The need to de-identify pathology images before sharing them for research, education, or telemedicine is now more critical than ever, especially in light of tightening data privacy laws such as HIPAA and GDPR. These regulations mandate the removal of all personally identifiable information from medical images, driving demand for advanced de-identification solutions that can efficiently anonymize vast datasets without compromising image quality or diagnostic value.

Another significant factor propelling the market is the rapid advancement and adoption of artificial intelligence and machine learning technologies in pathology. AI-driven image analysis tools require access to large, diverse, and high-quality datasets to train and validate their algorithms. However, sharing these datasets across institutions or with third-party developers raises substantial privacy concerns. Pathology image de-identification solutions enable healthcare providers and researchers to comply with regulatory requirements while facilitating data sharing and collaborative innovation. The integration of automated de-identification tools within digital pathology platforms not only streamlines compliance but also reduces manual workload, minimizes human error, and accelerates the pace of research and clinical trials, further boosting market expansion.

Moreover, the growing emphasis on medical education and international clinical research collaborations is fostering the need for pathology image de-identification. Academic institutions and research organizations are increasingly leveraging digital pathology images for training, education, and multi-center studies. To ensure the ethical use of patient data and to meet institutional review board (IRB) standards, these organizations are adopting robust de-identification solutions. The surge in global clinical trials, particularly in oncology and rare diseases, has amplified the need for secure image sharing across borders. These trends, combined with the rising investments in healthcare IT infrastructure and the proliferation of cloud-based solutions, are creating a fertile environment for the growth of the pathology image de-identification market.

From a regional perspective, North America is currently the largest market for pathology image de-identification, accounting for a substantial share of global revenues in 2024. This dominance is attributed to the region’s advanced healthcare infrastructure, stringent enforcement of data privacy regulations, and early adoption of digital pathology systems. Europe follows closely, driven by strong regulatory frameworks and increasing investments in digital healthcare. The Asia Pacific region is emerging as the fastest-growing market, fueled by rapid healthcare digitization, government initiatives to improve healthcare data security, and expanding research activities. Latin America and the Middle East & Africa are also witnessing steady growth, supported by rising awareness of data privacy and gradual adoption of digital pathology technologies.

Component Analysis

The Component segment of the Pathology Image De-Identification market is primarily divided into Software and Services. Software solutions are at the forefront, accounting for the largest market share in 2024. These solutions encompass a wide range of automated tools and platforms designed

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

De-Identification Software for Healthcare Data Market Outlook

According to our latest research, the global De-Identification Software for Healthcare Data market size reached USD 410 million in 2024, reflecting a robust surge in demand for data privacy and compliance solutions. The market is projected to expand at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 1,444 million by 2033. This significant growth is primarily driven by escalating regulatory requirements, increasing incidences of data breaches, and the proliferation of digital health data across healthcare systems worldwide.

One of the primary growth factors for the De-Identification Software for Healthcare Data market is the tightening of data privacy regulations such as HIPAA in the United States, GDPR in Europe, and similar frameworks in other regions. These legislations mandate stringent procedures for handling personally identifiable information (PII) and protected health information (PHI), compelling healthcare organizations to adopt advanced de-identification solutions. As healthcare providers, payers, and research entities increasingly digitize patient records, the risk of data exposure intensifies, making robust de-identification tools indispensable for compliance and risk mitigation. Furthermore, the growing awareness among healthcare professionals and administrators regarding the consequences of non-compliance, including hefty fines and reputational damage, is accelerating the adoption of these solutions.

Another critical driver is the exponential growth of healthcare data generated from electronic health records (EHRs), wearable devices, telemedicine platforms, and genomic studies. The sheer volume and complexity of this data necessitate sophisticated de-identification software capable of processing both structured and unstructured information. The demand is further amplified by the surge in collaborative research, clinical trials, and data sharing initiatives, which require the anonymization of patient data to protect privacy while enabling valuable insights. As artificial intelligence and machine learning applications become more prevalent in healthcare, the need for high-quality, de-identified datasets is also rising, fostering further market expansion.

Additionally, the rise in cyber threats and high-profile data breaches within the healthcare sector have underscored the urgent need for comprehensive data protection strategies. Healthcare organizations are increasingly prioritizing investments in de-identification software to safeguard sensitive patient information from unauthorized access and malicious actors. This trend is supported by the growing involvement of insurance companies and research organizations, which handle vast amounts of patient data and are equally vulnerable to breaches. The convergence of these factors is expected to sustain the momentum of the De-Identification Software for Healthcare Data market over the forecast period.

From a regional perspective, North America continues to dominate the market, accounting for the largest share in 2024, driven by robust healthcare infrastructure, early adoption of advanced technologies, and strict regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitization of healthcare systems, increasing investments in health IT, and rising awareness of data privacy. Europe, with its comprehensive data protection laws, also represents a significant market, while Latin America and the Middle East & Africa are gradually catching up as healthcare modernization accelerates in these regions. The global landscape is thus characterized by both mature and emerging markets, each contributing to the overall growth trajectory.

Data Loss Prevention in Healthcare is becoming increasingly crucial as the industry continues to digitize and expand its data management capabilities. With the rise of electronic health records, telemedicine, and wearable health devices, the volume of sensitive patient information being handled by healthcare organizations has skyrocketed. This surge in data has made the sector a prime target for cyberattacks, emphasizing the need for robust data loss prevention strategies. Healthcare providers are now investing in advanced technologies and protocols to protect patient data from unauthorized access and bre

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

De-Identification Software for Healthcare Data Market Outlook

According to our latest research, the global market size for De-Identification Software for Healthcare Data in 2024 stands at USD 468 million, with a robust compound annual growth rate (CAGR) of 20.1% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach an impressive USD 2,633 million, reflecting substantial momentum driven by increasing regulatory demands and the proliferation of digital health records. As per our latest research, the primary growth driver for this sector is the intensifying focus on patient privacy and security in healthcare data management, propelled by global data protection regulations and the expanding adoption of electronic health records (EHRs).

The growth trajectory of the De-Identification Software for Healthcare Data Market is significantly influenced by the evolving regulatory landscape governing patient information privacy. Stringent regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and similar frameworks globally are compelling healthcare organizations to invest in advanced de-identification solutions. These regulations mandate the removal or masking of personally identifiable information (PII) from healthcare datasets before sharing, research, or analytics, to safeguard patient privacy. As healthcare data becomes increasingly digitized, the risk of data breaches and unauthorized access grows, making robust de-identification software not just a compliance tool but a critical component of risk management strategies for healthcare providers, payers, and researchers.

Another significant growth factor is the rising volume and complexity of healthcare data generated through diverse sources such as EHRs, wearables, genomic sequencing, and telemedicine platforms. The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification software has enabled more sophisticated and automated data anonymization processes, reducing manual intervention and improving accuracy. This technological advancement allows for the secure sharing of large-scale clinical and genomic datasets, which is crucial for collaborative research, population health analytics, and the development of personalized medicine. As the demand for interoperability and data exchange across healthcare ecosystems intensifies, scalable and automated de-identification solutions are becoming indispensable.

The market is further propelled by the expanding use of healthcare data for secondary purposes such as clinical research, public health monitoring, and healthcare analytics. Pharmaceutical companies, research organizations, and health insurers increasingly require access to de-identified datasets to derive insights, improve patient outcomes, and streamline operations without compromising privacy. The growing trend of data monetization and the emergence of health data marketplaces are also fueling the adoption of de-identification software, as organizations seek to unlock the value of their data assets while adhering to ethical and legal standards. These factors collectively create a fertile environment for sustained market growth over the forecast period.

Regionally, North America continues to dominate the De-Identification Software for Healthcare Data Market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate of EHRs, advanced healthcare IT infrastructure, and the presence of leading market players in the United States and Canada underpin this leadership. Europe’s market is bolstered by GDPR compliance requirements and growing investments in digital health innovation, while Asia Pacific is witnessing rapid growth due to increasing healthcare digitization and a rising awareness of data privacy. Latin America and the Middle East & Africa are gradually emerging as promising markets, driven by healthcare modernization initiatives and evolving regulatory frameworks.

Component Analysis

The Component segment of the De-Identification Software for Healthcare Data Market is broadly categorized into Software and Services. The software segment holds the lion’s share of the market, primarily due to the growing need for automated

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Veterinary Data De-Identification Services Market Outlook

According to our latest research, the veterinary data de-identification services market size reached USD 145.8 million in 2024, reflecting a growing emphasis on data privacy and regulatory compliance in the veterinary sector. The market is poised for robust expansion, projected to attain USD 393.2 million by 2033, propelled by a CAGR of 11.7% from 2025 to 2033. This growth is primarily fueled by the increasing digitization of veterinary records, rising concerns over data security, and the integration of advanced technologies in veterinary healthcare management.

The surge in demand for veterinary data de-identification services is largely attributed to the exponential growth of digital data in the veterinary industry. As veterinary practices, research institutes, and pharmaceutical companies increasingly adopt electronic health records and data-driven approaches, the volume of sensitive animal health data has soared. This growth has necessitated robust data protection strategies to safeguard confidential information, especially as regulations similar to human healthcare data privacy, such as GDPR and HIPAA-like standards, are being extended to veterinary data. The need to anonymize and pseudonymize animal health data for research, clinical trials, and collaborative studies without compromising privacy is a significant market driver, pushing organizations to invest in specialized de-identification services.

Another key growth factor is the rising collaboration between veterinary clinics, research institutions, and pharmaceutical companies. These collaborations often require the sharing of large datasets to advance veterinary science, drug development, and clinical research. However, the sharing of identifiable data poses ethical and legal risks, elevating the importance of de-identification solutions that ensure compliance and foster trust among stakeholders. The increasing prevalence of zoonotic diseases and the global focus on One Health initiatives have further highlighted the need for secure and compliant data sharing, driving the uptake of de-identification services across the veterinary ecosystem.

Technological advancements are also reshaping the veterinary data de-identification services market. The integration of artificial intelligence, machine learning, and blockchain technologies has enhanced the efficacy and reliability of de-identification processes. These innovations enable more precise anonymization and encryption of veterinary data, reducing the risk of re-identification while maintaining data utility for research and analytics. Additionally, the growing awareness among veterinary professionals about the risks of data breaches and the potential legal consequences has led to increased investments in comprehensive data de-identification and security solutions, further propelling market growth.

From a regional perspective, North America continues to dominate the veterinary data de-identification services market, accounting for the largest revenue share in 2024. The region’s leadership is supported by stringent data privacy regulations, a high concentration of veterinary research institutions, and rapid adoption of digital health technologies. Europe follows closely, driven by strong regulatory frameworks and increasing investments in veterinary research. Asia Pacific is emerging as a high-growth region, with expanding veterinary healthcare infrastructure, rising pet ownership, and growing awareness of data privacy. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as digital transformation initiatives gain traction in these regions.

Service Type Analysis

The service type segment in the veterinary data de-identification services market encompasses anonymization, pseudonymization, data masking, encryption, and other specialized services. Anonymization remains the most widely adopted service, as it irreversibly removes personally identifiable information from veterinary datasets, ensuring compliance with stringent data privacy regulations. Veterinary clinics and research institutions favor anonymization for sharing data in multi-institutional studies and public health surveillance, as it allows for the safe aggregation and analysis of large datasets without risking the exposure of sensitive information. The growing complexity of veterinary data, including genomic and behavioral da

Supplementary Materials for: Andrés M. Arias-Lorza, James R. Costello, Sunil R. Hingorani, Daniel D. Von Hoff, Ronald L. Korn, and Natarajan Raghunand. Magnetic resonance imaging of tumor response to stroma-modifying pegvorhyaluronidase alpha (PEGPH20) therapy in early-phase clinical trials. Scientific Reports 14, 11570 (2024).

https://doi.org/10.1038/s41598-024-62470-9

Here we are publicly sharing deidentified HTML reports for each subject in the above study. The clinical trials in question are, HALO-109-101 (NCT00834704), HALO-109-102 (NCT01170897), and HALO-109-201 (NCT01453153).

Please click “Download” to obtain a copy of all these data on your computer. Then, please navigate to each subject’s folder and open "Report.html" in a browser to view images and movies of all slices of the raw and processed images and parameter maps from all scan dates of that subject, including:

DW-MRI ORIGINAL IMAGES REGISTERED IMAGES LOCAL REGISTERED IMAGES ADC Maps Global Registration Across Scan Dates Local Registration Across Scan Dates

T1-weighted pre-contrast MRI ORIGINAL IMAGES REGISTERED IMAGES LOCAL REGISTERED IMAGES T1 Maps

DCE-MRI ORIGINAL IMAGES REGISTERED IMAGES LOCAL REGISTERED IMAGES Contrast Concentration maps, Tofts Model Parameter Maps Global Registration Across Scan Dates Local Registration Across Scan Dates

T1w/DCE/DWI Co-Registration: Only performed in the 6 subjects in HALO-109-102 with complete DW-MRI and DCE-MRI datasets at multiple scan dates that were acquired consistently in the same view.

Note: De-identified raw DICOM images corresponding to these processed results are available upon request from the corresponding author, subject to appropriate research and data-sharing agreements with Moffitt Cancer Center and Halozyme Therapeutics.