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 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.

This file contains de-identified and anonymized healthcare facility-level raw primary data used in the analysis.

This study was approved by the IRB committee at East Carolina University (UMCIRB 24-001121). Patient consent was not required as all data will be deidentified and is for secondary use in nature.

The data set is deidentified to comply with the Health Insurance Portability and Accountability Act (HIPAA) and the National Institute of Standards and Technology (NIST) Safe Harbor regulations.
• All identifying patient information has been removed from the data set.
• Any patient, encounter, or culture order identification numbers have been anonymized. The anon_id was created by giving each patient an identification number, a random pair of letters, accompanied by a random set of numbers. The pat_enc_csn_id_coded and order_proc_id_coded values were created by giving each encounter number or culture order a prefix (3 for pat_enc_csn_id_coded and 4 for order_proc_id_coded), accompanied by a serialized value based on the initial randomization of the accompanying anon_id. This allows for ...

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

This repository contains the complete replication package for the manuscript “Digital Nomads as Unintentional Brand Intermediaries". It includes de-identified data, analysis code, study instruments, and supplementary sources to reproduce all tables and figures.

Contents

• Study 1 (Survey): cleaned, de-identified dataset; codebook; SEM/CFA scripts and output; item wordings and scale anchors.

• Study 2 (Experiment): cleaned, de-identified dataset; pre-registration (if applicable); analysis scripts for manipulation checks, ANCOVA, and mediation; instrument and manipulation-check items.

• Study 3 (Interviews): anonymized excerpted quotations used in the paper; theme/codebook; audit trail summary (sampling, saturation notes).

• Supplementary materials: figure/table source files; robustness checks (controls, multigroup, invariance); README with step-by-step replication instructions and software versions.

Restrictions
Full video stimuli are not redistributed due to third-party rights. We provide transcripts, frame stills, detailed metadata (links, durations, posting dates), and procedures to reconstruct the stimuli set. Researchers may request time-limited access to the files for verification under a non-distribution agreement.

Anonymization & Compliance
All datasets are de-identified and stored per GDPR and institutional ethics approval. Any indirect identifiers were removed or binned.

Licensing & Citation
Data and materials: CC BY 4.0. Please cite this repository using its DOI (https://doi.org/10.5281/zenodo.17199056) when reusing these materials.

Reproducibility
A replication script (R/Python) reproduces the tables and figures from the raw de-identified data; session info and package versions are provided.

Imaging Study De-Identification Gateways Market Outlook

According to our latest research, the global Imaging Study De-Identification Gateways market size reached USD 612.4 million in 2024, and is expected to grow at a robust CAGR of 16.7% from 2025 to 2033. By the end of the forecast period, the market is projected to reach USD 2,134.7 million. This remarkable growth trajectory is driven by the heightened demand for data privacy compliance and the rapid adoption of digital health technologies worldwide, as regulatory frameworks such as HIPAA and GDPR increasingly mandate strict de-identification of medical imaging data.

The primary growth factor fueling the Imaging Study De-Identification Gateways market is the intensifying focus on patient privacy and data security. With the proliferation of digital health records and the exponential rise in imaging studies, healthcare providers are under mounting pressure to ensure that sensitive patient information is adequately protected. De-identification gateways have become indispensable for organizations aiming to comply with complex regulatory requirements. These solutions systematically remove or obfuscate personally identifiable information (PII) from imaging data, thereby enabling secure data sharing for clinical collaboration, research, and artificial intelligence (AI) model training. The surge in telemedicine and remote diagnostics further amplifies the need for robust de-identification solutions, as data is increasingly exchanged across disparate systems and geographies, exposing it to potential breaches if not adequately protected.

Another significant driver is the integration of AI and machine learning technologies into medical imaging workflows. As healthcare organizations leverage large, diverse datasets to develop and validate AI algorithms, the necessity for de-identified imaging data becomes paramount. De-identification gateways facilitate the ethical and legal use of patient data for secondary purposes such as research and clinical trials, without compromising patient confidentiality. The growing adoption of cloud-based healthcare solutions is also propelling the market, as cloud environments demand advanced de-identification capabilities to safeguard data during storage, processing, and transmission. Furthermore, the increasing collaboration between hospitals, research institutes, and technology vendors is fostering innovation and accelerating the deployment of sophisticated de-identification solutions.

The market is also benefitting from the global trend toward interoperability and data standardization in healthcare. As health systems strive to integrate disparate imaging modalities and electronic health record (EHR) platforms, de-identification gateways play a crucial role in ensuring that data exchanged across networks adheres to privacy standards. The rise in cross-border research initiatives and international clinical trials is further stimulating demand, as organizations must navigate a complex web of privacy laws and data protection regulations. Additionally, the emergence of precision medicine and personalized healthcare is driving the need for large-scale, anonymized imaging datasets, which can only be achieved through robust de-identification processes. These trends collectively underscore the critical importance of de-identification gateways in the modern healthcare ecosystem.

Regionally, North America dominates the Imaging Study De-Identification Gateways market, accounting for the largest revenue share in 2024, owing to stringent regulatory mandates, advanced healthcare infrastructure, and early adoption of digital health technologies. Europe follows closely, driven by the enforcement of GDPR and a strong emphasis on data privacy across the region. The Asia Pacific region is witnessing the fastest growth, supported by rapid healthcare digitization, expanding diagnostic imaging capabilities, and increasing investments in health IT. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as governments and healthcare providers in these regions recognize the value of secure data sharing and compliance with international standards. Overall, the global landscape is characterized by a growing awareness of privacy risks and a collective push toward secure, compliant imaging data management.

Real-World Data De-identification AI Market Outlook

According to our latest research, the global Real-World Data De-identification AI market size reached USD 1.85 billion in 2024, with a robust compound annual growth rate (CAGR) of 21.6% projected from 2025 to 2033. The market is anticipated to achieve a value of USD 13.95 billion by 2033. This remarkable growth is primarily driven by the escalating need for secure data sharing and compliance with stringent privacy regulations across industries, particularly in healthcare, life sciences, and insurance sectors. As organizations increasingly leverage real-world data (RWD) for advanced analytics, clinical research, and operational efficiency, the demand for sophisticated AI-powered de-identification solutions continues to surge worldwide.

One of the principal growth factors fueling the Real-World Data De-identification AI market is the intensifying focus on data privacy and regulatory compliance. Global regulations such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and other regional data protection laws have necessitated the adoption of robust de-identification technologies. Organizations in healthcare, pharmaceuticals, and insurance are increasingly mandated to anonymize or pseudonymize sensitive data before it can be used for research, analytics, or shared with third parties. AI-driven de-identification solutions offer the scalability, accuracy, and adaptability required to process vast volumes of structured and unstructured data, ensuring compliance while preserving the analytical value of the data. This regulatory landscape, combined with the growing value placed on ethical data stewardship, continues to propel market expansion.

Another significant driver is the exponential growth in healthcare and life sciences data, fueled by the proliferation of electronic health records (EHRs), wearable devices, genomics, and real-world evidence (RWE) initiatives. The integration of AI for de-identification enables organizations to unlock the full potential of these data sources without compromising patient privacy. Pharmaceutical companies, for example, leverage de-identified real-world data for drug development, safety monitoring, and post-market surveillance. Similarly, insurers and government agencies utilize anonymized datasets to enhance risk assessment, optimize healthcare delivery, and inform policy decisions. The ability of AI-powered de-identification tools to rapidly and accurately process diverse data types—including text, images, and audio—further amplifies their adoption across multiple sectors, driving sustained market growth.

Technological advancements in artificial intelligence and machine learning are also instrumental in shaping the Real-World Data De-identification AI market. The evolution of natural language processing (NLP), deep learning, and pattern recognition algorithms has significantly improved the precision and efficiency of de-identification processes. These innovations enable the automation of previously labor-intensive tasks, such as identifying and masking personally identifiable information (PII) in complex datasets. Moreover, AI-based solutions can dynamically adapt to evolving data formats and regulatory requirements, offering future-proof capabilities to organizations. The continuous investment in R&D and strategic collaborations between technology providers and industry stakeholders further stimulate innovation, expanding the scope and effectiveness of de-identification solutions.

From a regional perspective, North America currently dominates the Real-World Data De-identification AI market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced healthcare infrastructure, high adoption of digital technologies, and proactive regulatory environment. Europe follows closely, driven by stringent data protection laws and significant investments in healthcare digitization. The Asia Pacific region, meanwhile, is witnessing the fastest growth rate, propelled by the rapid expansion of healthcare IT, increasing awareness of data privacy, and supportive government initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a comparatively nascent stage, as organizations in these regions begin to recognize the value of AI-driven data de-identification for compliance and innovation.

These three datasets contain de-identified data on testing for pests in imports of horticultural products into Australia in a period within 2021-2023. The creator of this data page is distributing the data with the permission of the data owner (emails 14/6/2024, 25/6/2024, 1/7/2024).

Dataset anonymized_hort_aggdat_01-07-2024.csv

This dataset (anonymized_hort_aggdat_01-07-2024.csv) has one row for each line of fruit or vegetables tested. Consignments of fruits or vegetables are divided into lines (details may depend on the type of fruit or vegetable). 600 units are sampled from each line, where a unit is usually a single fruit or vegetable (rounding may occur, for example if fruit are grouped into punnets). A result is then obtained from each line ("inspection result"). If the result is not Pass, then fumigation or other actions may be taken. The columns of the data are:

Dataset anonymized_hort_pests_01-07-2024.csv

This dataset contains a row for when there is a pest detection. Note that not all pest detections require action. It may be linked to anonymized_hort_aggdat_01-07-2024.csv using rcd_nbr as a key. The columns of the data are:

Dataset anonymized_hort_seeds_incidents_01-07-2024.csv

This dataset contains a row for seeds detections. Note that not all seed detections require action. It may be linked to anonymized_hort_aggdat_01-07-2024.csv by rcd_nbr as a key and to anonymized_hort_pests_01-07-2024.csv using bottle_number as a key. The columns of the data are:

Antimicrobial Resistance Microbiological Dataset (ARMD-UTSW): A deidentified collection of electronic health records, from a quaternary, academic medical center, for antimicrobial resistance research

Dataset DOI: 10.5061/dryad.0rxwdbsd5

Description of the data and file structure

Our dataset is comprised of longitudinal electronic health records from the University of Texas Southwestern Medical Center. This collection includes deidentified urine, respiratory, and blood based microbiological culture results and susceptibilities from a cohort of adult patients (≥18 years old) regardless of culture positivity. Additional data included in the dataset consists of prior medical histories such as comorbidities, socioeconomic indicators, prior medications, prior infections, and prior medical procedures. The dataset includes 13 .csv files that can be used for future analyses such as machine learning predictive models. All identifying personal information has...

PII Deidentification in Worker Analytics Market Outlook

According to our latest research, the global PII Deidentification in Worker Analytics market size reached USD 1.82 billion in 2024, with a robust year-on-year growth rate. The market is expected to further accelerate at a CAGR of 17.8% from 2025 to 2033, reaching a forecasted value of USD 9.34 billion by 2033. The primary driver of this remarkable expansion is the increasing need for privacy-compliant analytics solutions in organizations leveraging workforce data for strategic decision-making, propelled by stringent data privacy regulations and the rising adoption of advanced analytics in human resource management.

The exponential growth in the PII Deidentification in Worker Analytics market is underpinned by the intensifying focus on data privacy and regulatory compliance across all industry verticals. Organizations are collecting vast amounts of employee data to enhance productivity, optimize workforce management, and drive operational efficiency. However, the emergence of global data protection laws such as the GDPR, CCPA, and similar frameworks in Asia Pacific and Latin America has made it imperative for businesses to adopt robust deidentification solutions. These solutions not only ensure compliance but also foster trust among employees, enabling organizations to harness the full potential of worker analytics without risking exposure of personally identifiable information (PII).

Another critical growth factor is the rapid digital transformation and integration of artificial intelligence (AI) and machine learning (ML) technologies into workforce analytics platforms. As enterprises increasingly rely on predictive analytics and automation to gain actionable insights, the need to safeguard sensitive employee data has become paramount. Deidentification technologies, especially those leveraging advanced cryptographic and anonymization techniques, allow organizations to process large datasets while minimizing privacy risks. This technological evolution is driving adoption among both large enterprises and small and medium enterprises (SMEs), further fueling the expansion of the PII Deidentification in Worker Analytics market.

Furthermore, the rise in remote and hybrid work models post-pandemic has led to a surge in the use of digital collaboration tools and monitoring systems. This shift has generated a massive influx of worker data, intensifying the demand for deidentification solutions to protect employee privacy while enabling effective analytics. Organizations are now prioritizing privacy-by-design approaches in their analytics workflows, integrating deidentification as a core component. This trend is expected to persist, with companies across BFSI, healthcare, IT & telecommunications, and manufacturing sectors leading the way in adopting these solutions to balance compliance, employee trust, and business intelligence.

Regionally, North America continues to dominate the PII Deidentification in Worker Analytics market, accounting for over 41% of the global revenue in 2024, driven by early adoption of privacy technologies, strict regulatory frameworks, and a mature analytics ecosystem. Europe follows closely, benefiting from the GDPR and a strong culture of data protection. The Asia Pacific region is witnessing the fastest growth, with a CAGR exceeding 20% during the forecast period, fueled by rapid digitalization, increasing awareness of data privacy, and expanding regulatory mandates. Latin America and the Middle East & Africa are also showing promising growth trajectories, albeit from a smaller base, as organizations in these regions ramp up their privacy and compliance capabilities.

Component Analysis

The Component segment of the PII Deidentification in Worker Analytics market is bifurcated into software and services, each playing a pivotal role in the ecosystem. Software solutions dominate

Anonymized dataset for the Why We Retweet Scale. Data was collected during the year 2016. Current dataset is deidentified. Please refer to codebook for descriptions of the variables in the dataset.Codebook describes columns in the dataset.

Context

This dataset consists of anonymized and deidentified panoramic dental X-rays of 116 patients, taken at Noor Medical Imaging Center, Qom, Iran. The subjects cover a wide range of dental conditions from healthy, to partial and complete edentulous cases. The mandibles of all cases are manually segmented by two dentists. This dataset is used as the basis for the article by Abdi et al [1].

[1] A. H. Abdi, S. Kasaei, and M. Mehdizadeh, “Automatic segmentation of mandible in panoramic x-ray,” J. Med. Imaging, vol. 2, no. 4, p. 44003, 2015.

Content

I have converted the dataset into tf.record file with a starter notebook as a guide.

Acknowledgements

These kinds of datasets are absolute gems, especially in the field of Dentistry. The great work was achieved by Amir Abdi and Shohreh Kasaei. The link to the article is here : https://data.mendeley.com/datasets/hxt48yk462/2

Inspiration

It would be interesting to see how this data can be used to create a classification model of abnormalities in dental x-ray.

The Medical Staff People Tracking Dataset provides high-quality, anonymized clinical and movement data of healthcare personnel in medical environments. It is designed to support AI and ML models for hospital workflow optimization, safety monitoring, and activity analysis while ensuring privacy and compliance.

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

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.9 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.9 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

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.

Introduction As mobile robots proliferate in communities, designers must consider the impacts these systems have on the users, onlookers, and places they encounter. It becomes increasingly necessary to study situations where humans and robots coexist in common spaces, even if they are not directly interacting. This dataset presents a multidisciplinary approach to study human-robot encounters in an indoor apartment-like setting between participants and two mobile robots. Participants take questionnaires, wear sensors for physiological measures, and take part in a focus group after experiments finish. This dataset contains raw time series data from sensors and robots, and qualitative results from focus groups. The data can be used to analyze measures of human physiological response to varied encounter conditions, and to gain insights into human preferences and comfort during community encounters with mobile robots. Dataset Contents A dictionary of terms found in the dataset can be found in the "Data-Dictionary.pdf" Synchronized XDF files from every trial with raw data from electrodermal activity (EDA), electrocardiography (ECG), photoplethysmography (PPG) and seismocardiography (SCG). These synchronized files also contain robot pose data and microphone data. Results from analysis of two important features found from heart rate variability (HRV) and EDA. Specifically, HRV_CMSEn and nsEDRfreq is computed for each participant over each trial. These results also include Robot Confidence, which is a classification score representing the confidence that the 80 physiological features considered originate from a subject in a robot encounter. The higher the score, the higher the confidence A vectormap of the environment used during testing ("AHG_vectormap.txt") and a csv with locations of participant seating within the map ("Participant-Seating-Coordinates.csv"). Each line of the vectormap represents two endpoints of a line: x1,y1,x2,y2. The coordinates of participant seating are x,y positions and rotation about the vertical axis in radians. Anonymized videos captured using two static cameras placed in the environment. They are located in the living room and small room, respectively. Animations visualized from XDF files that show participant location, robot behaviors and additional characteristics like participant-robot line-of-sight and relative audio volume. Quotes associated with themes taken from focus group data. These quotes demonstrate and justify the results of the thematic analysis. Raw text from focus groups is not included for privacy concerns. Quantitative results from focus groups associated with factors influencing perceived safety. These results demonstrate the findings from deductive content analysis. The deductive codebook is also included. Results from pre-experiment and between-trial questionnaires Copies of both questionnaires and the semi-structured focus group protocol. Human Subjects This dataset contain de-identified information for 24 total subjects over 13 experiment sessions. The population for the study is the students, faculty and staff at the University of Texas at Austin. Of the 24 participants, 18 are students and 6 are staff at the university. Ages range from 19-48 and there are 10 males and 14 females who participated. Published data has been de-identified in coordination with the university Internal Review Board. All participants signed informed consent to participate in the study and for the distribution of this data. Access Restrictions Transcripts from focus groups are not published due to privacy concerns. Videos including participants are de-identified with overlays on videos. All other data is labeled only by participant ID, which is not associated with any identifying characteristics. Experiment Design Robots This study considers indoor encounters with two quadruped mobile robots. Namely, the Boston Dynamics Spot and Unitree Go1. These mobile robots are capable of everyday movement tasks like inspection, search or mapping which may be common tasks for autonomous agents in university communities. The study focus on perceived safety of bystanders under encounters with these relevant platforms. Control Conditions and Experiment Session Layout We control three variables in this study: Participant seating social (together in the living room) v. isolated (one in living room, other in small room) Robots Together v. Separate Robot Navigation v. Search Behavior A visual representation of the three control variables are shown on the left in (a)-(d) including the robot behaviors and participant seating locations, shown as X's. Blue represent social seating and yellow represent isolated seating. (a) shows the single robot navigation path. (b) is the two robot navigation paths. In (c) is the single robot search path and (d) shows the two robot search paths. The order of behaviors and seating locations are randomized and then inserted into the experiment session as...

Postnatal Affective MRI Dataset

Authors Heidemarie Laurent, Megan K. Finnegan, and Katherine Haigler

The Postnatal Affective MRI Dataset (PAMD) includes MRI and psych data from 25 mothers at three months postnatal, with additional psych data collected at three additional timepoints (six, twelve, and eighteen months postnatal). Mother-infant dyad psychosocial tasks and cortisol samples were also collected at all four timepoints, but this data is not included in this dataset. In-scanner tasks involved viewing own- and other-infant affective videos and viewing and labeling adult affective faces. This repository includes de-identified MRI, in-scanner task, demographic, and psych data from this study.

Citation Laurent, H., Finnegan, M. K., & Haigler, K. (2020). Postnatal Affective MRI Dataset. OpenNeuro. Retrieved from OpenNeuro.org.

Acknowledgments Saumya Agrawal was instrumental in getting the PAMD dataset into a BIDS-compliant structure.

Funding This work was supported by the Society for Research in Child Development Victoria Levin Award "Early Calibration of Stress Systems: Defining Family Influences and Health Outcomes" to Heidemarie Laurent and by the University of Oregon College of Arts and Sciences

Contact For questions about this dataset or to request access to alcohol- and tobacco-related psych data, please contact Dr. Heidemarie Laurent, hlaurent@illinois.edu.

References Laurent, H. K., Wright, D., & Finnegan, M. K. (2018). Mindfulness-related differences in neural response to own-infant negative versus positive emotion contexts. Developmental Cognitive Neuroscience 30: 70-76. https://doi.org/10.1016/j.dcn.2018.01.002.

Finnegan, M. K., Kane, S., Heller, W., & Laurent, H. (2020). Mothers' neural response to valenced infant interactions predicts postnatal depression and anxiety. PLoS One (under review).

MRI Acquisition The PAMD dataset was acquired in 2015 at the University of Oregon Robert and Beverly Lewis Center for Neuroimaging with a 3T Siemens Allegra 3 magnet. A standard 32-channel phase array birdcage coil was used to acquire data from the whole brain. Sessions began with a shimming routine to optimize signal-to-noise ratio, followed by a fast localizer scan (FISP) and Siemens Autoalign routine, a field map, then the 4 functional runs and anatomical scan.

Anatomical: T1*-weighted 3D MPRAGE sequence, TI=1100 ms, TR=2500 ms, TE=3.41 ms, flip angle=7°, 176 sagittal slices, 1.0mm thick, 256×176 matrix, FOV=256mm.

Fieldmap: gradient echo sequence TR=.4ms, TE=.00738 ms, deltaTE=2.46 ms, 4mm thick, 64x64x32x2 matrix.

Task: T2-weighted gradient echo sequence, TR=2000 ms, TE=30 ms, flip angle=90°, 32 contiguous slices acquired ascending and interleaved, 4 mm thick, 64×64 voxel matrix, 226 vols per run.

Participants Mothers (n=25) of 3-month-old infants were recruited from the Women, Infants, and Children program and other community agencies serving low-income women in a midsize Pacific Northwest city. Mothers' ages ranged from 19 to 33 (M=26.4, SD=3.8). Most mothers were Caucasian (72%, 12% Latina, 8% Asian American, 8% other) and married or living with a romantic partner (88%). Although most reported some education past high school (84%), only 24% had completed college or received a graduate degree, and their median household income was between $20,000 and $29,999. For more than half of the mothers (56%), this was their first child (36% second child, 8% third child). Most infants were born on time (4% before 37 weeks and 8% after 41 weeks of pregnancy), and none had serious health problems. A vaginal delivery was reported by 56% of mothers, with 88% breastfeeding and 67% bed-sharing with their infant at the time of assessment. Over half of the mothers (52%) reported having engaged in some form of contemplative practice (mostly yoga and only 8% indicated some form of meditation), and 31% reported currently engaging in that practice. All women gave informed consent prior to participation, and all study procedures were approved by the University of Oregon Institutional Review Board. Due to a task malfunction, participant 178's scanning session was split over two days, with the anatomical acquired in ses-01, and the field maps and tasks acquired in ses-02.

Study overview Mothers visited the lab to complete assessments at four timepoints postnatal: the first session occurred when mothers were approximately three months postnatal (T1), the second session at approximately six months postnatal (T2), the third session at approximately twelve months postnatal (T3), and the fourth and last session at approximately eighteen months postnatal (T4). MRI scans were acquired shortly after their first session (T1).

Asssessment data Assessments collected during sessions include demographic, relationship, attachment, mental health, and infant-related questionnaires. For a full list of included measures and timepoints at which they were acquired, please refer to PAMD_codebook.tsv in the phenotype folder. Data has been made available and included in the phenotype folder as 'PAMD_T1_psychdata', 'PAMD_T2_psychdata', 'PAMD_T3_psychdata', 'PAMD_T4_psychdata'. To protect participants' privacy, all identifiers and questions relating to drugs or alcohol have been removed. If you would like access to drug- and alcohol-related questions, please contact the principle investigator, Dr. Heidemarie Laurent, to request access. Assessment data will be uploaded shortly.

Post-scan ratings After the scan session, mothers watched all of the infant videos and rated the infant's and their own emotional valence and intensity for each video. For valence, mothers were asked "In this video clip, how positive or negative is your baby's emotion?" and "While watching this video clip, how positive or negative is your emotion? from -100 (negative) to +100 (positive). For emotional intensity, mothers were asked "In this video clip, how intense is your baby's emotion?" and "While watching this video clip, how intense is your emotion?"" on a scale of 0 (no intensity) to 100 (maximum intensity). Post-scan ratings are available in the phenotype folder as "PAMD_Post-ScanRatings."

MRI Tasks

Neural Reactivity to Own- and Other-Infant Affect

File Name: task-infant 

Approximately three months postnatal, a graduate research assistant visited mothers’ homes to conduct a structured clinical interview and video-record the mother interacting with her infant during a peekaboo and arm-restraint task, designed to elicit positive and negative emotions, respectively. The mother and infant were face-to-face for both tasks. For the peekaboo task, the mother covered her face with her hands and said "baby," then opened her hands and said "peekaboo" (Montague and Walker-Andrews, 2001). This continued for three minutes, or until the infant showed expressions of joy. For the arm-restraint task, the mother changed their baby's diaper and then held the infant's arms to their side for up to two minutes (Moscardino and Axia, 2006). The mother was told to keep her face neutral and not talk to her infant during this task. This procedure was repeated with a mother-infant dyad that were not included in the rest of the study to generate other-infant videos. Videos were edited to 15-second clips that showed maximum positive and negative affect. Presentation® software (Version 14.7, Neurobehavioral Systems, Inc. Berkeley, CA, www.neurobs.com) was used to present positive and negative own- and other-infant clips and rest blocks in counterbalanced order during two 7.5-minute runs. Participants were instructed to watch the videos and respond as they normally would without additional task demands. To protect participants' and their infants' privacy, infant videos will not be made publicly available. However, the mothers' post-scan rating of their infant's, the other infant's, and their own emotional valence and intensity can be found in the phenotype folder as "PAMD_Post-ScanRatings."

Observing and Labeling Affective Faces

File Name: task-affect 

Face stimuli were selected from a standardized set of images (Tottenham, Borscheid, Ellersten, Markus, & Nelson, 2002). Presentation Software (version 14.7, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com) was used to show participants race-matched adult target faces displaying emotional expressions (positive: three happy faces; negative: one fear, one sad, one anger; two from each category were open-mouthed; one close-mouthed) and were instructed to "observe" or choose the correct affect label for the target image. In the observe task, subjects viewed an emotionally evocative face without making a response. During the affect-labeling task, subjects chose the correct affect label (e.g., "scared," "angry," "happy," "surprised") from a pair of words shown at the bottom of the screen (Lieberman et al., 2007). Each block was preceded by a 3-second instruction screen cueing participants for the current task ("observe" and "affect labeling") and consisted of five affective faces presented for 5 seconds each, with a 1- to 3-second jittered fixation cross between stimuli. Each run consisted of twelve blocks (six observe; six label) counterbalanced within the run and in a semi-random order of trials within blocks (no more than four in a row of positive or negative and, in the affect-labeling task, of the correct label on the right or left side).

.Nii to BIDs

The raw DICOMs were anonymized and converted to BIDS format using the following procedure (for more details, seehttps://github.com/Haigler/PAMD_BIDS/).

1. Deidentifying DICOMS: Batch Anonymization of the DICOMS using DicomBrowser (https://nrg.wustl.edu/software/dicom-browser/)

2. Conversion to .nii and BIDS structure: Anonymized DICOMs were converted to

This dataset contains de-identified resting-state EEG recordings from individuals with Parkinson’s disease (PD) and age-matched healthy control subjects. All EEG data were recorded using standard clinical EEG systems at Neurology Clinic. Dataset Purpose: This dataset was originally used to evaluate whether resting-state EEG can help distinguish subjects who were later deceased from those who remained living (mortality classification). Only de-identified EEG data and mortality labels are included.

Participant Information: - Participants are labeled as either "living" or "deceased" in participants.tsv - No other demographic or clinical information (age, cognition, UPDRS, disease duration, etc.) is included per data-sharing guidelines. - All participant IDs are anonymized following BIDS convention (e.g., sub-PD1301).

EEG Acquisition Details: - Recording type: Resting-state EEG (eyes open) - Device: Clinical BrainVision EEG system - File formats: .vhdr, .eeg, .vmrk - Sampling rate: 500 Hz - Montage: Standard 10–20 international system - Recording condition: “task-rest” (no task)

Data Organization: Data are structured following the BIDS (Brain Imaging Data Structure) EEG standard: sub-

Mortality Label Format: - Living subjects: survival_status = "living" - Deceased subjects: survival_status = "deceased"

Ethics & Privacy: All subjects provided consent for EEG recording at the University of Iowa Hospitals and Clinics. The publicly shared version here is fully de-identified and contains no clinical or personal health information other than mortality classification.

Suggested Use: This dataset can be used to explore EEG biomarkers of mortality risk, EEG signal characteristics in PD, or to build machine learning models for classification.

Questions or requests: Please contact nandakumar-narayanan@uiowa.edu.