Clinical Data Tokenization Platforms Market Outlook

According to our latest research, the global clinical data tokenization platforms market size reached USD 1.12 billion in 2024, demonstrating a robust growth trajectory. The market is currently expanding at a CAGR of 19.7%, with projections indicating that it will attain a value of USD 4.89 billion by 2033. This impressive growth is primarily driven by the increasing adoption of advanced data security measures, the proliferation of clinical trials, and the growing need for regulatory compliance across the healthcare and life sciences sectors. The market's rapid expansion is underpinned by the urgent demand for secure, interoperable, and privacy-preserving solutions that can seamlessly manage and protect sensitive patient and clinical data in a digital-first healthcare environment.

A key growth factor propelling the clinical data tokenization platforms market is the escalating volume and complexity of healthcare data generated by electronic health records (EHRs), wearable devices, and clinical research activities. As healthcare organizations and pharmaceutical companies strive to leverage this data for improved patient outcomes and accelerated drug development, the risk of data breaches and non-compliance with stringent regulations such as HIPAA and GDPR has become a significant concern. Clinical data tokenization platforms address these challenges by converting sensitive data into non-identifiable tokens, thereby minimizing exposure to unauthorized access while enabling secure data sharing and analysis. The growing awareness of cybersecurity threats and the increasing frequency of data breaches in the healthcare sector are compelling stakeholders to invest in advanced tokenization technologies, further fueling market growth.

Another critical driver is the surge in decentralized and virtual clinical trials, particularly in the wake of the COVID-19 pandemic. The adoption of remote and hybrid clinical trial models has necessitated robust data protection mechanisms to ensure the privacy and integrity of patient information across multiple digital touchpoints. Clinical data tokenization platforms play a pivotal role in enabling secure data exchange between stakeholders, including sponsors, contract research organizations (CROs), and regulatory bodies. This not only streamlines trial operations but also facilitates compliance with global data privacy laws, thereby reducing the risk of costly legal penalties. As the pharmaceutical industry continues to embrace digital transformation and real-world evidence (RWE) generation, the demand for tokenization solutions is expected to witness sustained growth.

Furthermore, the increasing emphasis on interoperability and data integration across healthcare ecosystems is catalyzing the adoption of clinical data tokenization platforms. With the proliferation of health information exchanges (HIEs), data lakes, and cloud-based data repositories, there is a growing need to protect patient identities while enabling seamless data aggregation and analytics. Tokenization offers a scalable and efficient approach to de-identifying and linking disparate datasets, supporting advanced analytics, artificial intelligence (AI), and machine learning (ML) applications in healthcare. This enables organizations to derive actionable insights from large-scale clinical data while maintaining compliance with privacy regulations. The convergence of regulatory mandates, technological advancements, and the shift towards value-based care models is expected to further accelerate market growth in the coming years.

Regionally, North America continues to dominate the clinical data tokenization platforms market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to the presence of major pharmaceutical and biotechnology companies, a highly digitized healthcare infrastructure, and stringent regulatory frameworks governing data privacy and security. The United States, in particular, is witnessing significant investments in healthcare IT and cybersecurity solutions, driving the adoption of tokenization platforms across hospitals, research institutions, and CROs. Meanwhile, the Asia Pacific region is poised for the fastest growth, fueled by increasing healthcare digitization initiatives, expanding clinical research activities, and rising awareness of data protection standards. As global collaborations in drug development and clinical research intensify, the deman

Clinical trial data-sharing is seen as an imperative for research integrity and is becoming increasingly encouraged or even required by funders, journals, and other stakeholders. However, early experiences with data-sharing have been disappointing because they are not always conducted properly. Health data is indeed sensitive and not always easy to share in a responsible way. We propose 10 rules for researchers wishing to share their data. These rules cover the majority of elements to be considered in order to start the commendable process of clinical trial data-sharing: Rule 1: Abide by local legal and regulatory data protection requirementsRule 2: Anticipate the possibility of clinical trial data-sharing before obtaining fundingRule 3: Declare your intent to share data in the registration stepRule 4: Involve research participantsRule 5: Determine the method of data accessRule 6: Remember there are several other elements to shareRule 7: Do not proceed aloneRule 8: Deploy optimal data management to ensure that the data shared is usefulRule 9: Minimize risksRule 10: Strive for excellence.

Health Data Clean Room Liability Insurance Market Outlook

As per our latest research, the global Health Data Clean Room Liability Insurance market size reached USD 1.32 billion in 2024, reflecting a robust response to the increasing complexity of healthcare data privacy and compliance regulations. The market is projected to expand at a CAGR of 18.5% from 2025 to 2033, forecasting a value of approximately USD 6.23 billion by 2033. This remarkable growth is primarily driven by the rising adoption of data clean rooms in healthcare, growing cyber threats, and the intensifying need for specialized liability coverage to mitigate emerging risks associated with sensitive health data handling.

The primary growth factor for the Health Data Clean Room Liability Insurance market is the accelerating digital transformation within the healthcare sector. As healthcare providers and related organizations increasingly rely on data clean rooms to facilitate secure and compliant data collaboration, the risks associated with privacy breaches and regulatory non-compliance have surged. The implementation of stringent data protection regulations such as HIPAA, GDPR, and other regional mandates has compelled organizations to seek robust liability insurance solutions tailored to the unique risks of health data clean rooms. This heightened regulatory scrutiny is pushing organizations to invest in insurance products that not only cover traditional liabilities but also address the complexities of data anonymization, third-party access, and cross-border data sharing.

Another significant driver is the proliferation of cyber threats targeting healthcare organizations, which are among the most lucrative targets for cybercriminals due to the sensitive nature of the data they handle. The increasing frequency and sophistication of cyberattacks, including ransomware, phishing, and insider threats, have underscored the need for comprehensive liability insurance. Health data clean rooms, while designed to enhance privacy and security, present novel vulnerabilities that insurers are now addressing through specialized products. These products offer coverage for financial losses, legal expenses, and reputational damage arising from data breaches or misuse within clean room environments, further fueling market demand.

Additionally, the growing collaboration between healthcare providers, insurers, pharmaceutical companies, and research organizations is amplifying the need for shared data environments that maintain privacy and compliance. Health data clean rooms enable these collaborations by providing secure spaces for data analysis without exposing raw data. However, this also introduces shared liability and complex risk profiles that traditional insurance policies may not adequately cover. As a result, there is a rising trend among organizations to seek bespoke liability insurance solutions that can be customized to their specific operational models, data governance frameworks, and regulatory obligations, thereby driving the market's expansion.

From a regional perspective, North America continues to dominate the Health Data Clean Room Liability Insurance market, accounting for over 41% of the global market share in 2024. This leadership is attributed to the advanced healthcare infrastructure, early adoption of digital health technologies, and a highly regulated environment that prioritizes data privacy and security. Europe follows closely, supported by the comprehensive data protection framework under GDPR and increased investments in healthcare IT. Meanwhile, the Asia Pacific region is experiencing the fastest growth, driven by rapid digitalization, expanding healthcare sectors, and evolving regulatory landscapes, making it a key area of focus for market participants in the coming years.

Coverage Type Analysis

The Coverage Type segment in the Health Data Clean Room Liability Insurance market is categorized into General Liability, Professional Liability, Cyber Liability, and Others. Among these, Cyber Liability has emerged as the most dynamic sub-segment, primarily due to the escalating cyber risks associated with health data clean room environments. As healthcare organizations increasingly rely on interconnected digital platforms, the exposure to data breaches, unauthorized access, and cyberattacks has expanded significantly. Cyber Liability insurance products are specifically designed to address these r

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

The report reviews existing standards, best practices, and governance requirements needed to establish and run trusted repositories that house health research data. The objectives of the report are to identify existing standards related to data repositories; to assess the standards that are currently in place in selected repositories that house health data; to conduct a gap analysis of governance standards used in existing repositories.Updated to version 3 on 11th May 2018:The first version of Appendix 1 had the word ‘confidential’ on the cover page which had been left in in error during the editing process. This has now been taken out. Updated to version 2 on 8th May 2018: The document was previously called ‘Development of International Standards for Online Repositories’ but has been changed to ‘Development of Standards for Online Repositories’ as it better reflects the document and its scope. The first version of the document had the word ‘confidential’ on the cover page which had been left in in error during the editing process. This has now been taken out. Finally, Appendix 1 has been added as it is referred to in the document but was not available.

What is the Active Prevalence of COVID-19? By Mu-Jeung Yang, Marinho Bertanha, Nathan Seegert, Maclean Gaulin, Adam Looney, Brian Orleans, Andrew T. Pavia, Kristina Stratford, Matthew Samore, Steven Alder Code repository to recreate the figures and tables in “What is the Active Prevalence of COVID-19?”, Review of Economics and Statistics, 2023 Data • Our primary data on COVID-19 positivity rates and case counts are publicly available from covidtracking.com • Population data for Utah is publicly available from the Census Bureau. • Our testing data used to calibrate our model contains sensitive private information, and is thus not available for distribution. However, researchers interested in replicating this part of the analysis can apply with an email to mjyang@ou.edu, for an anonymized and randomized subsample that replicates our main results. Decisions about data sharing will be made on a case-by-case basis. Instructions Code can generally be run in numerical order presented in filenames. All but one are stata files, run using Stata 17 (but should be generally compatible with other versions): 1. 1.0_load_data.do is run by other files, not individually. 2. 1.1_cache-load_lasso_data.do is used to create the dataset for lasso regressions, which use interactions. This file makes those interaction variables, and names them appropriately to be used in loops and with Stata’s * notation. 3. 2.1_cache_bootstrap_results.do caches the CIs from our SE bootstrap procedure, because it takes a long time to run. Caches bootstrap results to ./output/bootstrap/. 4. 3.0_table_1.do creates summary statistics and tex variables to be used in the paper. 5. 3.1_table_2.do creates table 2, which uses bootstrap SEs, so 2.1_cache_bootstrap_results.do should have been run first. Also saves off data to a temporary file for use in making figures below. 6. 3.2_table_3.do makes the state estimates in table 3. 7. 4.0_figure_1.ipynb uses python to generate Figure 1. 8. 4.1_figure_2.do makes both panels of figure 2, using the cached file from 3.1_table_2.do. 9. 5.0_appendix_c_table_1.do makes Table 1 in Appendix C. 10. 5.1_appendix_c_table_2.do makes Table 2 in Appendix C. 11. 6.0_appendix_b_figure_3.do makes figure 3 in Appendix B. To run, extract this repo to ~/Desktop/RESTAT_CODE and execute the files in Stata or Python as per above.

Medical Profile Safe Share Platform Market Outlook

According to our latest research, the global Medical Profile Safe Share Platform market size reached USD 2.1 billion in 2024, and is expected to grow at a robust CAGR of 15.2% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a value of USD 6.2 billion by 2033. The rapid expansion of this market is primarily driven by the rising demand for secure, interoperable, and patient-centric digital health data sharing solutions across healthcare ecosystems, as organizations and individuals prioritize privacy, compliance, and seamless information exchange.

One of the key growth factors propelling the Medical Profile Safe Share Platform market is the increasing digitization of healthcare records and the growing emphasis on data interoperability. As healthcare systems worldwide transition from paper-based to electronic health record (EHR) systems, the need for platforms that can facilitate secure, standardized, and real-time sharing of medical profiles has become paramount. This trend is further fueled by regulatory mandates such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe, which require stringent data privacy and security measures. Additionally, the proliferation of telemedicine and remote patient monitoring has heightened the necessity for platforms that can enable trusted sharing of sensitive medical information across diverse healthcare stakeholders, including hospitals, clinics, research institutions, and insurance providers.

Another significant driver is the growing focus on patient-centric care and the empowerment of individuals to control their own health data. Modern Medical Profile Safe Share Platforms are designed to provide patients with greater transparency, access, and authority over their personal health information. This patient-first approach not only enhances user engagement but also enables seamless care coordination, reduces redundancies, and mitigates medical errors. The integration of advanced technologies such as blockchain, artificial intelligence, and end-to-end encryption further strengthens the security and reliability of these platforms, fostering trust among users and accelerating market adoption. As healthcare organizations increasingly recognize the value of patient-controlled data sharing, the demand for robust, scalable, and interoperable platforms is expected to surge.

Furthermore, the Medical Profile Safe Share Platform market is benefiting from the expanding ecosystem of digital health startups and strategic collaborations between technology providers and healthcare organizations. Venture capital investments in health IT and digital health are at an all-time high, enabling the development of innovative solutions that address the unique needs of diverse end-users, from healthcare providers and payers to patients and researchers. The competitive landscape is characterized by rapid technological advancements, frequent product launches, and the integration of emerging standards such as Fast Healthcare Interoperability Resources (FHIR). This dynamic environment is fostering a culture of continuous innovation, which is critical for addressing evolving cybersecurity threats and regulatory requirements.

Regionally, North America continues to dominate the global Medical Profile Safe Share Platform market, accounting for the largest share in 2024. This dominance is attributed to the region’s advanced healthcare IT infrastructure, high adoption rates of electronic health records, and a strong regulatory framework supporting data privacy and interoperability. Europe follows closely, driven by stringent data protection regulations and growing investments in digital health transformation. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by government initiatives to digitize healthcare, rising healthcare expenditures, and increasing awareness of the benefits of secure health data sharing. Latin America and the Middle East & Africa are also showing promising growth trajectories, albeit from a smaller base, as they invest in modernizing their healthcare systems and improving access to digital health services.

Component Analysis

The Medical Profile Safe Share Platform market is segmented by component into software, hardware, and services, each playing a crucial role in the

This publication provides statistics on the number of unique NHS numbers with an associated national data opt-out. The national data opt-out was introduced on 25 May 2018. It was introduced following recommendations from the National Data Guardian. It indicates that a patient does not want their confidential patient information to be shared for purposes beyond their individual care across the health and care system in England. The service allows individuals to set a national data opt-out or reverse a previously set opt-out. It replaced the previous type 2 opt-outs which patients registered via their GP Practice. Previous type 2 opt-outs have been converted to national data opt-outs each month, until November 2018. This is why the monthly increase in opt-outs decreases from December 2018 onward. This publication includes the number of people who have a national data opt-out, broken down by age, gender and a variety of geographical breakdowns. From June 2020 the methodology for reporting NDOP changed, representing a break in time series. Therefore, caution should be used when comparing data to publications prior to June 2020. The number of deceased people with an active NDOP has been captured and reported for the first time in June 2020. Please note that this publication is no longer released monthly. It is released annually or when the national opt-out rate changes by more than 0.1 per cent. Prior to September 2020 there is a slight inflation of less than 0.05 percent in the number of National Data Opt-outs. This is due to an issue with the data processing, which has been resolved and does not affect data after September 2020. This issue does not disproportionately affect any single breakdown, including geographies. Please take this into consideration when using the data. As of January 2023, index of multiple deprivation (IMD) data has been added to the publication, allowing the total number of opt-outs to be grouped by IMD decile. This data has been included as a new CSV, and has also been added to a new table in the summary file. IMD measures relative deprivation in small areas in England, with decile 1 representing the most deprived areas, and decile 10 representing least deprived. Please note that the figures reported in IMD decile tables will not add up to the national totals. This is because the IMD-LSOA mapping reference data was created in 2019, and any geography codes added since then will not be mapped to an IMD decile. For more information about the reference data used, please view this report: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 Management information describes aggregate information collated and used in the normal course of business to inform operational delivery, policy development or the management of organisational performance. It is usually based on administrative data but can also be a product of survey data. We publish these management information to ensure equality of access and provide wider public value.

Privacy-Preserving Analytics in Healthcare Market Outlook

According to our latest research, the global market size for Privacy-Preserving Analytics in Healthcare reached USD 1.42 billion in 2024, demonstrating robust momentum driven by the increasing demand for secure data processing and regulatory compliance. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 7.08 billion by the end of the period. This impressive growth is primarily attributed to the rising adoption of advanced analytics technologies, the proliferation of sensitive healthcare data, and stringent privacy regulations such as HIPAA and GDPR.

One of the primary growth drivers for the Privacy-Preserving Analytics in Healthcare market is the exponential increase in healthcare data generation. The widespread adoption of electronic health records (EHRs), wearable health devices, and connected medical equipment has resulted in a massive influx of sensitive patient information. Healthcare organizations are under mounting pressure to extract actionable insights from this data while ensuring patient privacy and regulatory compliance. Privacy-preserving analytics, leveraging technologies such as homomorphic encryption, federated learning, and secure multi-party computation, enable organizations to analyze data without exposing or compromising individual identities. This capability is crucial not only for protecting patient trust but also for facilitating data sharing and collaboration between entities, thereby accelerating medical research and improving patient outcomes.

Another significant factor propelling market growth is the evolving regulatory landscape governing healthcare data privacy. Governments and regulatory bodies across the globe are enforcing stricter guidelines to safeguard patient information. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe mandate robust data protection measures and impose hefty penalties for non-compliance. As a result, healthcare organizations are investing heavily in privacy-preserving analytics solutions to mitigate legal risks, avoid financial penalties, and maintain their reputational integrity. The increasing frequency of high-profile data breaches and cyberattacks further underscores the need for advanced privacy-preserving technologies, making them a top priority for healthcare IT budgets.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) into healthcare analytics are also fueling the expansion of this market. AI-driven analytics can deliver unprecedented insights for clinical decision-making, drug discovery, and personalized medicine. However, the effectiveness of these technologies hinges on access to large, diverse datasets, which raises concerns about data privacy. Privacy-preserving analytics bridge this gap by enabling secure and compliant data utilization, allowing organizations to harness the full potential of AI and ML without compromising confidentiality. This synergy between privacy and innovation is expected to unlock new opportunities for stakeholders across the healthcare ecosystem, from hospitals and clinics to research institutes and pharmaceutical companies.

From a regional perspective, North America currently dominates the Privacy-Preserving Analytics in Healthcare market, accounting for the largest share in 2024. This leadership is driven by the region’s advanced healthcare infrastructure, strong regulatory framework, and significant investments in digital health technologies. Europe follows closely, supported by stringent data protection regulations and a growing emphasis on cross-border healthcare collaboration. The Asia Pacific region is poised for the fastest growth, fueled by increasing healthcare digitization, rising awareness about data privacy, and expanding government initiatives to modernize healthcare systems. Latin America and the Middle East & Africa are also witnessing gradual adoption, though market penetration remains at an early stage compared to other regions.

Component Analysis

The Privacy-Preserving Analytics in Healthcare market is segmented by component into software, hardware, and services, each playing a crucial role in enabling secure and compliant healthcare data analytics. Software solutions form the backbone of this market, offering a w

Medical Profile Sharing for Crash Response Market Outlook

According to our latest research, the global medical profile sharing for crash response market size reached USD 2.9 billion in 2024, with a compound annual growth rate (CAGR) of 11.2% expected from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 7.6 billion, driven by increasing adoption of digital health solutions and the rising importance of rapid, data-driven emergency response. The market’s growth is underpinned by technological advancements, regulatory support, and the growing need for integrated patient information systems to improve outcomes in crash and emergency scenarios.

A primary growth factor for the medical profile sharing for crash response market is the surge in connected healthcare infrastructure globally. As governments and private organizations invest in interoperable health information exchanges, the ability to instantly share patient medical profiles with emergency responders becomes critical. This not only reduces the time required to deliver appropriate care but also minimizes the risk of medical errors during crash response. The proliferation of electronic health records (EHRs), advanced telematics, and IoT-enabled devices in vehicles has significantly enhanced the capacity to transmit vital health data to paramedics and trauma centers in real-time, driving the market forward.

Another significant driver is the growing awareness among consumers and healthcare providers regarding the benefits of real-time data sharing during emergencies. The integration of medical profile sharing solutions with emergency response systems allows for immediate access to crucial information such as allergies, chronic conditions, medications, and previous medical history. This capability is particularly valuable in complex crash scenarios where victims may be unconscious or unable to communicate. Insurance providers and law enforcement agencies are also leveraging these solutions to streamline claims processing, improve fraud detection, and enhance overall response efficiency, further accelerating market adoption.

Moreover, favorable regulatory frameworks and data privacy standards are encouraging the adoption of medical profile sharing technologies. Many regions are implementing legislation that supports secure data exchange while maintaining patient confidentiality. The introduction of advanced encryption, blockchain, and consent management tools is fostering trust among end-users and stakeholders, ensuring compliance with HIPAA, GDPR, and other data protection regulations. As the demand for seamless, cross-platform data sharing grows, vendors are focusing on developing scalable, interoperable solutions that can be easily integrated with existing healthcare and emergency response infrastructure.

Regionally, North America continues to dominate the medical profile sharing for crash response market, accounting for over 38% of the global market share in 2024. This leadership is attributed to the region’s robust healthcare IT ecosystem, high adoption of digital health records, and strong collaboration between public and private sectors. Europe follows closely, driven by its advanced regulatory environment and growing investments in smart mobility and emergency response systems. The Asia Pacific region is witnessing the fastest growth, supported by rapid urbanization, increasing vehicular population, and government initiatives aimed at improving road safety and emergency healthcare delivery.

Component Analysis

The software segment represents the largest share within the medical profile sharing for crash response market, owing to the critical role of platforms that enable secure, real-time data exchange between disparate healthcare and emergency response systems. These software solutions are designed to aggregate, encrypt, and transmit patient information from EHRs and personal health devices directly to first responders and trauma centers. The rapid evolution of AI-driven analytics, interoperability standards such as HL7 and FHIR, and mobile application interfaces has further strengthened the software segment. Vendors are increasingly focusing on user-friendly dashboards, automated alerts, and customizable data-sharing protocols to enhance the efficiency and reliability of crash response operations.

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Digital-First Primary Care Platforms Market Outlook

According to our latest research, the global Digital-First Primary Care Platforms market size reached USD 7.4 billion in 2024, reflecting a robust upward trajectory driven by the increasing adoption of digital healthcare solutions. The market is expected to expand at a CAGR of 18.2% from 2025 to 2033, projecting a value of USD 38.9 billion by 2033. This substantial growth is primarily attributed to the rising demand for accessible, efficient, and cost-effective healthcare services, coupled with advancements in digital health technologies and supportive regulatory frameworks. As per our latest research, the market is experiencing a paradigm shift towards patient-centric, technology-enabled care delivery models, which are reshaping the global healthcare landscape.

The growth of the Digital-First Primary Care Platforms market is propelled by several key factors, foremost among them being the increasing prevalence of chronic diseases and the need for continuous patient engagement. With the global burden of chronic illnesses such as diabetes, hypertension, and cardiovascular diseases on the rise, healthcare providers are seeking innovative solutions to manage these conditions effectively. Digital-first platforms enable remote monitoring, personalized care plans, and timely interventions, which not only improve patient outcomes but also reduce the strain on traditional healthcare infrastructure. Furthermore, these platforms facilitate seamless communication between patients and healthcare professionals, ensuring that care delivery remains uninterrupted, even in the face of challenges such as pandemics or geographical barriers.

Another significant driver fueling market expansion is the widespread adoption of smartphones, high-speed internet, and digital literacy among both patients and healthcare providers. As digital connectivity becomes ubiquitous, patients are increasingly comfortable using mobile applications and online portals to access healthcare services. This shift in consumer behavior has prompted healthcare organizations to invest heavily in digital-first care platforms, integrating features such as telemedicine, electronic health records, and AI-driven diagnostics. The proliferation of wearable devices and remote patient monitoring tools further enhances the capabilities of these platforms, enabling proactive health management and real-time data sharing. As a result, digital-first primary care is becoming the preferred model for delivering preventive, acute, and chronic care services.

Supportive government policies and regulatory initiatives are also playing a pivotal role in shaping the growth trajectory of the Digital-First Primary Care Platforms market. In many countries, regulatory bodies have introduced guidelines and reimbursement frameworks that encourage the adoption of telehealth and digital health solutions. These measures have not only accelerated market penetration but have also fostered innovation by enabling new entrants and established players to develop advanced, compliant solutions. Additionally, collaborations between public and private stakeholders are driving investments in digital infrastructure, further expanding the reach and impact of digital-first primary care. The convergence of technology, policy support, and evolving patient expectations is expected to sustain the market's growth momentum in the coming years.

Regionally, North America continues to dominate the Digital-First Primary Care Platforms market, accounting for the largest share in 2024, closely followed by Europe and Asia Pacific. The high adoption rate in North America is attributed to advanced healthcare infrastructure, favorable reimbursement policies, and a strong presence of key market players. Europe is witnessing significant growth due to increasing government initiatives to promote digital health and a rising geriatric population. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by large patient populations, increasing smartphone penetration, and growing investments in healthcare technology. Latin America and the Middle East & Africa are also showing promising growth, albeit at a slower pace, as digital health adoption gains momentum in these regions.

US State Transportation & Health

Modes, Fatalities, and Physical Activity

By Department of Transportation [source]

About this dataset

This dataset provides a comprehensive look at the transportation and health of each US state. Included are important indicators such as commute mode share (auto, transit, bicycle and walk), complete streets policies, person miles of travel by private vehicle and walking, physical activity from transportation sources, road traffic fatalities exposure rates (auto, bicycle and pedestrian), seat belt use, transit trips per capita, use of federal funds for bicycle/pedestrian efforts, vehicle miles traveled per capita and proximity to major roadways. All these parameters allow for a comprehensive evaluation of the health state in regards to transportation. Thus allowing users to gain insights into the way different states go about their fundamental transport practices that may have implications on their overall health. This tool will allow you to compare different states across these variables in order to make correlations between policy choices and public health outcomes over time – equipping decision makers with crucial information that could help make data-driven decisions in the future

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How to use the dataset

This dataset contains transportation and health information for every state in the US. This data can be used to gain a better understanding of how transportation affects our health and quality of life.

To use this dataset, you first need to understand what each column means. The columns are: State, Commute Mode Share - Auto, Commute Mode Share - Transit, Commute Mode Share - Bicycle, Commute Mode Share - Walk , Complete Streets Policies, Person Miles of Travel by Private Vehicle , Person Miles of Travel by Walking , Physical Activity from Transportation , Road Traffic Fatalities Exposure Rate- Auto , Road Traffic Fatalities Exposure Rate- Bicycle , Road Traffic Fatalities Exposure Rate-Pedestrian , Seat Belt Use Transit Trips per Capita Use of Federal Funds for Bicycle and Pedestrian Efforts Vehicle Miles Traveled per Capita Proximity to Major Roadways . Each column describes a different aspect related to transportation and health in the US states such as the number commuters who drive their own car or those who use the public transit system.

Once you understand what each column represents you can start exploring different states’ data on that particular feature with statistics such as mean value or maximum/minimum value or visualize it in charts/graphs. Additionally, you can look at correlations between different features across multiple states and try to see if they have any relationship or not. You may also want to combine multiple columns together in order create new metrics (or score) that can be compared across all the states (e.g., calculate a “Commuting Score” based on commute mode share for private vehicle/transit/bicycle). Once your analysis is complete you should have an idea about which state has better (or worse) conditions concerning transportation & health indicators and draw conclusions from there!

Research Ideas

• Creating an interactive map of the US illustrating transportation and health data from each state.
• Developing predictive models to forecast the impact of different transportation policies on health outcomes in various states.
• Identifying correlations between changes in transit mode share and road traffic fatalities/injuries based on locations/states within the US over a particular period of time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: THT_Data_508.csv | Column name | Description | |:----------------------------------------------|:------------------------------------------------------------------------------| | State | The name of the US state. (String) | | Commute Mode Share - Auto | The score assigned to the commute mode share for auto. (Number) | | **Commute Mode Share ** | Score | | Commute Mode Share - Transit | The score assigned to the commute mode share for transit. (Number) ...

Animal Disease Data Analytics Market Outlook

According to our latest research, the global animal disease data analytics market size is valued at USD 1.45 billion in 2024, with a robust CAGR of 12.3% expected from 2025 to 2033. This dynamic growth trajectory is projected to drive the market to a significant USD 4.11 billion by 2033. The primary growth factor fueling this expansion is the increasing integration of advanced analytics and artificial intelligence in veterinary health management, enabling proactive disease surveillance and early outbreak detection.

One of the most significant growth drivers for the animal disease data analytics market is the intensification of zoonotic disease outbreaks and their impact on public health and global trade. With recent epidemics such as avian influenza, African swine fever, and COVID-19 highlighting the interconnectedness of animal and human health, governments and private stakeholders are investing heavily in predictive analytics, real-time disease monitoring, and biosecurity measures. This heightened awareness is pushing veterinary organizations, livestock producers, and government agencies to adopt sophisticated data analytics platforms to track, analyze, and contain disease outbreaks more effectively. The adoption of these solutions is further propelled by their ability to provide actionable insights, optimize resource allocation, and mitigate economic losses associated with animal disease outbreaks.

Another major growth factor is the digital transformation sweeping across the animal health sector. The proliferation of Internet of Things (IoT) devices, wearable sensors, and smart farm management systems is generating vast volumes of data related to animal health, behavior, and environment. Advanced data analytics platforms are being increasingly leveraged to process this data, identify early warning signals, and support evidence-based veterinary interventions. This trend is especially pronounced in high-value livestock farming and aquaculture, where disease management is directly tied to productivity and profitability. Furthermore, the integration of cloud-based analytics solutions is enabling seamless data sharing and collaboration among stakeholders, thus fostering a more connected and resilient animal health ecosystem.

Moreover, the growing focus on preventive veterinary care and precision livestock farming is catalyzing demand for animal disease data analytics. As livestock producers strive to enhance animal welfare, reduce antibiotic usage, and comply with stringent regulatory standards, data-driven approaches are becoming indispensable. Analytics-driven risk assessment and outbreak prediction tools are empowering veterinarians and producers to implement targeted interventions, monitor vaccination efficacy, and optimize herd health programs. This paradigm shift toward proactive disease management not only improves animal health outcomes but also supports sustainable agricultural practices and food safety objectives.

From a regional perspective, North America currently dominates the animal disease data analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced veterinary technologies, strong presence of leading market players, and robust regulatory frameworks in these regions are critical factors underpinning their market leadership. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing accelerated growth, driven by increasing investments in animal health infrastructure, expanding livestock sectors, and rising awareness about the economic impact of animal diseases. As these regions continue to modernize their animal health management systems, the global market is expected to witness further diversification and expansion.

Component Analysis

The animal disease data analytics market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment holds the largest market share, driven by the rapid adoption of advanced analytics platforms, artificial intelligence, and machine learning algorithms tailored for veterinary applications. These software solutions enable real-time data processing, visualization, and predictive modeling, which are essential for effective disease surveillance and outbreak management. Cloud-based software platforms, in particular, are gaining traction due to their scalability, remote accessibility, and ability to integrate data from

Medical Profile Sharing for Crash Response Market Outlook

As per our latest research, the global medical profile sharing for crash response market size reached USD 1.13 billion in 2024, and is expected to grow at a robust CAGR of 13.8% during the forecast period, reaching USD 3.23 billion by 2033. The rapid expansion of this market is primarily driven by the increasing emphasis on real-time access to critical patient data during emergencies, which significantly enhances the efficiency and effectiveness of crash response efforts. The integration of advanced digital health technologies, coupled with growing investments in emergency healthcare infrastructure worldwide, is further propelling the adoption of medical profile sharing solutions.

One of the most significant growth factors for the medical profile sharing for crash response market is the rising incidence of road accidents and traumatic injuries globally. With the World Health Organization reporting over 1.3 million fatalities annually due to road traffic crashes, there is a pressing need for emergency responders to access accurate and up-to-date medical information at the scene. This enables faster, more informed clinical decision-making, reducing the risk of adverse outcomes and improving patient survival rates. Furthermore, the increasing adoption of connected devices and IoT solutions in ambulances and emergency vehicles is streamlining the process of sharing medical data, making it possible for first responders to access patient histories, allergies, medications, and other vital information in real time.

Another key driver fueling the growth of the medical profile sharing for crash response market is the growing demand for interoperability among healthcare systems and emergency response networks. As healthcare providers and emergency responders strive to deliver coordinated and timely care, the ability to securely share patient profiles across disparate platforms has become crucial. Regulatory initiatives such as the Health Information Technology for Economic and Clinical Health (HITECH) Act in the United States and similar policies in Europe and Asia Pacific are encouraging the adoption of interoperable health information exchange systems. This regulatory push, combined with advancements in cloud computing and data security technologies, is making it easier for stakeholders to collaborate and deliver seamless patient care during emergencies.

The proliferation of mobile health applications and wearable devices is also contributing to the expansion of the medical profile sharing for crash response market. These technologies empower patients to maintain and update their medical records, which can be instantly shared with emergency responders in the event of an accident. Additionally, increasing public awareness about the benefits of sharing medical information during emergencies is leading to greater acceptance and adoption of these solutions. As governments and private organizations continue to invest in public safety and emergency response infrastructure, the market is poised for sustained growth over the next decade.

The introduction of an Emergency Medical Assistance Platform is revolutionizing the way emergency services operate during crash responses. This platform provides a centralized system where critical medical data can be accessed and shared among first responders, hospitals, and other healthcare providers in real-time. By integrating with existing emergency response systems, the platform ensures that all parties have up-to-date information, which is crucial for making informed decisions quickly. This not only enhances the efficiency of emergency medical services but also improves patient outcomes by reducing the time taken to administer care. The platform's ability to streamline communication and data sharing is particularly beneficial in high-pressure situations, where every second counts. As more regions adopt this technology, the overall effectiveness of crash response efforts is expected to increase significantly.

From a regional perspective, North America currently dominates the medical profile sharing for crash response market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of digital health solutions, robust healthcare IT infrastru

One Health Surveillance Market Outlook

According to our latest research, the global One Health Surveillance market size reached USD 4.2 billion in 2024 and is projected to grow at a robust CAGR of 11.6% from 2025 to 2033, reaching a forecasted market value of USD 11.4 billion by 2033. This remarkable growth is primarily driven by the increasing recognition of the interconnectedness between human, animal, and environmental health, as well as the rising frequency of zoonotic diseases and the urgent need for integrated surveillance systems. The market’s rapid expansion reflects a global commitment to collaborative, cross-sectoral data sharing and technology adoption in public health, veterinary, and environmental domains.

One of the most significant growth factors for the One Health Surveillance market is the escalating threat of zoonotic diseases, which account for more than 60% of emerging infectious diseases globally. The COVID-19 pandemic has dramatically underscored the necessity for integrated surveillance systems that can detect and respond to health threats at the human-animal-environment interface. Governments and international organizations, including the World Health Organization (WHO), Food and Agriculture Organization (FAO), and World Organisation for Animal Health (OIE), are increasingly advocating for One Health approaches. These initiatives are leading to higher investments in surveillance infrastructure, advanced analytics, and real-time data sharing platforms, all of which are vital for early detection, rapid response, and effective containment of outbreaks. The integration of artificial intelligence, machine learning, and big data analytics into surveillance platforms is further enhancing the predictive capabilities and operational efficiency of these systems, fueling market growth.

Another key driver is the evolution of regulatory frameworks, which are mandating robust surveillance protocols and cross-sectoral collaboration. Countries are enacting policies that require data harmonization and interoperability between human health, animal health, and environmental monitoring systems. The growing adoption of digital health technologies, such as electronic health records (EHRs), geographic information systems (GIS), and cloud-based platforms, is facilitating seamless data exchange and real-time analytics. These technological advancements not only improve the accuracy and timeliness of health threat detection but also support more informed decision-making by public health officials. Additionally, the increasing prevalence of antimicrobial resistance (AMR) and the need to monitor its spread across species and ecosystems are further catalyzing demand for integrated One Health surveillance solutions.

The market is also benefiting from heightened public awareness and funding for global health security. International donors, non-governmental organizations, and public-private partnerships are channeling substantial resources into One Health initiatives, particularly in regions vulnerable to emerging infectious diseases. Collaborative research projects and capacity-building programs are fostering innovation and accelerating the deployment of surveillance technologies in both high-income and low- and middle-income countries. This influx of funding is enabling the development of scalable, interoperable solutions that can be tailored to diverse epidemiological and ecological contexts. As a result, the One Health Surveillance market is poised for sustained growth, with increasing adoption across a broad range of end-users, including hospitals, veterinary centers, research institutes, and government agencies.

Regionally, North America currently dominates the One Health Surveillance market due to its advanced healthcare infrastructure, strong regulatory support, and significant investments in research and development. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rising awareness of zoonotic disease risks, expanding healthcare infrastructure, and increasing government initiatives to strengthen One Health capacities. Europe also represents a substantial share of the market, with robust cross-sectoral collaboration and well-established surveillance networks. The Middle East & Africa and Latin America are emerging markets, benefiting from growing international cooperation and investments in health security. Overall, the global landscape is characterized by a dynamic interplay of regional strengths and challenges, shaping the trajectory of the One Health Surveillance market through 2033.

Haiti Health Facilities Locations

An Evaluation of Operating Health Facilities in 2019

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides an authoritative list of operating health facilities in Haiti. It includes comprehensive information that can be used to analyze the geographical distribution of medical resources throughout the country. The data includes attributes such as facility name, nature and type; categories of services offered; and their exact geographic coordinates (latitude/longitude). This dataset is compiled by a registry, ensuring it is up-to-date and reliable, making it a valuable resource for organizations seeking to understand healthcare infrastructure in the nation. The data can be employed to study inequalities within countries between rural and urban areas or urban hubs versus peripheral regions. Given that access to quality health care should be equitable regardless of geographic areas, researchers could use this dataset to trace unevenness across Haiti's geography—or within certain districts or cities—so that corresponding resources may then be allocated accordingly within the region. In addition, major restructuring initiatives can use this kind of detailed information for effective decision making capabilities with respect to delivery networks – allowing for critical epidemiological analysis on population coverage, accessibility issues faced by citizens from remote locations etc. This dataset is released under Creative Commons Attribution license with no rights reserved which allows users to share or adapt the material in any medium or format they wish as long as they attribute its original creator adequately

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How to use the dataset

Getting Started:

Exploring Data:

Analyzing Data: Now that you know what information is provided in each column within a view; analyzing data becomes easier than ever! For example if a researcher wanted insight into specific healthcare facilities in one area they could filter by “Adm 1 Code” which is administrative area level one code - selecting only those located geographical regions would be quickly displayed in first view allowing quick insights into what kind facilities (private / public; pharmacy/hospital) are actually being utilized by people living within those boundaries without having manually sort through every row then totalling up counts across all rows - saving invaluable time when working with large datasets such as those found on Kaggle today! With this data we can determine how accessible healthcare is per region within selected parameters like sectorial access (public/private) or hospital capacity related studies etcetera – helping us understand patient shift trends across various areas that could prove invaluable during times like COVID-19 pandemic where numerous areas might lack resources due higher influxes coming from other surrounding regions facing similar restrictions due same natural hazards etcetera… Allowing us draw insights depending upon our research goals quickly without having sort manage thousands plus rows line item basis saves time & money over long haul allowing quicker actions if needed be put place intervene locally on regional level depending upon findings

Research Ideas

• Finding hospitals in relation to natural disasters or humanitarian crises using a heatmap representation of the data to visualize health facility locations.
• Mapping rural healthcare facilities to provide residents with access to health services and for the government to monitor usage trends over time.
• Plotting data points on an interactive map of Haiti with lines connecting population centers and medical facilities, providing useful data-driven insight into public transportation networks and health infrastructure covering geographic areas across the country

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: haiti-healthsites-hdx-1.csv | Column name | Description | |:---------------------|:-------------------------------------------------------| | **Adm1code*...

The National Institute of Mental Health (NIMH) Research Volunteer (RV) Data Set

A comprehensive dataset characterizing healthy research volunteers in terms of clinical assessments, mood-related psychometrics, cognitive function neuropsychological tests, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unprecedented in its depth of characterization of a healthy population and will allow a wide array of investigations into normal cognition and mood regulation.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Release Notes

Release v2.0.0

This release includes data collected between 2020-06-03 (cut-off date for v1.0.0) and 2024-04-01. Notable changes in this release:

1. 769 new participants have been added along with re-evaluation data for 15 participants. Total unique participants count is now 1859.
2. visit and age_at_visit columns added to phenotype files to distinguish between visits and intervals between them.
3. Follow-up online survey data included.
4. Replaced Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) with General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively.
5. Discontinued the Perceived Health rating survey.
6. Added Brief Trauma Questionnaire (BTQ) and Big Five personality survey to online screening questionnaires.
7. MRI:
   • Replaced ADNI-3 resting state sequence with a multi-echo sequence with higher spatial resolution.
   • Replaced field map scans with a shorter reversed-blipped EPI scan.
8. MEG:
   • Some participants have 6-minute empty room data instead of the shorter duration empty room acquisition.

See the CHANGES file for complete version-wise changelog.

Participant Eligibility

To be eligible for the study, participants need to be medically healthy adults over 18 years of age with the ability to read, speak and understand English. All participants provided electronic informed consent for online pre-screening, and written informed consent for all other procedures. Participants with a history of mental illness or suicidal or self-injury thoughts or behavior are excluded. Additional exclusion criteria include current illicit drug use, abnormal medical exam, and less than an 8th grade education or IQ below 70. Current NIMH employees, or first degree relatives of NIMH employees are prohibited from participating. Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers visit the study website, check a box indicating consent, and fill out preliminary screening questionnaires. The questionnaires include basic demographics, the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure, the DSM-5 Level 2 Cross-Cutting Symptom Measure - Substance Use, the Alcohol Use Disorders Identification Test (AUDIT), the Edinburgh Handedness Inventory, and a brief clinical history checklist. The WHODAS 2.0 is a 15 item questionnaire that assesses overall general health and disability, with 14 items distributed over 6 domains: cognition, mobility, self-care, “getting along”, life activities, and participation. The DSM-5 Level 1 cross-cutting measure uses 23 items to assess symptoms across diagnoses, although an item regarding self-injurious behavior was removed from the online self-report version. The DSM-5 Level 2 cross-cutting measure is adapted from the NIDA ASSIST measure, and contains 15 items to assess use of both illicit drugs and prescription drugs without a doctor’s prescription. The AUDIT is a 10 item screening assessment used to detect harmful levels of alcohol consumption, and the Edinburgh Handedness Inventory is a systematic assessment of handedness. These online results do not contain any personally identifiable information (PII). At the conclusion of the questionnaires, participants are prompted to send an email to the study team. These results are reviewed by the study team, who determines if the participant is appropriate for an in-person interview.

Participants who meet all inclusion criteria are scheduled for an in-person screening visit to determine if there are any further exclusions to participation. At this visit, participants receive a History and Physical exam, Structured Clinical Interview for DSM-5 Disorders (SCID-5), the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The purpose of these cognitive and psychometric tests is two-fold. First, these measures are designed to provide a sensitive test of psychopathology. Second, they provide a comprehensive picture of cognitive functioning, including mood regulation. The SCID-5 is a structured interview, administered by a clinician, that establishes the absence of any DSM-5 axis I disorder. The KBIT-2 is a brief (20 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Biological and physiological measures

Biological and physiological measures are acquired, including blood pressure, pulse, weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), c-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, three additional tubes of blood samples are collected and banked for future analysis, including genetic testing.

Imaging Studies

Participants were given the option to enroll in optional magnetic resonance imaging (MRI) and magnetoencephalography (MEG) studies.

MRI

On the same visit as the MRI scan, participants are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks asses attention and executive functioning (Flanker Inhibitory Control and Attention Task), executive functioning (Dimensional Change Card Sort Task), episodic memory (Picture Sequence Memory Task), and working memory (List Sorting Working Memory Task). The MRI protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed, and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional, and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

MEG

The optional MEG studies were added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system. The position of the head was localized at the beginning and end of the recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For some participants, photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants, a BrainSight neuro-navigation unit was used to coregister the MRI, anatomical fiducials, and localizer coils directly prior to MEG data acquisition.

Specific Survey and Test Data within Data Set

NOTE: In the release 2.0 of the dataset, two measures Brief Trauma Questionnaire (BTQ) and Big Five personality survey were added to the online screening questionnaires. Also, for the in-person screening visit, the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) were replaced with the General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. The Perceived Health rating survey was discontinued.

1. Preliminary Online Screening Questionnaires

IntroductionExercise produces an immediate lessening of pain sensitivity (Exercise-Induced Hypoalgesia (EIH)) in healthy individuals at local and distant sites, possibly through a shared mechanism with conditioned pain modulation (CPM). Dynamic resistance exercise is a recommended type of exercise to reduce pain, yet limited research has examined the effects of intensity on EIH during this type of exercise. Therefore, the primary purpose of this study is to compare changes in PPT at a local and distant site during a leg extension exercise at a high intensity, a low intensity, or a quiet rest condition. A secondary purpose is to examine if CPM changes after each intervention. The final purpose is to examine if baseline pain sensitivity measures are correlated with response to each intervention.MethodsIn a randomized controlled trial of 60 healthy participants, participants completed baseline pain sensitivity testing (heat pain threshold, temporal summation, a cold pressor test as measure of CPM) and were randomly assigned to complete a knee extension exercise at: 1) high intensity (75% of a 1 Repetition Maximum (RM), 2) low intensity (30% 1RM), or 3) Quiet Rest. PPT was measured between each set at a local (quadriceps) and distant (trapezius) site during the intervention. CPM was then repeated after the intervention. To test the first purpose of the study, a three-way ANOVA examined for time x site x intervention interaction effects. To examine for changes in CPM by group, a mixed-model ANOVA was performed. Finally, a Pearson Correlation examined the association between baseline pain sensitivity and response to each intervention.ResultsTime x site x intervention interaction effects were not significant (F(5.3, 150.97) = 0.87, p = 0.51, partial eta2 = 0.03). CPM did not significantly change after the interventions (time x intervention F(1,38) = 0.81, p = 0.37, partial eta2 = 0.02. EIH effects at the quadriceps displayed a significant, positive moderate association with baseline HPT applied over the trapezius (r = 0.61, p

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

Animal Disease Surveillance Market Outlook

According to our latest research, the global animal disease surveillance market size in 2024 stands at USD 3.9 billion, reflecting the sector’s increasing significance amid rising concerns over zoonotic outbreaks and animal health. The market is projected to grow at a robust CAGR of 8.2% from 2025 to 2033. By the end of 2033, the animal disease surveillance market is forecasted to reach USD 7.9 billion, driven by advancements in diagnostic technologies, increasing governmental initiatives, and the growing need for early detection and control of diseases in animal populations. As per our latest research, this upward trend is primarily attributed to heightened awareness about animal-to-human disease transmission and the expanding global livestock sector.

One of the primary growth factors for the animal disease surveillance market is the surge in zoonotic diseases, which has underscored the necessity for robust surveillance systems. Outbreaks such as avian influenza, African swine fever, and rabies have demonstrated the devastating impact animal diseases can have, not only on animal health but also on public health and economies worldwide. Governments and international organizations are investing heavily in surveillance infrastructure, deploying advanced software and hardware solutions to monitor, detect, and respond to disease outbreaks rapidly. This proactive approach is further supported by the integration of artificial intelligence and data analytics, enabling real-time data sharing and predictive modeling, which significantly enhances the efficacy of surveillance programs.

Another key driver is the rapid expansion of the livestock industry, especially in emerging economies. The intensification of animal farming practices has increased the risk of disease transmission within and between animal populations. This has prompted both private and public stakeholders to adopt comprehensive disease surveillance measures, including routine health monitoring, vaccination tracking, and early warning systems. The demand for animal protein is rising, leading to larger herds and flocks, which in turn necessitates more sophisticated disease monitoring tools. Additionally, the globalization of trade in animals and animal products has heightened the need for stringent surveillance to prevent the cross-border spread of infectious diseases, further fueling market growth.

Technological advancements are also playing a pivotal role in shaping the animal disease surveillance market. The integration of cloud-based platforms, IoT-enabled devices, and mobile applications has revolutionized data collection and analysis, making surveillance more efficient and accessible. These innovations are particularly beneficial for remote and resource-limited regions, enabling real-time reporting and faster response to outbreaks. Furthermore, collaborative initiatives between governments, research institutions, and the private sector are fostering the development of standardized protocols and interoperable systems, enhancing the overall effectiveness of disease surveillance networks. The growing emphasis on One Health approaches, which recognize the interconnectedness of human, animal, and environmental health, is further driving investments in comprehensive surveillance solutions.

Regionally, North America dominates the animal disease surveillance market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate of advanced surveillance technologies, well-established veterinary infrastructure, and strong governmental support contribute to North America’s leading position. Europe is also witnessing significant growth, driven by stringent regulatory frameworks and increased funding for research and development. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by expanding livestock populations, increasing awareness about zoonotic diseases, and government-led initiatives to strengthen animal health systems. Latin America and the Middle East & Africa, while smaller in market share, are expected to experience steady growth due to rising investments in animal health infrastructure and growing concerns over disease outbreaks.

Component Analysis

The component segment of the animal disease surveillance market is categorized into software, hardware, and services. Software solutions play a critical role in modern surveillance systems by facilitating data collection, integration, visualization, and analysi