With an emphasis on reaching historically underrepresented populations, the All of Us Research Program recruits adults aged 18 and above across the United States to share their health data to enable new insights into human health and research on precision medicine. Participants contribute electronic health records (EHR), survey responses, biospecimens, wearable devices (biometrics), and physical measurements.

The six All of Us surveys assess the areas listed below:

• Basic demographic information
• Lifestyle/substance use (i.e., tobacco, alcohol, and recreational drugs)
• Overall health (general health status, daily activities, and women’s health)
• Medical history (medical conditions and approximate age of diagnosis)
• Family medical history (medical history of immediate biological family members)
• Health care access and utilization (self-reported use of various health services)

There are currently three tiers of data access.

• Public Tier: Anonymized, aggregate data that can be viewed with the Data Browser.
• Registered Tier: Contains individual-level data and is available only to approved researchers on the Researcher Workbench. Authorized users also have access to tools such as the Cohort Builder, Jupyter Notebooks, and Dataset Builder.
• Controlled Tier: Includes genomic data in the form of whole genome sequencing and genotyping arrays, demographic data fields from EHRs and surveys that are suppressed in other tiers, and unshifted dates.

With an emphasis on reaching historically underrepresented populations, the datasets from the All of Us Research Program include:

• Electronic health records (OMOP CDM used to standardize all EHR data)
• Biosamples (blood, urine, and saliva)
• Mobile health data (from wearable devices that may track biometric data like heart rate and blood pressure)
• Physical measurements (height, weight, BMI, waist circumference, hip circumference, blood pressure, heart rate, pregnancy status, and wheelchair use)
• Surveys (there are three core surveys: basic demographics, lifestyle/substance use, and overall health. Additional ones on health care access and utilization, personal and family medical history, and the impact of COVID-19 are available)
• Genomic data in the form of whole genome sequencing (WGS) and genotyping arrays

The Research Hub divides data into three tiers:

• Public Tier dataset: contains only anonymized, aggregate data available to everyone through the Data Browser (does not allow download).
• Registered Tier dataset: contains individual-level data, available only to approved researchers on the Researcher Workbench. Authorized users also have access to tools such as the Cohort Builder, Jupyter Notebooks, and Dataset Builder.
• Controlled Tier dataset: contains genomic data in the form of whole genome sequencing (WGS) and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events.

Genomic Data Federation Platforms Market Outlook

According to our latest research, the Global Genomic Data Federation Platforms market size was valued at $1.3 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a CAGR of 18.2% during 2024–2033. The primary factor propelling this robust growth is the surging demand for secure, scalable, and interoperable data sharing solutions in genomics research and clinical applications. As genomic data volume explodes due to advancements in sequencing technologies and the proliferation of precision medicine initiatives, organizations are increasingly seeking platforms that enable federated data analysis while ensuring compliance with privacy regulations and safeguarding sensitive patient information. The ability to seamlessly connect disparate datasets across institutions and geographies, without necessitating centralized storage, is transforming the landscape of biomedical research and healthcare innovation worldwide.

Regional Outlook

North America currently dominates the genomic data federation platforms market, accounting for the largest share of global revenue. This leadership position is underpinned by the region’s mature healthcare infrastructure, extensive investments in genomics research, and favorable regulatory frameworks that encourage data interoperability and collaborative science. The United States, in particular, has been at the forefront, with major government-backed initiatives such as the All of Us Research Program and the NIH’s Genomic Data Commons driving large-scale adoption. The presence of leading technology vendors, robust funding from both public and private sectors, and a high concentration of pharmaceutical and biotechnology companies further contribute to North America’s market dominance. The region’s market share is estimated to be over 40% in 2024, with steady growth anticipated through the forecast period as organizations continue to prioritize secure, federated access to genomic datasets.

The Asia Pacific region is expected to register the fastest growth in the genomic data federation platforms market, with a projected CAGR exceeding 22% from 2024 to 2033. This rapid expansion is fueled by increasing government investments in genomics and digital health infrastructure, as well as a burgeoning biotech sector across countries such as China, Japan, South Korea, and India. National precision medicine initiatives, rising awareness of personalized healthcare, and the emergence of regional genomic databases have created a fertile environment for the deployment of federated data platforms. Additionally, the region is witnessing a surge in cross-border research collaborations, necessitating advanced solutions for secure data sharing and compliance with evolving privacy regulations. These factors collectively position Asia Pacific as a key engine of growth for the global market in the coming years.

In emerging economies across Latin America, the Middle East, and Africa, adoption of genomic data federation platforms remains at a nascent stage, but momentum is gradually building. These regions face unique challenges such as limited digital infrastructure, fragmented healthcare systems, and varying levels of regulatory maturity, which can impede the widespread implementation of advanced data federation technologies. Nonetheless, there is a growing recognition of the value of genomic research for addressing local health challenges, and several countries are launching pilot projects and public-private partnerships to build genomic data capabilities. As international collaborations expand and digital health policies evolve, these markets are expected to witness incremental adoption, particularly in research institutes and leading hospitals, albeit at a slower pace compared to more developed regions.

Report Scope

BackgroundDisparities in healthcare access, driven by socioeconomic status and social determinants of health (SDOH), contribute to poor health outcomes. While prior studies established the relationship between SDOH and care access, fewer have explored their joint relationships with social satisfaction and health challenges across the lifespan. Rather than assessing direct associations between dental care utilization and physical or mental difficulties, this study examines broader interrelationships among SDOH, access to oral health care, and self-reported health challenges.MethodsA cross-sectional study using a lifespan approach–by examining participants within discrete age groups–was conducted on 127,886 individuals aged 18 years and older who participated in the All of Us research program and completed the “Basics”, “Overall Health” and “Health Care Access and Utilization” questionnaires. The distribution of participants' SDOH and self-reported health difficulties was presented and stratified by dental care utilization, income group and age across the lifespan. Multivariate logistic regression analyses were performed to assess the associations between SDOH and access to oral health care.ResultsAcross age groups, a consistent trend of disadvantaged social determinants associated with lacking oral health care utilization was noted. Young participants (18–35 years old) were the most likely to report not having received oral health care within the past 12 months (32.2%), worse mental health (29.6%, fair/poor), emotional problems (31.8%), and difficulties in concentrating or remembering (18%). Notably, young adults who did not visit a dentist within 12 months were also more likely to report not visiting a medical doctor (18.1%), being unable to afford copayment (69%), and more frequently using emergency or urgent care (20.2%). No insurance coverage [odds ratio (OR) = 1.67, 95% confidence interval (CI): 1.52–1.84], annual income less than $35,000 (OR = 3.79, 95% CI: 3.58–4.01), and housing instability (OR = 1.38, 95% CI: 1.32–1.44) were all significantly associated with lack of dental care.ConclusionThis study confirms that SDOH—particularly income and housing instability—significantly impact individuals' ability to afford and access healthcare services, including dental care. These disparities were most pronounced among the youngest age group. Our findings support future policy interventions aimed at integrating dental care into overall healthcare, especially during early adulthood.

Cloud Genomics Market Outlook

According to our latest research, the cloud genomics market size reached USD 6.1 billion globally in 2024, demonstrating the sector’s robust expansion. The market is projected to grow at a CAGR of 20.5% from 2025 to 2033, reaching an estimated value of USD 38.6 billion by 2033. This exceptional growth is primarily driven by the increasing adoption of cloud-based solutions in genomics for data storage, analysis, and sharing, as well as the surge in large-scale genomic projects and personalized medicine initiatives worldwide.

One of the most significant growth factors propelling the cloud genomics market is the exponential increase in genomic data generation. With the cost of sequencing genomes plummeting over the past decade, research institutions, hospitals, and pharmaceutical companies are generating massive volumes of genomic data. Traditional on-premises infrastructure often struggles to efficiently store, manage, and analyze such vast datasets. Cloud computing offers a scalable, flexible, and cost-effective solution, enabling stakeholders to process and share data seamlessly. The ability to access advanced analytics and machine learning tools through cloud platforms further enhances the value proposition, fostering innovation in diagnostics, drug discovery, and personalized medicine.

Another critical driver is the growing emphasis on collaborative research and the need for secure data sharing across institutions and borders. Cloud genomics platforms facilitate real-time collaboration among geographically dispersed research teams, breaking down silos and accelerating scientific discovery. This is particularly important for large-scale initiatives such as population genomics projects, which require the aggregation and analysis of genomic data from diverse sources. Furthermore, cloud providers have invested heavily in security and compliance, addressing concerns related to data privacy and regulatory requirements such as HIPAA and GDPR. These advancements have increased confidence among healthcare providers and researchers, further fueling market adoption.

The integration of artificial intelligence (AI) and machine learning (ML) capabilities into cloud genomics solutions is also transforming the landscape. AI-powered algorithms can analyze complex genomic datasets at unprecedented speed and accuracy, uncovering patterns and insights that were previously unattainable. Cloud-based platforms democratize access to these advanced tools, enabling not only large pharmaceutical companies but also smaller biotech firms and academic researchers to harness the power of AI in genomics. This democratization is leading to faster drug discovery cycles, more precise diagnostics, and highly personalized treatment strategies, all of which are key growth drivers for the cloud genomics market.

From a regional perspective, North America continues to dominate the cloud genomics market, owing to its advanced healthcare infrastructure, high research and development expenditure, and the presence of major cloud service providers. The region benefits from strong government support for genomics research, particularly in the United States, where initiatives like the All of Us Research Program are generating massive genomic datasets. Europe is rapidly catching up, driven by collaborative projects and stringent data protection regulations that favor cloud-based solutions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by increasing investments in healthcare IT, expanding genomics research, and rising awareness of personalized medicine. This dynamic regional landscape is expected to shape the future trajectory of the global cloud genomics market.

Component Analysis

The component segment of the cloud genomics market encompasses software, hardware, and services, each playing a pivotal role in the value chain. Software solutions form the backbone of cloud genomics platforms, providing functionalities such as data management, analytics, visualization, and workflow automation. The demand for advanced software tools is being driven by the need to process vast and complex genomic datasets efficiently. Vendors are increasingly offering AI-powered analytics, intuitive user interfaces, and seamless integration with laboratory information management systems (LIMS), making it easier for end-users to derive actionable insights from their data. Open-sou

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Genetic Variant Databases Market Outlook

According to our latest research, the global genetic variant databases market size reached USD 1.45 billion in 2024, reflecting robust growth as advanced genomics and precision medicine initiatives accelerate worldwide. The market is projected to expand at a CAGR of 10.3% during the forecast period, reaching a value of approximately USD 3.51 billion by 2033. This impressive growth is driven by the increasing integration of genetic information into clinical practice, the rising prevalence of genetic disorders, and the growing demand for personalized therapies across healthcare and pharmaceutical sectors.

One of the primary growth factors propelling the genetic variant databases market is the exponential increase in genetic sequencing data generated globally. Advances in next-generation sequencing (NGS) technologies have significantly reduced the cost and time required to sequence entire genomes, leading to an unprecedented accumulation of genetic information. This surge in data necessitates robust, scalable, and accessible databases to catalog, annotate, and interpret genetic variants. As healthcare providers, researchers, and pharmaceutical companies increasingly rely on genetic data to inform diagnostics, treatment decisions, and drug discovery, the demand for comprehensive and interoperable genetic variant databases is expected to rise sharply. The integration of artificial intelligence and machine learning tools further enhances the utility of these databases by enabling high-throughput analysis, variant prioritization, and clinical interpretation, thereby accelerating the pace of genomic medicine.

Another significant driver for the genetic variant databases market is the expanding landscape of precision medicine and population genomics initiatives. Governments and private organizations worldwide are investing heavily in large-scale genomic projects, such as the UK Biobank, the All of Us Research Program in the United States, and the GenomeAsia 100K initiative. These projects aim to collect and analyze genetic data from diverse populations, fueling the need for databases that can handle population-specific and disease-specific variant information. Such initiatives not only enhance the understanding of genetic diversity and disease mechanisms but also support the development of targeted therapies and personalized interventions. As the global healthcare ecosystem shifts towards more individualized approaches, the role of genetic variant databases in supporting clinical diagnostics, risk assessment, and therapeutic decision-making becomes increasingly indispensable.

The market is also benefiting from the growing collaboration between academic institutions, healthcare providers, and the life sciences industry. Strategic partnerships are being forged to create, curate, and share genetic variant data on a global scale, breaking down traditional silos and fostering data interoperability. The adoption of standardized formats and ontologies, such as those promoted by the Global Alliance for Genomics and Health (GA4GH), is facilitating the seamless exchange of genetic information across platforms and borders. Additionally, regulatory agencies are providing clearer guidelines for the use and sharing of genetic data, further supporting market growth. However, challenges related to data privacy, security, and ethical considerations remain critical, necessitating ongoing investment in robust governance frameworks and secure data management solutions.

From a regional perspective, North America currently holds the largest share of the genetic variant databases market, driven by its advanced healthcare infrastructure, strong research ecosystem, and early adoption of genomic medicine. Europe follows closely, benefiting from well-established genomic initiatives and supportive regulatory environments. The Asia Pacific region is emerging as a high-growth market, fueled by increasing genomic research investments, rising awareness of genetic testing, and expanding healthcare access. Latin America and the Middle East & Africa, while currently representing smaller market shares, are witnessing growing interest in genomic technologies and are expected to contribute to future market expansion as infrastructure and expertise develop further.

Database Type Analysis

The database type segment of the genetic variant databases market is diverse, encompassing germline variant databases, somat

GPU-Accelerated Variant Calling Market Outlook

According to our latest research, the Global GPU-Accelerated Variant Calling market size was valued at $612 million in 2024 and is projected to reach $2.51 billion by 2033, expanding at a robust CAGR of 16.9% during 2024–2033. One major factor propelling the growth of this market globally is the exponential rise in genomic data generation, which necessitates advanced computational solutions for swift, accurate, and scalable variant analysis. GPU-accelerated platforms have emerged as the backbone for next-generation sequencing (NGS) workflows, enabling researchers and clinicians to process vast datasets in a fraction of the time compared to traditional CPU-based systems. This efficiency is particularly critical in clinical diagnostics, precision medicine, and large-scale research projects, where turnaround time and data accuracy are paramount. As personalized medicine and genomic research continue to expand, the demand for high-performance, scalable, and cost-effective variant calling solutions is expected to surge across diverse end-user segments.

Regional Outlook

North America currently dominates the GPU-Accelerated Variant Calling market, accounting for over 42% of global revenue in 2024. This region’s leadership is anchored by its mature healthcare infrastructure, robust investment in genomic research, and the presence of leading biotechnology and pharmaceutical companies. The United States, in particular, has witnessed significant adoption of GPU-accelerated platforms in both clinical and academic settings, driven by initiatives like the All of Us Research Program and large-scale cancer genomics projects. The availability of advanced computational resources, favorable reimbursement policies, and a highly skilled workforce further bolster the region’s stronghold. North American institutions are also at the forefront of integrating artificial intelligence and machine learning with GPU-powered variant calling, enhancing both the speed and accuracy of genomic analyses for precision medicine applications.

The Asia Pacific region is projected to be the fastest-growing market, with a remarkable CAGR of 21.3% during the forecast period. Rapid improvements in healthcare infrastructure, increasing government funding for genomics research, and a burgeoning biotechnology sector are key drivers in countries like China, Japan, South Korea, and India. These nations are investing heavily in next-generation sequencing technologies and computational genomics platforms to address large population health challenges and support national precision medicine initiatives. Strategic collaborations between local research institutions and global technology providers are accelerating technology transfer and adoption. Additionally, the expansion of cloud-based deployment models is making advanced variant calling solutions more accessible to emerging markets in the region, further fueling growth.

Emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady uptake of GPU-accelerated variant calling solutions. However, these regions face unique challenges, including limited access to high-performance computing infrastructure, budgetary constraints, and a shortage of trained bioinformatics professionals. Nonetheless, localized demand is rising, particularly in urban centers and academic hubs, where research grants and international partnerships are facilitating technology adoption. Policy reforms aimed at strengthening healthcare systems, coupled with targeted investments in genomics research, are expected to gradually improve market penetration. As cloud-based solutions become more affordable and regulatory frameworks evolve, these regions are poised to contribute significantly to the global market in the coming years.

Report Scope

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

This repository includes code files and data to recreate this paper:

Griffith, KN, & Bor, JH. (2020). “Changes in access, health behaviors, utilization, and self-reported health through the fourth year of the Affordable Care Act.” Medical Care 58(6), 574-578.

These materials are provided for public use without restriction. Please cite this work if it these materials are referenced in any publications. For comments, corrections, or questions please email Dr. Kevin Griffith: kevin.griffith@vumc.org

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include state-level abortion access, which reports the proportion of a state's females aged 15-44 who reside in counties with an abortion provider by year and month from 2009-2022. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

FQHC Location Data

Detailed Address Data on Federally Qualified Health Centers in the US

By US Department of Health and Human Services [source]

About this dataset

This dataset provides comprehensive address-level information on Federally Qualified Health Centers (FQHCs) in the United States. FQHCs are community-driven and consumer run organizations that serve populations with limited access to health care, including those who are low-income, uninsured, have a limited grasp of English, migrating and seasonal farm workers, individuals experiencing homelessness, and those living in public housing. In addition to detailed location addressing data such as postal code and city name for each center in the scope of this dataset; users can find optional information about an individual center such as its operator description or the type of population it serves, along with rich backroom management data which includes grant number, grantee name and uniform resource locator (URL). Get familiarized with this essential dataset to help provide quality medical care access to under served communities across the US

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How to use the dataset

This dataset is an address-level dataset on the locations of Federally Qualified Health Centers (FQHCs). This dataset includes information on the FQHCs such as name, address, contact information, operating hours per week and grant number. It can be used to locate FQHCs in a particular area and to gain insights into the services they provide.

In order to use this data set, it is important to understand what attributes are included. These are broken down into categories including basic site information (name, telephone number etc.), service description (what population is served etc.), region info (HHS region code etc.) and supplemental info including records for operator and grantee organization.

Once you have identified what fields you are interested in, you can then use this data set for further analysis such as counting how many FQHCs exist within a certain area or determining which states have higher numbers of FQHCs than others. You can also filter by features such as services offered or population served to gain further insights into a particular segment of the FQHC market.

It should also be noted that there may be discrepancies between different sources regarding different fields due to variations in data collection methods; however this dataset is sourced from reliable government datasets making it more accurate than other options. Additionally it contains multiple years of data which provides invaluable insight over time trends that would otherwise not be available through other sources

Research Ideas

• Monitoring health outcomes in a given region and comparing changes over time in terms of FQHC locations, services available, and populations served.
• Analyzing the regional distribution of FQHCs and determining whether there are underserved areas based on population density and access to healthcare services.
• Creating a geographic information system (GIS) map to visualize the FQHC locations across the United States, highlighting rural or underserved areas in need of additional support for healthcare access

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: SITE_HCC_FCT_DET.csv | Column name | Description | |:-----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------| | Site Name | Name of the FQHC. (String) | | UDS Number | Unique identifier assigned by the US Department of Human Services for each FQHC. (Integer) | | Site Telephone Number | Telephone number of the FQHC. (String) | | Site Facsimile Telephone Number | Facsimile telephone number of the FQHC. (String) | | **Administrati...

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Introduction

The global Blockchain Technology in Healthcare Market is poised for remarkable growth, anticipated to surge from USD 3.9 billion in 2023 to around USD 750 billion by 2033, reflecting a CAGR of 69.2%. This growth is driven by the technology's potential to enhance security, efficiency, and transparency in healthcare services.

Blockchain technology offers enhanced security and privacy for electronic health records (EHRs). Its decentralized nature allows for secure, tamper-proof storage, significantly reducing risks associated with unauthorized access and data breaches. This secure platform empowers patients by giving them control over their own information, while providing healthcare providers secure and immediate access when necessary.

The technology also revolutionizes data management within the healthcare sector. Blockchain facilitates streamlined health record management, improving the exchange and accessibility of medical data across various providers. This enhanced coordination aids in reducing medical record redundancies and inaccuracies, ultimately leading to better patient care management.

In the realm of research and clinical trials, blockchain provides a robust framework that supports data integrity, traceability, and transparency. This ensures the authenticity and reliability of clinical research findings and offers all stakeholders access to consistent, unalterable data. Additionally, blockchain improves supply chain transparency, ensuring the authenticity of drugs and medical supplies by enabling precise tracking of their origin, handling, and distribution.

Furthermore, blockchain technology can significantly streamline the processing of insurance claims by automating verification processes and reducing fraudulent claims. This not only expedites the claims process but also reduces operational costs for healthcare insurers. The technology also enhances interoperability among healthcare systems, facilitating seamless and secure data exchanges that are crucial for integrating various digital health applications and services.

Recent developments highlight the ongoing integration of blockchain in healthcare. In April 2023, Patientory Inc. launched two blockchain-based solutions aimed at improving health data management and monetization. Additionally, in March 2023, Guardtime collaborated with major pharmaceutical companies on a project to facilitate value-based rebate agreements using blockchain, enhancing the integrity and privacy of transactions. These initiatives underline the transformative impact of blockchain on the healthcare sector, suggesting a promising future for its broader adoption.

U.S. Pandemic Mental Health Care

Impact on Households in Previous 4 Weeks

By US Open Data Portal, data.gov [source]

About this dataset

This U.S. Household Pandemic Impacts dataset assesses the mental health care that households in America have been receiving over the past four weeks during the Covid-19 pandemic. Produced by a collaboration between the U.S. Census Bureau, and five other federal agencies, this survey was designed to measure both social and economic impacts of Covid-19 on American households, such as employment status, consumer spending trends, food security levels and housing disruptions among other important factors. The data collected was based on an internet questionnaire which was conducted through emails and text messages sent to randomly selected housing units from across America linked with email addresses or cell phone numbers from the Census Bureau Master Address File Data; all estimates comply with NCHS Data Presentation Standards for Proportions. Be sure to check out more about how U.S Government Works for further details!

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How to use the dataset

This dataset can be useful to examine the impact of the Covid-19 pandemic on access to and utilization of mental health care by U.S. households in the last 4 weeks.

By studying this dataset, you can gain insight into how people’s mental health has been affected by the pandemic and identify trends based on population subgroups, states, phases of the survey and more.

Instructions for Use: - To get started, open up ‘csv-1’ found in this dataset. This file contains information on access to and utilization of mental health care by U.S households in the last 4 weeks, broken down into 14 different columns (e.g., Indicator, Group, State).
- Familiarize yourself with each column label (e.g., Time Period Start Date), data type (e

Research Ideas

• Analyzing the impact of pandemic-induced stress on different demographic groups, such as age and race/ethnicity.
• Comparing the mental health care services received in different states over time.
• Investigating the correlation between socio-economic status and access to mental health care services during Covid-19 pandemic

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: csv-1.csv | Column name | Description | |:---------------------------|:-------------------------------------------------------------------| | Indicator | The type of indicator being measured. (String) | | Group | The group (by age, gender or race) being measured. (String) | | State | The state where the data was collected. (String) | | Subgroup | A narrower level categorization within Group. (String) | | Phase | Phase number reflective of survey iteration. (Integer) | | Time Period | A label indicating duration captured by survey period. (String) | | Time Period Label | A label indicating duration captured by survey period. (String) | | Time Period Start Date | Beginning date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Time Period End Date | End date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Value | The value of the indicator being measured. (Float) | | LowCI | The lower confidence interval of the value. (Float) | | HighCI | The higher confidence interval of the value. (Float) | | Quartile Range | The quartile range of the value. (String) | | Suppression Flag | A f...

BackgroundSince the site of human subjects research has public health, regulatory, ethical, economic, and social implications, we sought to determine the global distribution and migration of clinical research using an open-access trial registry.MethodsWe obtained individual clinical trial data including location of trial sites, dates of operation, funding source (United States government, pharmaceutical industry, or organization), and clinical study phase (1, 1/2, 2, 2/3, or 3) from ClinicalTrials.gov. We used the World Bank's classification of each country's economic development status ["High Income and a Member of the Organization for Economic Co-operation and Development (OECD)", "High Income and Non-Member of the OECD", "Upper-Middle Income", "Lower-Middle Income", or "Low Income"] and United Nations Populations Division data for country-specific population estimates. We analyzed data from calendar year 2006 through 2012 by number of clinical trial sites, cumulative trial site-years, trial density (trial site-years/106 population), and annual growth rate (%) for each country, and by development category, funding source, and clinical study phase.ResultsOver a 7-year period, 89,647 clinical trials operated 784,585 trial sites in 175 countries, contributing 2,443,850 trial site-years. Among those, 652,200 trial sites (83%) were in 25 high-income OECD countries, while 37,195 sites (5%) were in 91 lower-middle or low-income countries. Trial density (trial site-years/106 population) was 540 in the United States, 202 among other high-income OECD countries (excluding the United States), 81 among high-income non-OECD countries, 41 among upper-middle income countries, 5 among lower-middle income countries, and 2 among low-income countries. Annual compound growth rate was positive (ranging from 0.8% among low-income countries to 14.7% among lower-middle income countries) among all economic groups, except the United States (-0.5%). Overall, 29,191 trials (33%) were funded by industry, 4,059 (5%) were funded by the United States government, and 56,397 (63%) were funded by organizations. Countries with emerging economies (low- and middle-income) operated 19% of phase 3 trial sites, as compared to only 6% of phase 1 trial sites.ConclusionHuman clinical research remains concentrated in high-income countries, but operational clinical trial sites, particularly for phase 3 trials, may be migrating to low- and middle-income countries with emerging economies.

The Food Access Research Atlas presents a spatial overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility, provides food access data for populations within census tracts, and offers census-tract-level data on food access that can be downloaded for community planning or research purposes.

Public government dataset 2019

Variable lookup file has explanations for fields Data is located in Data file

U.S. Health Insurance State-Based Coverage

Market Coverage, County, and Issuer Information by State

By Carl V. Lewis [source]

About this dataset

This dataset contains information about health insurance state-based marketplaces in the United States, collated from data received from the National Association of Insurance Commissioners (NAIC). With this dataset, you’ll learn about the various plans available in each state and gain insight into their features. It provides a comprehensive snapshot of all aspects of each service area – its service area name, year of coverage, state code and county FIPS code assignment, issuer ID number details, source name identification and version numbers. Additionally, it includes import dates for each entity’s participation as well as an indication if it covers an entire state or subset counties. Additionally this data set includes details on certain plans market coverage such as stand-alone dental plan status. This data has been organized for easy accessibility for researchers to quickly access every relevant field needed to analyze health insurance markets in different states. It is also important to note that many states have provided an opportunity to review and verify this data prior to publication. Herein lies a great opportunity; use this vital data set today with confidence!

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How to use the dataset

The U.S. Health Insurance State-Based Marketplace coverage data from 2016 provides detailed information about health insurance plans in the United States in a yearlong period from January 1st, 2016 to December 31st, 2016 for those states that operate their own marketplaces (commonly known as state-based Marketplaces or SBMs). It contains information about service area names, years of coverage, FIPS codes for each county and issuer IDs all across the US that help users get an idea of what plans are available in their area and other areas of interest.

This dataset contains 21 different fields with different pieces of information collected from a variety of sources including the National Association of Insurance Commissioners’ System for Electronic Rate and Form Filing (SERFF) system which contains all rate filings in a state as well as other datasets related to health insurance companies operating within each state. Furthermore it includes partial county justifications and dental only plan categorizations which helps users determine what type(s) of plan they can take advantage when searching for health insurance options with this dataset.

In order to use this dataset you will need some basic knowledge on how to work with spreadsheets such as Microsoft Excel or Google Sheets etc.. You may also find it helpful if you have experience processing data through database programms like MySQL or SQLite depending on how you intend on querying the data extracted from this database set. After downloading the file open up your preferred spreadsheet application and create a new worksheet (or multiple ones). The first step is organizing everything into columns: business year, source name, import date etc.. Once that has been completed you can then begin exploring by sorting through each column individually then cross referencing them against one another based on whatever criteria interests you at that point such as service area name/zip code/county or any combination thereof; taking into account age restrictions related to certain services that vary by jurisdiction amongst multiple parts located within both rural & urban areas within individual states should also be taken into consideration when exploring this data set due to regulations imposed by Medicaid expansion programs enacted across those states mentioned above along with more detailed information generally specific state housed websites associated with SBMs while using the “info” field within this dataset so users can stay informed if any changes occur over time concerning said programs & their respective marketplaces throughout US

Research Ideas

• Developing targeted healthcare campaigns to expand access to insurance plans in counties where they are not yet available.
• Creating custom programs for specific states and counties to encourage enrollment in health insurance programs.
• Generating data-driven insights about which states are covering more or fewer areas of the country and whether there may be disparities among state-level marketplaces when it comes to providing coverage for commonly necessary services or particular age groups or socioeconomic backgrounds

Acknowledgements

If you use this dataset in your research, ...

Proceedings of the National Academy of Sciences of the United States of America (PNAS) Acceptance Rate - ResearchHelpDesk - PNAS has been at the forefront of scientific research for over a century. Established in 1914 as the flagship journal of the US National Academy of Sciences (NAS), PNAS is now one of the largest and most-cited multidisciplinary scientific journals in the world, with a global readership and more than 3,500 research articles published annually. Why Submit to PNAS? Comprehensive scientific coverage PNAS publishes exceptional research in all branches of the Biological, Physical, and Social Sciences. Innovation often happens at the margins, and we are particularly interested in research that crosses disciplinary bounds, answers questions with broad scientific impact, or breaks new ground. Broad scientific audience With one of the largest scientific audiences in the world, PNAS articles reach millions of top researchers each year. Our equitable access and open research programs further our mission to make scientific research accessible to all. Rapid, high-quality peer review PNAS is edited by members of the NAS, a private, nonprofit society of distinguished scholars. Scientists are elected by their peers to membership in the NAS for outstanding contributions to research. Nearly 500 members of the NAS have won Nobel Prizes. The NAS is committed to furthering science in America, and its members are active contributors to the international scientific community. On average, a full review takes just 45 days, and most articles publish within 6 months of submission.