Read reports on priority public health topics including opioid, stimulant, & other substance use, maternal & child health, and racial & health inequities

DATA.HRSA.GOV is the go-to source for data, dashboards, maps, reports, locators, APIs and downloadable data files on HRSA's public health programs, including:

• HRSA-funded Health Center grants, grantees, sites, and related primary care programs
• Health Professional Shortage Areas (HPSA) and Medically Underserved Areas/Populations (MUA/P)
• Ryan White HIV/AIDS services, grantees, and providers
• Maternal and Child Health grants (Title V, Home Visiting, Healthy Start)
• National Health Service Corps (NHSC), Nurse Corps, and other workforce loan repayment/scholarship programs
• Grants for workforce training programs in medicine, nursing, dentistry, and public health
• Grants for rural health programs
• Organ donation

DATA.HRSA.GOV allows you to search by topic area, by geography, and by tool.

Objective The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health r..., Aggregated data from 2020-05-16 to 2022-01-17 regarding Bordeaux Hospital EHR. Bordeaux hospital data warehouse was used, during the pandemic, to describe the current state of the epidemic at the hospital level on a daily basis. Those data were then used in the forecast model including: hospitalizations, hospital and ICU admission and discharge, ambulance service notes and emergency unit notes. Concepts related to COVID-19 were extracted from notes by dictionary-based approaches (e.g. cough, dyspnoea, covid-19). Dictionaries were manually created based on manual chart review to identify terms used by practitioners. Then, the number and proportion of ambulance service calls or hospitalization in emergency units mentioning concepts related to covid-19 were extracted. Due to different data acquisition mechanisms, there was a delay between the occurrence of events and the data acquisition. It was of 1 day for EHR data, 5 days for department hospitalizations and RT-PCR, 4 days for weather, 2..., Data are stored in a .rdata file. Please use R (https://www.r-project.org/) software to open the data.

IntroductionThe emergence of data warehousing in clinical settings has greatly enhanced data analysis capabilities, facilitating the accurate and comprehensive extraction of valuable information. This scoping review explores the contributions of data warehouses in clinical settings by analysing the strengths, challenges and implications of each type of data warehouse, with a particular focus on general and specialised types.MethodsThis scoping review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched four databases (PubMed, CINAHL, Scopus and IEEE-Xplore), identifying peer-reviewed, English-language studies from 1st January 2014 to 1st January 2024, that focus on data warehousing in healthcare, covering either general or specialised data warehouse applications. Python programming was used to extract the search results and transform the data into a tabular format for analysis.ResultsAfter removing 1,194 duplicates, 4,864 unique papers remained. Abstract screening excluded 4,590 as irrelevant, leaving 274 for full-text evaluation. In total, 27 papers met the inclusion criteria, of which 17 focused on general data warehouses and 10 on specialised data warehouses.General data warehouses were found to be primarily used to address data integration issues, particularly for electronic health record (EHR)/ Electronic medical Record (EMR) and general clinical data. These warehouses typically use a star schema architecture with online analytical processing (OLAP) and query analysis capabilities. In contrast, specialised data warehouses were focused on improving the quality of decision support by handling a wide range of data specific to diseases, using specialised architectures and advanced artificial intelligence (AI) capabilities to address the unique and complex challenges associated with these tasks.ConclusionsGeneral purpose data warehouses effectively integrate disparate data sources to provide a comprehensive view of disease management, patient care, and resource management. However, their flexibility and analytical capabilities need improvement. In contrast, specialised data warehouses are gaining popularity for their focus on specific diseases or research purposes, using advanced tools such as data mining and AI for superior analytical performance. Despite their innovative designs, these specialised warehouses face scalability challenges due to their customised nature. Addressing these challenges with advanced analytics and flexible architectures is critical.

The HRSA Data Warehouse is the go-to source for data, maps, reports, locators, and dashboards on HRSA's public health programs. This website provides a wide variety of data on HRSA's programs, including:

• HRSA-funded Health Center grants, grantees, sites, and related primary care programs
• Health Professional Shortage Areas (HPSA) and Medically Underserves Areas/Populations (MUA/P)
• Ryan White HIV/AIDS services, grantees, and providers
• Maternal and Child Health grants (Title V, Home Visiting, Health Start)
• National Health Service Corps (NHSC), Nurse Corps, and other workforce loan repayment/scholarship programs
• Grants for workforce training programs in medicine, nursing, dentistry, and public health
• Grants for rural health programs
• Organ donation
• 340(b) drug discount program

The HRSA Data Warehouse allows you to search by topic area, by geography, and by tool.

The Veterans Health Administration (VHA) is increasingly dependent upon data. Most of its employees generate and use vast amounts of data on a daily basis. To improve our capacity for data analysis while providing the most efficient and the highest quality health care to our Veteran patients, VHA, working with the VA Office of Information and Technology, implemented a health data warehouse. Central to this plan is consolidating data from disparate sources into a coherent single logical data model. The Corporate Data Warehouse (CDW) is the physical implementation of this logical data model at the enterprise level for VHA. Although the CDW initially began to store data as early as 2006, a renewed effort began in 2010 to accelerate CDW's content by including more subject areas from Veterans Health Information Systems and Technology Architecture (VistA) and content from other existing national data systems. CDW supports fully developed subject areas in its production environment as well as supporting rapid prototyping by extracting data directly from source systems with very minor data transformations. The Regional Data Warehouses and the Veterans Integrated Service Network (VISN) Data Warehouses share content from CDW and allow for greater reporting flexibility at the local level throughout the VHA organization.

The amount of global healthcare data is expected to increase dramatically by the year 2020. Despite the growing amount of data, there is not enough storage space to accommodate the data being generated. It is projected that by 2020 there will be 985 exabytes of storage available for healthcare data but there will be 2,314 exabytes of healthcare data generated.

The Health Claims Data Warehouse (HCDW) will receive and analyze health claims data to support management and administrative purposes. The Federal Employee Health Benefits Program (FEHBP) is a $40 billion program covering approximately 8 million eligible participants using more than 100 health insurance carriers. The HCDW will incorporate extensive analytical capabilities to support cost analysis, administration, design, and quality improvement of healthcare services provided to eligible participants.

Healthcare Data Storage Market size was valued at USD 3.97 Billion in 2024 and is projected to reach USD 10.27 Billion by 2032, growing at a CAGR of 13.90% during the forecast period 2026-2032.Global Healthcare Data Storage Market DriversThe market drivers for the Healthcare Data Storage Market can be influenced by various factors. These may include:Growing volume of healthcare data: The amount of data produced by healthcare providers has increased dramatically as a result of the digitalization of medical records. This covers genomic information, medical imaging, electronic health records (EHRs), and more. To handle this data, healthcare institutions need effective and safe storage options.Severe laws and compliance requirements: HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe are two examples of the severe laws that apply to healthcare data. In order to protect patient information, these requirements mandate that healthcare organisations employ secure data storage solutions.Cloud storage is becoming more and more popular since it is affordable, flexible, and scalable, which appeals to healthcare institutions. Adoption is accelerated by cloud storage companies' provision of specialised healthcare cloud solutions that meet legal and regulatory standards.Technological developments: Artificial intelligence (AI), machine learning (ML), and big data analytics are some of the technologies that are revolutionising healthcare. To handle the massive volumes of data collected and analysed, these technologies need reliable data storage systems.Growing need for data interoperability: In order to enhance patient care coordination and results, healthcare providers are placing a greater emphasis on interoperability. This calls for the smooth transfer of medical data between various systems, which calls for trustworthy data storage options.Escalating healthcare expenses: There is pressure on healthcare institutions to save expenses without sacrificing care quality. Healthcare data management and storage operations can be made more cost-effective with the use of efficient data storage solutions.Growing comprehension of data security's significance Healthcare data breaches may result in severe repercussions, such as monetary losses and reputational harm. To safeguard patient data from online dangers, healthcare institutions are investing in secure data storage solutions.

Data Warehousing Market Outlook

According to our latest research, the global data warehousing market size reached USD 29.7 billion in 2024, reflecting robust demand across a range of industries. Driven by the growing need for advanced analytics, real-time data integration, and scalable storage solutions, the market is expected to register a CAGR of 9.2% during the forecast period. By 2033, the market size is projected to reach USD 65.2 billion, underscoring the transformative impact of data-driven decision-making and digital transformation initiatives worldwide. The expansion is propelled by the proliferation of big data, cloud adoption, and the increasing complexity of business operations as organizations strive for enhanced agility and competitiveness.

A significant growth factor for the data warehousing market is the accelerating adoption of cloud-based solutions. Enterprises are increasingly migrating from traditional on-premises data warehouses to cloud-native platforms due to their scalability, cost-effectiveness, and ability to handle vast volumes of structured and unstructured data. The flexibility offered by cloud deployment enables organizations to scale resources dynamically based on workload demands, driving operational efficiencies and reducing capital expenditures. Furthermore, the integration of artificial intelligence and machine learning within cloud data warehouses is empowering businesses to extract actionable insights, automate data management tasks, and support predictive analytics, further fueling market growth.

Another key driver is the surge in demand for advanced analytics and business intelligence tools. As organizations recognize the value of data-driven decision-making, there is a heightened focus on leveraging data warehousing solutions to consolidate disparate data sources, enable real-time analytics, and foster collaboration across business units. The rise of self-service analytics platforms and intuitive data visualization tools is democratizing data access, allowing non-technical users to generate insights independently and accelerating the pace of innovation. Additionally, regulatory compliance and data governance requirements are compelling enterprises to invest in robust data warehousing infrastructure to ensure data accuracy, security, and traceability.

The rapid digital transformation across verticals such as BFSI, healthcare, retail, and manufacturing is also contributing to the expansion of the data warehousing market. In sectors like healthcare and finance, the need for secure, compliant, and high-performance data storage and analytics solutions is paramount due to the sensitive nature of the data involved. Retailers and e-commerce platforms are leveraging data warehousing to personalize customer experiences, optimize inventory management, and enhance supply chain visibility. Meanwhile, manufacturers are utilizing data warehouses to improve operational efficiency, monitor equipment performance, and drive innovation through predictive maintenance and IoT integration.

From a regional perspective, North America continues to dominate the data warehousing market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology vendors, early adoption of advanced analytics, and a strong emphasis on digital transformation among enterprises. Europe follows closely, supported by stringent data privacy regulations and increasing investments in cloud infrastructure. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding digital economies, and government initiatives promoting smart city development and digital governance. Latin America and the Middle East & Africa are also emerging as promising markets, with organizations in these regions gradually embracing data-driven strategies to enhance competitiveness and operational resilience.

Offering Analysis

The data warehousing market by offering is segmented into ETL solutions, data warehouse databases, data warehouse software, and services. ETL (Extract, Transform, L

An export of the Ohio Public Health Data Warehouse, known as Ohio OneSource.http://publicapps.odh.ohio.gov/EDW/DataCatalogThe Ohio Public Health Data Warehouse is a self-service online tool where anyone can obtain the most recent public health data available about Ohio.

                  Citation:
                  Please use the following 

citation in any publication or release which uses or references data from the Warehouse: "These data were provided by the Ohio Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations or conclusions".

Information for authorized public health personnel and IRB-approved researchers to access ODH’s secure data warehouse can be found here. http://publicapps.odh.ohio.gov/EDW/DataBrowser/Browse/OhioOneSourceOhio OneSourceCategory:Data Quality Latest Update:5/21/2018 Description: Find licensed providers. Contact Email: informatics@odh.ohio.govPurpose:This tool is intended to provide a “one stop shop” to search, filter, and extract information for all licensed healthcare facilities within the State of Ohio. Examples of provider types include, but are not limited to the following:

AS - Ambulatory Surgical CenterCI - Correctional InstitutionCL - ClinicCT- Chemical TreatmentDU - Dialysis UnitEM - Emergency Medical ServiceFA - First Aid DepartmentHH - Home Health CareHS - HospitalIM - Imaging / DiagnosticLA - LaboratoryMG - Medical Gas ServicesMH - Mental HealthNH - Nursing HomePC - Practitioner CorporationPMC - Pain Management ClinicPS - Pharmacy ServicesPT - Physical TherapyTE - TeachingUR - Urgent Care
*Data for this facility lookup tool is provided by the Ohio Board of Pharmacy and the Ohio Department of Health.

Web based Data Warehouse disemminating typing data to the regions and Health Protection Unit

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). To support ongoing assessment of the effectiveness of the Health Home model, the CMS has established a recommended Core Set of health care quality measures that it intends to promulgate in the rulemaking process. The data used in the Health Home Quality Measures are taken from the following sources: • Medicaid Data Mart: Claims and encounters data generated from the Medicaid Data Warehouse (MDW). • QARR Member Level Files: Sample of the health plan eligible member’s quality. • New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse: Claims and encounters data generated from the Medicaid Data Warehouse (MDW).

Please refer to the Overview document for additional information.

This dataset provides summarized data for all expenditures from July 2003 through the current fiscal year, year to date, from the State's central accounting system. The state fiscal year runs from July 1 to the following June 30 and is numbered for the calendar year in which it ends. The State of Iowa operates on a modified accrual basis which provides that encumbrances on June 30 must be paid within 60 days after year end. The expenditures are summarized by Fiscal Year, Month, Fund, Appropriation, Department, Unit, and Object Class.

The global Data Warehousing Software market is experiencing robust growth, driven by the increasing need for businesses to analyze large volumes of data for improved decision-making. The market, estimated at $50 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of big data technologies requiring efficient warehousing solutions, and the growing demand for advanced analytics capabilities across various industries. The BFSI (Banking, Financial Services, and Insurance) sector remains a dominant adopter, leveraging data warehousing for risk management, fraud detection, and customer relationship management. However, growth is also significant in other sectors such as healthcare (for patient data analysis), government and education (for performance tracking and resource allocation), and manufacturing and distribution (for supply chain optimization). The competitive landscape is marked by established players like IBM, Microsoft, and Oracle, alongside emerging cloud-native providers like Snowflake, actively innovating and expanding their offerings. The continued growth in the data warehousing software market is expected to be further propelled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) for data analysis. This integration allows businesses to derive more valuable insights from their data, leading to better strategic decision-making and improved operational efficiency. While the market faces challenges such as data security concerns and the complexity of implementing and managing large-scale data warehousing systems, the overall outlook remains optimistic. The increasing availability of skilled professionals and ongoing technological advancements are mitigating these restraints, ensuring the sustained growth trajectory of the data warehousing software market over the forecast period. Specific regional growth will vary, with North America and Europe initially holding larger market shares due to established technological infrastructure and adoption rates, but rapid growth is anticipated in Asia Pacific, driven by increasing digitalization and economic development.

Data Warehouse Market size was valued at USD 27.68 Billion in 2024 and is projected to reach USD 63.9 Billion by 2032, growing at a CAGR of 11% from 2026 to 2032.

Key Market Drivers: Increasing Volume of Data Generated across Industries: The exponential expansion of data generation is increasing the demand for robust data warehouse solutions. According to the International Data Corporation (IDC), the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This tremendous rise in data volume demands sophisticated data warehousing capabilities to ensure efficient storage, administration, and analysis.

Growing Adoption of Cloud-based Data Warehousing: The shift to cloud-based solutions is a significant driver of the Data Warehouse Market.

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

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According to Cognitive Market Research, the global healthcare data storage market size is USD 5.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 14.3% from 2024 to 2031. Market Dynamics of Healthcare Data Storage Market

Key Drivers for Healthcare Data Storage Market

Increasing amount of healthcare records- Healthcare data storage market is in high demand due to the increasing amount of healthcare data. Electronic health records (EHRs), medical imaging, wearable electronics, and health applications all contribute to the daily deluge of data generated and amassed by healthcare institutions. This data includes a wide range of information, including patients’ medical records, diagnostic pictures, treatment programs, health indicators in real-time, and more. Moreover, healthcare data storage systems are necessary for efficient management of such vast data sets because they can manage high volumes, provide fast retrieval, and keep data secure. Further, state-of-the-art storage systems are required for compliance with data retention and security regulations. Thus, in order to facilitate better patient care and operational efficiency, the ever-increasing volume of healthcare data is driving the use of advanced data storage technologies.
The market is being propelled by the demand for efficient and rapid access to patient data in order to enhance clinical decision-making and patient care.

Key Restraints for Healthcare Data Storage Market

Healthcare data storage market growth is hindered due to the high costs of implementation and upkeep.
The market expansion is being impeded by concerns about data breaches and data accessibility.

Introduction of the Healthcare Data Storage Market

Healthcare data storage describes the infrastructure and procedures put in place to keep and handle massive volumes of patient records safely. Complying with regulatory requirements while ensuring data integrity, confidentiality, and accessibility is essential for healthcare data storage solutions. The rising amount of digital data produced by healthcare companies, the convenience and speed with which cloud storage solutions can be implemented, and the increasing popularity of hybrid data storage solutions are the primary elements propelling the expansion of this market. Security concerns over cloud-based image processing and analytics, however, are limiting the company’s growth. Concerns about the security of cloud-based image processing and analytics are expected to dampen the worldwide healthcare data storage industry. Additionally, advancements in artificial intelligence, big data analytics, and cloud computing have greatly improved the efficiency and capacity of the healthcare data storage market.

Data Warehouse as a Service Market Outlook

According to our latest research, the global Data Warehouse as a Service (DWaaS) market size reached USD 7.3 billion in 2024. The market is expected to grow at a robust CAGR of 22.1% during the forecast period, with projections indicating it will attain USD 54.7 billion by 2033. The primary growth factors driving this expansion include the escalating demand for scalable data storage, the proliferation of cloud-based services, and the surging need for advanced analytics across industries. As organizations increasingly seek agile, cost-effective solutions for managing vast volumes of data, DWaaS is emerging as a critical pillar in the modern enterprise data infrastructure landscape.

One of the most significant growth drivers for the Data Warehouse as a Service market is the accelerating adoption of cloud computing across both large enterprises and small and medium-sized enterprises (SMEs). Organizations are shifting away from traditional on-premises data warehouses due to the high upfront costs, complex maintenance, and limited scalability. DWaaS offers a compelling alternative by providing flexible, pay-as-you-go models and eliminating the need for significant capital expenditure. The ability to rapidly scale storage and compute resources in response to fluctuating business needs, combined with managed service offerings, enables companies to focus on deriving business value from their data rather than managing infrastructure. Moreover, the integration of advanced features such as real-time analytics, artificial intelligence, and machine learning within DWaaS platforms is further enhancing their appeal, enabling businesses to unlock actionable insights at unprecedented speed and scale.

Another key factor fueling the expansion of the DWaaS market is the exponential growth of data generated by digital transformation initiatives, IoT devices, and omnichannel customer interactions. Enterprises are under increasing pressure to harness this data for competitive advantage, making robust, scalable, and secure data warehousing solutions indispensable. DWaaS platforms enable seamless data integration from disparate sources, facilitate advanced analytics, and support compliance with evolving data privacy regulations. The rising adoption of data-driven decision-making across sectors such as BFSI, healthcare, retail, and government is further driving demand for sophisticated data warehousing solutions. Additionally, the trend towards remote work and global collaboration is amplifying the need for cloud-based data access, further boosting the market.

In addition to technological and business drivers, the evolving regulatory landscape is shaping the DWaaS market. Organizations face increasing scrutiny regarding data protection, privacy, and compliance, especially in highly regulated sectors like finance and healthcare. DWaaS providers are responding by investing heavily in security features, compliance certifications, and data governance capabilities. This focus on secure, compliant data warehousing is not only mitigating risk for enterprises but also expanding the addressable market for DWaaS solutions. As a result, vendors that can demonstrate robust security, data sovereignty, and regulatory compliance are gaining a competitive edge, fueling market growth.

Regionally, North America continues to dominate the DWaaS market, driven by the presence of major technology vendors, early adoption of cloud technologies, and a high concentration of data-centric enterprises. However, the Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, increasing cloud adoption, and significant investments in IT infrastructure. Europe is also witnessing strong uptake, particularly in sectors such as BFSI and healthcare, where regulatory compliance and data security are paramount. Latin America and the Middle East & Africa are gradually increasing their adoption rates, supported by digital transformation initiatives and growing awareness of the benefits of DWaaS.

Deployment Model Analysis

&

BackgroundHealthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner.MethodsThe MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources.ResultsA novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics.ConclusionsThe MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.