A 2023 report on data breaches in the healthcare system in the United States revealed that in most incidents, the leaked data was located in the network server, with almost 70 percent of data breaches indicating this location. The second-most common location of breached data was e-mail, with over 18 percent of the cases, followed by paper or films, with nearly six percent of the cases.

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Big Data Analytics in Healthcare Market Outlook

The global market size for Big Data Analytics in Healthcare was valued at approximately USD 34 billion in 2023 and is anticipated to grow at a robust CAGR of 11.9%, reaching an estimated USD 90 billion by 2032. This remarkable growth is driven by the increasing adoption of data-driven decision-making processes within the healthcare sector, spurred by the mounting pressure to enhance operational efficiencies, improve patient outcomes, and reduce overall healthcare costs. The integration of big data analytics within healthcare systems is enabling organizations to leverage vast amounts of data, leading to enhanced patient care and streamlined operations.

A significant growth factor fueling the expansion of the big data analytics market in healthcare is the ever-increasing volume of data generated by healthcare systems. With the surge of electronic health records, wearable health devices, and various other digital health technologies, the volume of data being generated is unprecedented. This data, if analyzed correctly, holds the potential to transform healthcare delivery models, allowing for more precise diagnostics, personalized treatment plans, and proactive disease management strategies. Consequently, healthcare organizations are increasingly investing in big data analytics tools to harness this data for clinical and operational improvements.

Another key driver of market growth is the growing emphasis on value-based care and the need for healthcare providers to demonstrate high-quality patient outcomes. Value-based care models require providers to focus on the quality rather than the quantity of care delivered, inherently demanding the use of advanced analytics to derive actionable insights from patient data. Big data analytics facilitates the identification of patterns and trends that can lead to improved treatment effectiveness and patient satisfaction. This shift in care models is prompting healthcare organizations to integrate sophisticated analytics solutions that help in predictive modeling, trend analysis, and real-time decision-making, further propelling market expansion.

Additionally, the increasing incidence of chronic diseases worldwide is driving the need for more efficient healthcare services. Big data analytics in healthcare can play a critical role in managing chronic diseases by enabling preventive care and personalized treatment plans. By analyzing patient data, including historical health records, genetic information, and lifestyle choices, healthcare providers can predict potential health issues and intervene early, thereby improving patient outcomes and reducing healthcare costs. This capability is essential in managing the global burden of chronic diseases, thereby boosting the adoption of big data analytics solutions in the healthcare sector.

Regionally, North America dominates the market due to the presence of advanced healthcare infrastructure, the availability of technologically advanced products, and the high adoption rate of healthcare IT solutions. The region's robust regulatory environment and substantial investments in healthcare IT make it a fertile ground for the growth of big data analytics solutions. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by increasing government initiatives supporting the digitization of healthcare, burgeoning healthcare infrastructure, and a growing focus on precision medicine. The integration of big data analytics in healthcare across diverse regions is indicative of its global importance in optimizing healthcare delivery and patient care.

Component Analysis

In the realm of big data analytics in healthcare, the component segment is vitally instrumental to the market's evolution and includes software and services. Software solutions are the backbone of big data analytics, providing healthcare organizations with the necessary tools to collect, process, and analyze vast datasets. These solutions encompass data management and analytical platforms, which are indispensable for extracting actionable insights from disparate data sources. The software component is continually evolving with advancements in artificial intelligence and machine learning, which enhance data analytics capabilities. Moreover, the increasing demand for user-friendly, customizable software solutions is driving innovation and growth within this segment.

The services component, on the other hand, plays a critical role in the implementation and maintenance of big data analytics solutions. This component includes cons

The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.

The National Health Data System (SNDS) will make it possible to link:

• health insurance data (SNIIRAM database);
• hospital data (PMSI database);
• the medical causes of death (base of the CépiDC of Inserm);
• disability-related data (from MDPH - CNSA data);
• a sample of data from complementary health insurance organisations.

The first two categories of data are already available and constitute the first version of the SNDS. The medical causes of death should feed the SNDS from the second half of 2017. The first data from the CNSA will arrive from 2018 and the sample of complementary organizations in 2019.

The purpose of the SNDS is to make these data available in order to promote studies, research or evaluations of a nature in the public interest and contributing to one of the following purposes:

• health information;
• the implementation of health policies;
• knowledge of health expenditure;
• informing professionals and establishments about their activities;
• innovation in the fields of health and medico-social care;
• monitoring, surveillance and health security.

Definitions and data sources for quality domains and subdomains.

Since 1 July 2018, in Austria, members of health care and nursing professions and higher medical-technical services are required to enlist in the register of healthcare professions.

Specific data from this healthcare professional register are publicly accessible to provide an overview of the existing health professions and the respective professionals with their basic qualifications.

Heterogenous Big dataset is presented in this proposed work: electrocardiogram (ECG) signal, blood pressure signal, oxygen saturation (SpO2) signal, and the text input. This work is an extension version for our relevant formulating of dataset that presented in [1] and a trustworthy and relevant medical dataset library (PhysioNet [2]) was used to acquire these signals. The dataset includes medical features from heterogenous sources (sensory data and non-sensory). Firstly, ECG sensor’s signals which contains QRS width, ST elevation, peak numbers, and cycle interval. Secondly: SpO2 level from SpO2 sensor’s signals. Third, blood pressure sensors’ signals which contain high (systolic) and low (diastolic) values and finally text input which consider non-sensory data. The text inputs were formulated based on doctors diagnosing procedures for heart chronic diseases. Python software environment was used, and the simulated big data is presented along with analyses.

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2031, growing at a CAGR of 13.92% during the forecast period 2024-2031.

Global Real World Evidence Solutions Market Drivers

The market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:

Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations. Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE. Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions. Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records. Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development. Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences. Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

This statistic displays the results of a survey on the main type of sources used for healthcare information in Brazil in 2018. According to data provided by Ipsos, 50 percent of Brazilian respondents claimed to receive health information from their doctor or other healthcare professionals, while 35 percent use online search engines to find such information.

Register of Health Care Providers is the basic national database
on health care system, medical staff and other health care employees. It is intended for planning and monitoring the public health service network, planning and monitoring the movement of health personnel, and implementation of health care and health insurance systems. It serves as a register of individual groups of medical staff, separately
doctors, dentists, pharmacists and private health professionals.

Epidemiological data sources.

The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). 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. The SID contain clinical and resource-use information that is included in a typical discharge abstract, 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, admission and 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’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. Sources: HealthCare.gov application and policy data through October 6, 2024, HealthCare.gov inbound account transfer data through November 7, 2024, and T-MSIS Analytic Files (TAF) through July 2024 (TAF version 7.1). The table includes states that use HealthCare.gov. Notes: This table includes Marketplace consumers who submitted a HealthCare.gov application from March 6, 2023 - October 6, 2024 or who had an inbound account transfer from April 3, 2023 - November 7, 2024, who can be linked to an enrollment record in TAF that shows a last day of Medicaid or CHIP enrollment from March 31, 2023 - July 31, 2024. Beneficiaries with a leaving event may have continuous coverage through another coverage source, including Medicaid or CHIP coverage in another state. However, a beneficiary that lost Medicaid or CHIP coverage and regained coverage in the same state must have a gap of at least 31 days or a full calendar month. This table includes Medicaid or CHIP beneficiaries with full benefits in the month they left Medicaid or CHIP coverage. ‘Account Transfer Consumers Whose Medicaid or CHIP Coverage was Terminated’ are consumers 1) whose full benefit Medicaid or CHIP coverage was terminated and 2) were sent by a state Medicaid or CHIP agency via secure electronic file to the HealthCare.gov Marketplace in a process referred to as an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. 'Marketplace Consumers Not on Account Transfer Whose Medicaid or CHIP Coverage was Terminated' are consumers 1) who applied at the HealthCare.gov Marketplace and 2) were not sent by a state Medicaid or CHIP agency via an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. Marketplace consumers counts are based on the month Medicaid or CHIP coverage was terminated for a beneficiary. Counts include all recent Marketplace activity. HealthCare.gov data are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. HealthCare.gov data are through Sunday, October 6. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: The percentages for the 'Marketplace Consumers Not on Account Transfer whose Medicaid or CHIP Coverage was Terminated' data record group are marked as not available (NA) because the full population of consumers without an account transfer was not available for this report. Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data about 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in November 2023, data from November 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through October 2023. APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; QHP: Qualified Health Plan; NA: Not Available

Made available through Socrata COVID-19 Plugin via API.

From the source Web site: This dataset is intended to be used as a baseline for understanding the typical bed capacity and average yearly bed utilization of hospitals reporting such information. The date of last update received from each hospital may be varied. While the dataset is not updated in real-time, this information is critical for understanding the impact of a high utilization event, like COVID-19.

Data source: https://coronavirus-resources.esri.com/datasets/1044bb19da8d4dbfb6a96eb1b4ebf629_0?geometry=49.394%2C-16.820%2C-74.356%2C72.123

Definitive Healthcare is the leading provider of data, intelligence, and analytics on healthcare organizations and practitioners. In this service, Definitive Healthcare provides intelligence on the numbers of licensed beds, staffed beds, ICU beds, and the bed utilization rate for the hospitals in the United States.

28 November 2019. Following user and stakeholder consultation, we made several revisions to our data processing and methodology and revised all figures from September 2015 to December 2018 in the General Practice Workforce December 2018 publication. Following later changes to the September 2015 to December 2016 full-time equivalent (FTE) GP locum figures, we revised all affected locum figures in the General Practice Workforce September 2019 publication. The figures in this publication are no longer valid as they were calculated using the previous methodology and therefore have now been superseded. More information and the revised figures can be found on the General Practice Workforce September 2019 publication page at https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services/final-30-september-2019. This report presents data about GPs, Nurses, Direct Patient Care and Admin/Non-Clinical staff working in General Practice in England, along with information on their patients, practice and the services they provide. This is a quarterly publication and includes final data from September 2015 to September 2018. Final December 2018 data will be available in February 2019. CHANGE NOTICE: From the June 2018 collection, the source for GP Registrars (foundation and specialty registrar trainees on placements in General Practice) changed. The new data source is the Health Education England (HEE) Trainee Information System (TIS). This has improved the quality of our Registrar data and removes the need for a provisional data release. This publication contains June 2018 and September 2018 data based on the change in data source, with information prior to June 2018 using the previous source of ESR data. CONSULTATION: Further information on the new data source for GP registrars and other proposed improvements to the General Practice Workforce publication are discussed in our "Methodological Change Notice" available under Resources. We are continually looking to improve the quality of the data in this series to make them more useful for our users and we welcome any feedback on these proposed changes to gp-data@nhs.net, by the 20th January 2018. Various data breakdowns are available in the accompanying Excel and CSV files, including time series and breakdowns by categories such as age and gender. Data is also presented regionally and at practice level in the accompanying CSVs. This publication also features an online interactive dashboard which allows users to explore the underlying data in a variety of ways. This can be accessed by clicking on the dashboard icon below. Links to other publications presenting healthcare workforce information can be found under Related Links.

Healthcare Provider/Professional Data contains the data of individual providers and facilities, including their information about opening hours, insurance networks, specialties, NPI, etcetera. In addition to discovering data sources, merging data, running analytics, and receiving decision-making guidance, the bigger problem is responding to marketplace business and patient care demands in a timely manner. Pharmacy contains the location details of pharmacies and has attributes such as addresses, opening hours, facilities, etcetera.

A. Usecase/Applications possible with the data:

a. Provider network data systems (PNDS) - The primary goal of the PNDS is to collect data needed to evaluate provider networks, which include physicians, hospitals, labs, home health agencies, durable medical equipment providers, and so on, for all types of Health Insurers. Such information can be used to:

b. Find health care providers in my network - Use this directory to easily find other providers in my network.

c. Comprehensive services assessment - Determine whether insurers have contracted with a sufficient number of primary care practitioners, clinical specialists, and service facilities (hospitals, labs, etc.) within the insurer's service area.

d. Capacity analysis - Calculate the potential capacity of a managed care plan’s primary care providers.

e. Locate pharmacies in your local areas.

f. Support Employee Benefits Decisions - Having access to network data can help you make better decisions about which providers to use for Employee Medical Benefits.

g. Know about the facilities available across different pharmacies.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

IMPORTANT NOTICE This item has moved to a new organization and will be entering Mature Support in Fall 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada.

The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).To access the hosted (downloadable) version of the dataset, go to https://services.arcgis.com/zmLUiqh7X11gGV2d/ArcGIS/rest/services/Open_Database_of_Healthcare_Facilities_Canada_(Hosted)/FeatureServer/0Version: 1.1 (May to June 2020)Data sources and methodology

The inputs for the ODHF are datasets whose sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. These datasets were available either under one of the various types of open data licences, e.g., in an open government portal, or as publicly available data. In certain cases, data were obtained directly from administrative sources. Details of the sources used are available in the ODHF metadata.

The data sources used do not deploy a uniform classification system. The ODHF harmonizes facility type by assigning one of three types to each health facility. This was done based on the facility type provided in the source data as well as using other research carried out for the purpose. The 3 facility types used in the ODHF include:Ambulatory Health Care ServicesHospitalsNursing and Residential Care FacilitiesHowever, alternative medicine (e.g., herbalists) and specialist areas (e.g., chiropractors, dentists, mental health specialists, etc.) are not in scope for the current ODHF version (version 1.1).

The ODHF does not assert having exhaustive coverage and may not contain all facilities in scope for the current version. While efforts have been made to minimize these, facility type classification and geolocation errors are also possible. While all data are released on the same date, the dates as of which data are current depends on the update dates of the sources used.

A subset of geo-coordinates available in the source data were validated using the internet and updated as needed. When latitude and longitude were not available, geocoding was performed for some sources using address data in the source. Some coordinates were also removed from the original sources when it was determined they were derived from postal codes or other aggregate geographic areas as opposed to street address.

Deduplication was done to remove duplicates for cases where sources overlapped in coverage.

This current version of the database (version 1.1) contains approximately 7,000 records. Data were collected by accessing sources between November 2019 and March 2020 for the initial release, with additional data collected or otherwise updated from May to June 2020 for version 1.1.

The variables included in the ODHF are as follows:

Index Facility Name Source Facility Type ODHF Facility Type Provider Unit Street Number Street Name Postal Code City Province or Territory Source-Format Street Address Census Subdivision Name Census Subdivision Unique Identifier Province or Territory Unique Identifier Latitude Longitude

For more information on how the addresses and variables were compiled, see the metadata that accompanies the ODHF.

This is a republishing of the data that is freely available from Statistics Canada at https://www.statcan.gc.ca/eng/lode/databases/odhf. Records that did not have a latitude and longitude value (about 484) were geocoded using the Esri World Geocoder. For more information on this data set please review the Statistics Canada metadata document.

Update Frequency: As needed

At a time of digital transformation, correlating as much relevant data as possible can provide a powerful lever in the fight against fraud. Focusing on the issue of the sources of this data, it appears that 73 percent of the players in the French healthcare ecosystem who responded to the survey in 2017 placed their partners and peers as the primary source of data collection. It was also found that open data occupied an equivalent place to data obtained from patients, clients and insured persons.