The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

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).

This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and the rising need for efficient health information management (HIM) systems. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors: the increasing digitization of healthcare, the growing demand for data-driven insights to improve patient care and operational efficiency, and the expanding adoption of cloud-based solutions offering scalability and accessibility. Pharmaceutical companies and academic/research institutions are significant drivers, leveraging these systems for drug discovery, clinical trials management, and advanced research initiatives. However, challenges such as data security concerns, high implementation costs, and the need for robust interoperability between different systems pose restraints to market growth. The market is segmented by software type (EHR, HIM) and application (pharmaceutical companies, academic institutions, others), providing diverse opportunities for specialized vendors. Geographic expansion continues, with North America and Europe currently holding significant market share, but growth is anticipated across Asia-Pacific and other regions as healthcare infrastructure modernizes. The competitive landscape is dynamic, with established players like NextGen Healthcare and emerging companies like Pabau and EHR Your Way vying for market share. The success of individual vendors depends on factors including the scalability of their solutions, the depth of their data analytics capabilities, and the strength of their customer support network. The market's trajectory is heavily influenced by government regulations regarding data privacy and interoperability, the ongoing evolution of healthcare technology, and the increasing focus on personalized medicine. Further growth is likely to be seen in areas such as AI-powered diagnostics, predictive analytics, and advanced data visualization tools integrated within medical databases.

ONC uses the SK&A Office-based Provider Database to calculate the counts of medical doctors, doctors of osteopathy, nurse practitioners, and physician assistants at the state and count level from 2011 through 2013. These counts are grouped as a total, as well as segmented by each provider type and separately as counts of primary care providers.

The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.

The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.

The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.

NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.

Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm

The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.

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.

A 10,000-patient database that contains in total 10,000 virtual patients, 36,143 admissions, and 10,726,505 lab observations.

The rationale for developing the EU HCCD for use in Health Technology Assessment (HTA) across countries is to provide a common dataset of international costs, which can feed into health economic evaluations carried out by transferring economic evaluation analysis and models across countries. Defining a core dataset of costs for use in HTA across countries enables analyses that try to understand the variation in costs within and across countries (taking into account the differences between the healthcare systems and other factors). Additionally, it makes it easier to carry out multi-country studies and to adapt economic evaluation studies from country to country by saving human resources time (and consequently costs) in the task of looking for healthcare costs.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. 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.

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.

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The State Inpatient Databases (SID) are part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The SID are a set of hospital databases containing the universe of the inpatient discharge abstracts from participating States, translated into a uniform format to facilitate multi-State comparisons and analyses. The SID can be used to investigate questions and identify trends unique to one state, to compare data from two or more states, and to conduct market area research or small area variation analyses. Data may not be available for all states across all years.

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and health information management (HIM) systems across healthcare providers. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The rising prevalence of chronic diseases necessitates efficient data management for better patient care and research. Furthermore, government initiatives promoting digital healthcare and interoperability are accelerating the adoption of these systems. The shift towards value-based care models requires sophisticated data analytics capabilities offered by medical database software, further boosting market demand. Technological advancements, such as cloud-based solutions and artificial intelligence (AI) integration, are enhancing data security, accessibility, and analytical capabilities, driving market growth. The market segmentation reveals strong growth across both EHR and HIM systems, with EHR systems currently dominating due to broader adoption. Major players like NextGen Healthcare, Epic (implied based on industry knowledge), and Cerner (implied based on industry knowledge) are actively innovating and expanding their market share through strategic partnerships and acquisitions. Regional analysis shows North America currently holding the largest market share, followed by Europe and Asia Pacific, with emerging markets in Asia Pacific expected to demonstrate rapid growth in the coming years. The market is not without its challenges. Data security and privacy concerns remain a significant restraint, necessitating robust security measures and compliance with regulations like HIPAA. High implementation and maintenance costs can hinder adoption, especially for smaller healthcare providers. Integration complexities with existing legacy systems can also pose a challenge. However, the long-term benefits of improved patient care, enhanced operational efficiency, and valuable data-driven insights are likely to outweigh these challenges, ensuring continued market expansion throughout the forecast period. The market is expected to reach approximately $45 billion by 2033, driven by ongoing technological advancements, increasing regulatory pressures for digital health adoption, and a growing need for efficient and secure healthcare data management.

The State Emergency Department Databases (SEDD) are part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The SEDD are a set of databases that capture discharge information on all emergency department visits that do not result in an admission. The SEDD combined with SID discharges that originate in the emergency department are well suited for research and policy questions that require complete enumeration of hospital-based emergency departments within market areas or states. Data may not be available for all states across all years.

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

Technical notes and documentation

The Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database) is a database of Spanish healthcare unit costs estimated from different national sources.

Version 3.0 of the CONCEPT-COSTS Database contains costs estimates (expresed in EUR 2024) for a core set of service items commonly used in the chronic health problems evaluated in CONCEPT Project.

It is a living document planned to be regularly updated and expanded in terms of the covered service over time.

Aims of CONCEPT-COSTS project:

CONCEPT-COSTS is part of the coordinated CONCEPT Project, which comprises four subprojects whose objective is to analyse the effectiveness and efficiency of care pathways (CP) in three chronic health problems of high prevalence and socioeconomic impact, which are diabetes mellitus type 2, breast cancer and ischemic stroke. As a common denominator, CONCEPT shares the innovative perspective of focusing its analysis on CP as a key determinant of healthcare adequacy, adherence to treatment, health outcomes and economic consequences. CONCEPT-COSTS' first objective is to complement the results produced by each CONCEPT clinical cohort, with a broad proposal of economic analyses based on real-world data (RWD), including incurred costs, avoidable costs and efficiency evaluation of identified CP. These results will be used to inform the clinical and management decisions about those CP to be promoted or avoided. As a second objective, CONCEPT-COSTS will identify the ethodological and logistical challenges faced by economic evaluations based on RWD, to develop a framework that will include recommendations for improvements related to feasibility, validity and transferability of results.

Files included in this publication:

• CONCEPT_COSTS_Database_v3.csv
• CONCEPT_COSTS_Database_v3.html
• Readme_v3.doc

What's new

• Costs updated to 2024
• Some sources updated

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.

Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries. Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.