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.

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 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).

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

CompanyData.com, (BoldData), is your gateway to verified global business intelligence. Our Healthcare Company Database provides in-depth, accurate data on 2.5 million organizations across the healthcare industry—from hospitals and clinics to pharmaceutical companies, biotech firms, and medical equipment suppliers. Every record is sourced from official trade registers and healthcare authorities, ensuring regulatory compliance and unmatched data quality.

We deliver comprehensive company profiles enriched with key firmographics, industry classifications, ownership structures, executive contact details, emails, direct phone numbers, and mobile data. Updated regularly and quality-checked against official sources, our healthcare data empowers organizations to make informed decisions across critical functions—from KYC verification and compliance to targeted sales campaigns, healthcare market analysis, CRM enrichment, and AI model development.

To suit every workflow, we offer flexible delivery solutions including custom bulk files, self-service platform access, real-time API integrations, and on-demand enrichment services. Whether you're scaling a B2B marketing strategy or building healthcare analytics tools, our datasets are ready to plug into your operations.

With coverage of over 380 million verified companies across all industries and regions, CompanyData.com (BoldData) offers the global reach and industry precision that modern organizations demand. Tap into our healthcare data solutions to discover new opportunities, reduce risk, and power smarter business growth across the global health economy.

A 100-patient database that contains in total 100 virtual patients, 372 admissions, and 111,483 lab observations.

PATRON is a human ethics approved program of research incorporating an enduring de-identified repository of Primary Care data facilitating research and knowledge generation. PATRON is a part of the 'Data for Decisions' initiative of the Department of General Practice, University of Melbourne. 'Data for Decisions' is a research initiative in partnership with general practices. It is an exciting undertaking that makes possible primary care research projects to increase knowledge and improve healthcare practices and policy. Principal Researcher: Jon EmeryData Custodian: Lena SanciData Steward: Douglas BoyleManager: Rachel CanawayMore information about Data for Decisions and utilising PATRON data is available from the Data for Decisions website.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

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 purpose of the collection of outpatient health statistics is to monitor, evaluate and plan curative and preventive health care at the primary and secondary level of health care system.

Data on outpatient statistics are an important source of information for population health monitoring indicators
and accessibility of outpatient health care activities in Slovenia. Health care providers collect data for each individual contact of the patients with the health service. It is reported by public and private healthcare providers.

Outpatient health statistics record contacts and services at general practicioners and specialist outpatient activities at the secondary level.

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising prevalence of chronic diseases, and the expanding demand for efficient healthcare management solutions. The market's value, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key trends, including the increasing integration of artificial intelligence (AI) and machine learning (ML) for improved diagnostics and personalized medicine, the growing adoption of cloud-based solutions for enhanced data accessibility and scalability, and the rising focus on data security and interoperability to comply with stringent regulations like HIPAA. The market is segmented by application (hospital management, clinical research, practice management) and type (cloud-based, on-premise), with cloud-based solutions rapidly gaining traction due to their cost-effectiveness and flexibility. Major players like Pabau, EHR Your Way, NextGen Healthcare, and others are driving innovation and market competition through continuous product development and strategic partnerships. Geographic expansion is another notable market driver, with North America currently holding the largest market share due to advanced healthcare infrastructure and high technological adoption. However, regions like Asia-Pacific are exhibiting rapid growth potential, driven by increasing healthcare expenditure and improving healthcare infrastructure. Despite the positive outlook, market restraints include concerns about data privacy and security, high implementation costs associated with some software solutions, and the need for extensive training for healthcare professionals to effectively use these systems. Furthermore, the heterogeneous nature of existing healthcare IT systems can pose integration challenges. To overcome these obstacles, vendors are focusing on developing user-friendly interfaces, robust security protocols, and cost-effective implementation strategies. The future of the medical database software market hinges on seamless integration, enhanced security features, and the ability to leverage data analytics for improved patient outcomes and operational efficiency.

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.

Our NHS medical database contains named medical and clinical specialists, and holds verified doctors email addresses, for targeted medical research and clinical marketing.

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays each year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. 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 KID is a sample of pediatric discharges from 4,000 U.S. hospitals in the HCUP State Inpatient Databases yielding approximately two to three million unweighted hospital discharges for newborns, children, and adolescents per year. About 10 percent of normal newborns and 80 percent of other neonatal and pediatric stays are selected from each hospital that is sampled for patients younger than 21 years of age. The KID contains 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). It includes discharge status, diagnoses, procedures, patient demographics (e.g., sex, age), expected source of primary payment (e.g., Medicare, Medicaid, private insurance, self-pay, and other insurance types), and hospital charges and cost. Restricted access data files are available with a data use agreement and brief online security training.

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

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.

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 National Health Service is the largest employer in the UK but is not a single homogenous organisation. Following devolution and major re-organisations in the past few years, the ways in which it is organised in England, Scotland, Wales and Northern Ireland are continuing to diverge.

Our database covers senior and mid-level posts across all functions and areas of the NHS. This includes both the Management and Medical/Clinical sides.

England - the NHS has undergone considerable re-organisation since 2011 with Strategic Health Authorities and Primary Care Trusts being replaced by a new structure of healthcare provision. The vast majority of services are now provided or commissioned at a local level via groups of GP Surgeries, known as Clinical Commissioning Groups (CCG's), or at a secondary care level via Hospital Trusts. Public Health services are now provided by Local Authorities who also work with CCG's via Health and Wellbeing Boards to commission services jointly. There are also a number of new 'Community Healthcare' providers, in the form of Health and Care Trusts (NHS organisations) and Community Interest Companies (Social Enterprises). These organisations provide a range of community, mental health, primary care and nursing functions and sit alongside Local Authorities, CCG's and Secondary Care providers in many areas. These, along with some Secondary Care Acute Trusts which inherited them following the dissolution of PCT's run Community Hospitals, Clinics, Walk in Centres and some Dental services.

Scotland - has a simplified structure with Scottish Health Boards having control of all operational responsibilities within their geographical area. The Community Health Partnerships provide a range of community health services and they work closely with primary health care professionals as well as hospitals and local councils.

Wales - has established Local Health Boards and with the exception of one remaining NHS Trust, they deal with all Primary and Secondary Healthcare services.

Northern Ireland - also has single organisations - Health & Social Care Trusts, which along with several other national bodies, deal with co-ordinating and providing all the regions Healthcare services.

Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.

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.