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.

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 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 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.

The "Healthcare Documentation Database" is a concise yet comprehensive collection of medical transcriptions spanning various specialties and patient encounters. Each entry includes a brief description of the medical encounter, categorized by specialty and accompanied by a unique sample name for easy reference. The transcriptions capture essential details such as patient history, symptoms, diagnoses, and treatments, providing valuable insights for healthcare professionals and researchers. This dataset serves as a valuable resource for analyzing trends, patterns, and outcomes across different medical disciplines, facilitating evidence-based decision-making and research advancements in healthcare. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18544731%2Fb8dea5ab6b921b5affbb637fdd99de5c%2Fhealth_g1164501548.jpg?generation=1708926919496930&alt=media" alt="">

[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.

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.

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 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.

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).

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.

Abstract

The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, uncommon treatments, and special populations.

Usage

IMPORTANT NOTE: Some records are missing from the Severity Measures table for 2017 & 2018, but none are missing from any of the other 2012-2020 data. We are in the process of trying to recover the missing records, and will update this note when we have done so.

Also %3Cu%3EDO NOT%3C/u%3E

use this data without referring to the NIS Database Documentation, which includes:

• Description of NIS Database
• Restrictions on Use

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• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the NIS Starting with 2015 (More details about this transition available here.)
• Known Data Issues
• NIS Supplemental Files
• HCUP Tools: Labels and Formats
• Obtaining HCUP Data

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Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

HCUP Online Tutorials

For additional assistance, AHRQ has created the HCUP Online Tutorial Series, a series of free, interactive courses which provide training on technical methods for conducting research with HCUP data. Topics include an HCUP Overview Course and these tutorials:

• The HCUP Sampling Design tutorial is designed to help users learn how to account for sample design in their work with HCUP national (nationwide) databases. • The Producing National HCUP Estimates tutorial is designed to help users understand how the three national (nationwide) databases – the NIS, Nationwide Emergency Department Sample (NEDS), and Kids' Inpatient Database (KID) – can be used to produce national and regional estimates. HCUP 2020 NIS (8/22/22) 14 Introduction • The Calculating Standard Errors tutorial shows how to accurately determine the precision of the estimates produced from the HCUP nationwide databases. Users will learn two methods for calculating standard errors for estimates produced from the HCUP national (nationwide) databases. • The HCUP Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data. • The HCUP Software Tools Tutorial provides instructions on how to apply the AHRQ software tools to HCUP or other administrative databases.

New tutorials are added periodically, and existing tutorials are updated when necessary. The Online Tutorial Series is located on the HCUP-US website at www.hcupus.ahrq.gov/tech_assist/tutorials.jsp.

Important notes about the 2015 data

In 2015, AHRQ restructured the data as described here:

https://hcup-us.ahrq.gov/db/nation/nis/2015HCUPNationalInpatientSample.pdf

Some key points:

• For the 2015 data, all diagnosis and procedure data elements, including any data elements derived from diagnoses and procedures, were moved out of the Core File and into the Diagnosis and Procedure Groups Files.
• Prior to 2015, and for Q1-3 of 2015, the DX1-30 and PR1-15 variables (which use ICD-9 codes) variables were used, but starting in Q4 of 2015, the I10_DX1-30 and I10_PR1-I10-15 (which use ICD-10 codes) were used. The best way to identify discharges for quarter 1-3 or quarter 4 is based on the value of the diagnosis version (DXVER); For quarters 1-3, DXVER has a value of 9; while for quarter 4, DXVER has a value of 10.
• Some other variables also transitioned in Q4 of 2015. Please refer to the link above for more details.
• Starting in 2016, the diagnosis and procedure information returned to the Core file. Additional details about the data in 2016 are available here: https://hcup-us.ahrq.gov/db/nation/nis/NISChangesBeginningDataYr2016.pdf

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NIS Areas of Research and HCUP Publications

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.

Healthcare Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain point geographic files of healthcare organizations, providers, and hospitals and an boundary file of Primary Care Service Areas.

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.

Note:- Only publicly available data can be worked upon

APISCRAPY leads the way in delivering hassle-free AI-driven healthcare data scraping, offering a seamless and ethical approach to accessing vital information. The platform excels in extracting Hospital Data, Healthcare Provider (HCP) Data, Pharma Data, telemedicine Data, and crucial COVID-19 Data, all without any associated costs.

Ensuring accuracy and real-time updates, APISCRAPY's advanced technology navigates the intricacies of healthcare systems, providing users with valuable insights at no expense. Hospital Data, encompassing bed capacities and specialized services, is retrieved effortlessly to empower stakeholders in making informed decisions. The extraction of HCP Data supports collaboration and advancements in medical science without financial barriers.

Pharma Data, Medical Imagery Data, and Medical Claims Data are sourced by APISCRAPY for market research, diagnostic breakthroughs, and streamlined financial workflows, respectively, all without incurring costs. Patient Data and Electronic Health Record (EHR) Data are handled with utmost privacy and compliance, enabling healthcare practitioners to access personalized information at no charge.

In the realm of Telemedicine, APISCRAPY facilitates virtual healthcare services without imposing financial burdens. As a socially responsible entity, APISCRAPY offers free access to critical COVID-19 Data, contributing to global efforts in research and strategy development to combat the ongoing pandemic.

APISCRAPY's commitment to providing cost-free, AI-driven healthcare data scraping underscores its dedication to making valuable information accessible to all, fostering a more inclusive and collaborative healthcare landscape. Through its innovative approach, APISCRAPY ensures that stakeholders can harness the power of data without financial constraints.

[Related tags: Hospital Data, Healthcare Provider (HCP) Data,Pharma Data, Medical Imagery Data, Medical Claims Data, Patient Data, Electronic Health, Record (EHR) Data, Telemedicine Data, COVID-19 Data, Wearables Data , Donor Data, Healthcare Professionals Database , Healthcare data, Medical Data Extraction, Data Extraction, Web Scraping Medical Data]

This is a peer-reviewed supplementary table for the article 'Healthcare resource utilization, costs and treatment associated with myasthenia gravis exacerbations among patients with myasthenia gravis in the USA: a retrospective analysis of claims data' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: MG treatment definitionsAim: There are limited data on the clinical and economic burden of exacerbations in patients with myasthenia gravis (MG). We assessed patient clinical characteristics, treatments and healthcare resource utilization (HCRU) associated with MG exacerbation. Patients & methods: This was a retrospective analysis of adult patients with MG identified by commercial, Medicare or Medicaid insurance claims from the IBM MarketScan database. Eligible patients had two or more MG diagnosis codes, without evidence of exacerbation or crisis in the baseline period (12 months prior to index [first eligible MG diagnosis]). Clinical characteristics were evaluated at baseline and 12 weeks before each exacerbation. Number of exacerbations, MG treatments and HCRU costs associated with exacerbation were described during a 2-year follow-up period. Results: Among 9352 prevalent MG patients, 34.4% (n = 3218) experienced ≥1 exacerbation after index: commercial, 53.0% (n = 1706); Medicare, 39.4% (n = 1269); and Medicaid, 7.6% (n = 243). During follow-up, the mean (standard deviation) number of exacerbations per commercial and Medicare patient was 3.7 (7.0) and 2.7 (4.1), respectively. At least two exacerbations were experienced by approximately half of commercial and Medicare patients with ≥1 exacerbation. Mean total MGrelated healthcare costs per exacerbation ranged from $26,078 to $51,120, and from $19,903 to $49,967 for commercial and Medicare patients, respectively. AChEI use decreased in patients with multiple exacerbations, while intravenous immunoglobulin use increased with multiple exacerbations. Conclusion: Despite utilization of current treatments for MG,MG exacerbations are associated with a high clinical and economic burden in both commercial and Medicare patients. Additional treatment options and improved disease management may help to reduce exacerbations and disease burden.

In a historical and developmental sense, the former one-year reporting on employees employed in healthcare grew during 1990/91. in the continuous collection and monitoring of data through the state Register of Health Professionals. The department maintains data on all healthcare workers and healthcare associates, and on administrative and technical staff for now only numerically, according to the number of permanent employees at the end of the year. In the future, it is intended to register employees who are not health-oriented and work in healthcare, and healthcare professionals who work outside the healthcare system can also be registered.

Data on health workers and health care associates are required to be submitted not only by state and county-owned health institutions, but also by all private institutions, health workers who independently perform private practice, as well as trading companies for the performance of health activities, regardless of whether they have a contract with the Croatian Institute for health insurance.

All employees are assigned a registration number (code) upon entry into the Registry's database on the day of employment. The connection with the Croatian Health Insurance Institute exists through the use of the registration number when registering, recognizing within the CEZIH system, as well as when registering prescriptions, referrals and other documents of the HZZO. that is, in monitoring and building the health information system.

As an integral part of the same, relational databases also include data on health organizational units, representing the Register of Health Institutions. Namely, in addition to data on employees, the Registry, based on the decision of the Ministry of Health on work authorization, also records basic data on health institutions, surgeries and all other types of independent health units, regardless of the contract with the Croatian Health Insurance Institute or the type of ownership. As for employees, received data on the opening, closing, change of name, address, type and activity of the health organizational unit is also updated daily.

Thus, the organizational structure of healthcare is monitored through the database, according to levels of healthcare, types of healthcare institutions, healthcare activities performed by institutions, divisions with regard to the type of ownership as well as territorial distribution.

In addition to the importance of data on human potential and space, that is, the units where health care is provided, medical equipment is also an important factor in management and planning. One part of the department's work is related to the collection of data on this material resource. In the near future, it is planned to form a Register of Medically Expensive Equipment, which would be technologically and functionally connected with the existing two registers into a whole register of resources in healthcare.

Also, the statistical research aims to include those entities that are not part of the health system, and in which health workers work, i.e. health activities are performed, such as long-term care homes, which means expanding the existing data of the Register of Health Institutions.

In the last decade, a new IT application of the Registry of Health Care Professionals was created and an even better connection with the Croatian Institute for Health Insurance, for example through the use of the so-called population register or the register of insured persons. The register continues to be the source of data and the authorized institution for the delivery of data to international bodies such as the WHO and the joint WHO/Eurostat/OECD database. Within the scope of the Department's activities are also activities in international initiatives and programs, and with regard to the problems of statistical monitoring, shortages and planning of health workers. Since 2012, we have been involved in the implementation of the "Global Code of Practice on International Recruitment of Health Personnel", a recommendation that is also an instrument in the regulation, improvement and establishment of standards in the migration process.

In the same year, the Department was involved in the work in the part of the program platform on the topic of Joint Action on European Health Workforce Planning and Forecasting.

Also, during the past years, there has been cooperation on the topic of health workers within the framework of the South-eastern Europe Health Network (SEEHN).

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.

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