The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

Medicare Claims Synthetic Public Use Files (SynPUFs)

Medicare Claims Synthetic Public Use Files (SynPUFs) were created to allow interested parties to gain familiarity using Medicare claims data while protecting beneficiary privacy. The data structure of the Medicare SynPUFs is very similar to the CMS Limited Data Sets, but with a smaller number of variables. They provide data analysts and software developers the opportunity to develop programs and products utilizing the identical formats and variable names as those which appear in the actual CMS data files. The files have been designed so that programs and procedures created on the SynPUFs will function on CMS Limited Data Sets. The SynPUFs also provide a robust set of metadata on the CMS claims data that have not been available in the public domain. After developmental work has been completed potential users should be much better informed about which CMS data products they would need to acquire to fulfill their analytic needs.

These files may be used to:

allow data entrepreneurs to develop and create software and applications that may eventually be applied to actual CMS claims data; train researchers on the use and complexity of conducting analyses with CMS claims data prior to initiating the process to obtain access to actual CMS data; and, support safe data mining innovations that may reveal unanticipated knowledge gains while preserving beneficiary privacy. Although these files have very limited inferential research value to draw conclusions about Medicare beneficiaries due to the synthetic processes used to create the files, they increase access to realistic Medicare claims data files in a timely and less expensive manner to spur the innovation necessary to achieve the goals of better care for beneficiaries and improve the health of the population.

Files will be made available as a free downloads in order to provide access to Medicare data without the time and cost associated with obtaining data files which require more restricted access.

The first Synthetic PUF released is the 2008-2010 Data Entrepreneurs’ SynPUF.

Acknowledgements

This data is published on the CMS website - https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs

The Medicare Current Beneficiary Survey (MCBS) - Survey File Microdata Public Use File (PUF) dataset provides information on topics such as Medicare beneficiaries' access to care, health status, other information regarding beneficiaries’ knowledge of, attitudes toward, and satisfaction with their health care, as well as demographic data and information on all types of health insurance coverage.Resources for Using and Understanding the DataThis dataset is based on information from the MCBS and administrative data. The MCBS is a continuous, multi-purpose longitudinal survey covering a representative national sample of the Medicare population, including the population of beneficiaries aged 65 and over and beneficiaries aged 64 and below with certain disabling conditions. The MCBS collects this information in three data collection periods, or rounds, per year. Disclosure protections have been applied to the file, including de-identification and other methods. As a result, the MCBS Survey File Microdata file does not require a Data Use Agreement (DUA). In contrast, the MCBS Limited Data Set (LDS) releases contain beneficiary-level protected health information (PHI) and therefore require a DUA. The MCBS - Survey File Microdata file is not intended to replace the more detailed LDS files but, rather, it makes available a general-use publicly-available alternative that provides the highest degree of protection to the Medicare beneficiaries’ PHI. The main benefits of using the MCBS - Survey File Microdata file are:Increased data access for researchers of the MCBS through a free file download that is consistent with other U.S. Department of Health and Human Services (HHS) public-use survey files.Enhanced potential for policy-relevant analyses, by attracting new researchers and policymakers. Accessing the MCBS LDS can be a significant deterrent due to the associated costs and time but the MCBS - Survey File Microdata file mitigates these barriers to encourage broader utilization. A link to the more detailed MCBS LDS files is provided in the Resources section on this page. MCBS LDS data are also presented in the MCBS Chartbook linked in the Visualization section on this page.

Abstract

The MarketScan Medicare Supplemental Database provides detailed cost, use and outcomes data for healthcare services performed in both inpatient and outpatient settings.

It Include Medicare Supplemental records for all years, and Medicare Advantage records starting in 2020. This page also contains the MarketScan Medicare Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 250 million patients (medical, drug and dental)

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• Laboratory results

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• Hospital discharges

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The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers and Medicare.

Usage

This page contains the MarketScan Medicare Database.

We also have the following on other pages:

• The MarketScan Commercial Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **

(though the 1% sample will remain free to use). The pricing structure and other

**relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

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

support@stanfordphs.freshdesk.com 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

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Usage

This dataset page includes some of the tables from the Medicare Data in PHS's possession. Other Medicare tables are included on other dataset pages on the PHS Data Portal. Depending upon your research question and your DUA with CMS, you may only need tables from a subset of the Medicare dataset pages, or you may need tables from all of them.

The location of each of the Medicare tables (i.e. a chart of which tables are included in each Medicare dataset page) is shown here.

Before Manuscript Submission

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

support@stanfordphs.freshdesk.com 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

Documentation

Metadata access is required to view this section.

Abstract

Outpatient

Usage

This dataset page includes some of the tables from the Medicare Data in PHS's possession. Other Medicare tables are included on other dataset pages on the PHS Data Portal. Depending upon your research question and your DUA with CMS, you may only need tables from a subset of the Medicare dataset pages, or you may need tables from all of them.

The location of each of the Medicare tables (i.e. a chart of which tables are included in each Medicare dataset page) is shown here.

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

Documentation

Metadata access is required to view this section.

Section 2

Metadata access is required to view this section.

Usage Notes

Data access is required to view this section.

The Medicare Current Beneficiary Survey (MCBS) is a comprehensive, longitudinal survey of Medicare beneficiaries conducted by the Centers for Medicare & Medicaid Services (CMS) in partnership with NORC at the University of Chicago. It contains detailed information on beneficiaries’ demographics, health status, healthcare utilization, and outcomes, including social and medical risk factors. The datasets can be linked with Medicare enrollment data and claims, and provide information to evaluate effectiveness of health care policy and policy interventions. This dataset is restricted-use due to the inclusion of sensitive and identifiable health information. As such, the data files themselves cannot be shared via this Dataverse entry. Students, post-docs, and researchers conducting NSAPH-related research in collaboration with PIs at Harvard or affiliated institutions may gain access to the data, provided have submitted a project initiation form, are listed on an approved IRB protocol, and have completed all required data security trainings. No data files are included in this entry, and the data has not been processed in any way. This entry serves as a reference to the original raw data received from CMS and includes only supporting materials such as a README file, and other relevant documentation, codebooks and tutorials from the CMS website to assist authorized users in understanding the structure and content of the MCBS restricted-use files. The README includes: Structure and content of the MCBS data files References to supporting documentation

Context

This dataset contains Hospital General Information from the U.S. Department of Health & Human Services. This is the BigQuery COVID-19 public dataset. This data contains a list of all hospitals that have been registered with Medicare. This list includes addresses, phone numbers, hospital types and quality of care information. The quality of care data is provided for over 4,000 Medicare-certified hospitals, including over 130 Veterans Administration (VA) medical centers, across the country. You can use this data to find hospitals and compare the quality of their care

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.cms_medicare.hospital_general_info.

Sample Query

How do the hospitals in Mountain View, CA compare to the average hospital in the US? With the hospital compare data you can quickly understand how hospitals in one geographic location compare to another location. In this example query we compare Google’s home in Mountain View, California, to the average hospital in the United States. You can also modify the query to learn how the hospitals in your city compare to the US national average.

“#standardSQL SELECT MTV_AVG_HOSPITAL_RATING, US_AVG_HOSPITAL_RATING FROM ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS MTV_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE city = 'MOUNTAIN VIEW' AND state = 'CA' AND hospital_overall_rating <> 'Not Available') MTV JOIN ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS US_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE hospital_overall_rating <> 'Not Available') ON 1 = 1”

What are the most common diseases treated at hospitals that do well in the category of patient readmissions? For hospitals that achieved “Above the national average” in the category of patient readmissions, it might be interesting to review the types of diagnoses that are treated at those inpatient facilities. While this query won’t provide the granular detail that went into the readmission calculation, it gives us a quick glimpse into the top disease related groups (DRG)
, or classification of inpatient stays that are found at those hospitals. By joining the general hospital information to the inpatient charge data, also provided by CMS, you could quickly identify DRGs that may warrant additional research. You can also modify the query to review the top diagnosis related groups for hospital metrics you might be interested in. “#standardSQL SELECT drg_definition, SUM(total_discharges) total_discharge_per_drg FROM bigquery-public-data.cms_medicare.hospital_general_info gi INNER JOIN bigquery-public-data.cms_medicare.inpatient_charges_2015 ic ON gi.provider_id = ic.provider_id WHERE readmission_national_comparison = 'Above the national average' GROUP BY drg_definition ORDER BY total_discharge_per_drg DESC LIMIT 10;”

The Synthetic Patient Data in OMOP Dataset is a synthetic database released by the Centers for Medicare and Medicaid Services (CMS) Medicare Claims Synthetic Public Use Files (SynPUF). It is synthetic data containing 2008-2010 Medicare insurance claims for development and demonstration purposes. It has been converted to the Observational Medical Outcomes Partnership (OMOP) common data model from its original form, CSV, by the open source community as released on GitHub Please refer to the CMS Linkable 2008–2010 Medicare Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) User Manual for details regarding how DE-SynPUF was created." This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The CMS EHR Incentive Programs provide incentives to eligible office-based providers and hospitals to adopt electronic health records. Both the Medicare and Medicaid programs have separate criteria and eligible participants. These measures track the percentage of physicians, nurse practitioners, physician assistants, short-term general, Critical Access, and Children's hospitals that have demonstrated meaningul use of certified electronic health record technology and/or adopted, implemented, or ugraded any electronic health record. These measures track the rate of adoption and use of EHR technology certified by HHS in addition to adoption of other non-certified EHR technology. These measures are cumulative, representing the most recent data.

Abstract (en): This data collection is the second in a series of data releases from the Medicare Current Beneficiary Survey (MCBS) relating to beneficiary access to medical care. The MCBS is a continuous, multipurpose survey of a representative sample of the Medicare population, both aged and disabled. Sample persons are interviewed three times a year over several years to form a continuous profile of their health care experience. Interviews are conducted regardless of whether the sample person resides at home or in a long-term care facility, using the questionnaire version appropriate to the setting. The MCBS also collects a variety of information about demographic characteristics (date of birth, sex, race, education, military service, and marital status), health status and functioning, access to care, sources of and satisfaction with care, insurance coverage, financial resources, and family supports. The 1992 interview data were collected during September through December of 1992, the fourth round of data collection. The 1992 data are designed to stand alone for cross-sectional analysis, or they can be used for longitudinal analysis. Weights are provided for both cross-sectional and longitudinal analysis. Medicare beneficiaries. Respondents were sampled from the Medicare enrollment file to be representative of the Medicare population as a whole and by age group: under 45, 45-64, 65-69, 70-74, 75-79, 80-84, and 85 and over. Because of interest in their special health care needs, the oldest old (85 and over) and the disabled (64 and under) were oversampled to permit detailed analysis of these subpopulations. The sample was drawn from 107 primary sampling units (PSUs). The 1992 Round 4 data include interviews for 10,388 persons who were interviewed in 1991 and for 1,995 new people added to the survey during the current round. The 1992 supplementary sample included newly enrolled beneficiaries, as well as previously enrolled beneficiaries who were included to improve coverage or to maintain the desired sample size. 2006-01-12 All files were removed from dataset 25 and flagged as study-level files, so that they will accompany all downloads.1997-04-22 Part 7 (Health Status and Functioning Record File) and Part 11 (Health Insurance Record File) have been resupplied by the principal investigator to correct several variables. In addition, the billing records data (Parts 25-30) were withdrawn from distribution by the principal investigator. On March 13, 1997, the HCFA withdrew the billing records data (Parts 25-30) from distribution.

Health care providers and Head Start programs can play a major role in finding and enrolling uninsured children through presumptive eligibility. States can authorize “qualified entities” -- health care providers, community-based organizations, and schools, among others -- to screen for Medicaid and CHIP eligibility and immediately enroll children who appear to be eligible. Presumptive eligibility allows children to get access to Medicaid or CHIP services without having to wait for their application to be fully processed. Qualified entities can also help families gather the documents needed to complete the full application process, thereby reducing the administrative burden on States to obtain missing information.

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarizes the utilization and payments for procedures, services, and prescription drugs provided to Medicare beneficiaries by specific inpatient and outpatient hospitals, physicians, and other suppliers. The dataset includes the following data - common inpatient and outpatient services, all physician and other supplier procedures and services, and all Part D prescriptions. Providers determine what they will charge for items, services, and procedures provided to patients and these charges are the amount that providers bill for an item, service, or procedure. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The Accountable Care Organization Realizing Equity, Access, and Community Health (ACO REACH) Model dataset provides overview information on REACH ACOs including their name, number of years in the program, and contact information of key personnel. DISCLAIMER: This information is current as of the last update. Changes to ACO information occur periodically. Each ACO has the most up-to-date information about their organization. Consider contacting the ACO for the latest information.Model OverviewThe ACO REACH Model provides novel tools and resources for health care providers to work together in an ACO to improve the quality of care for people with Traditional Medicare. REACH ACOs are comprised of different types of providers, including primary and specialty care physicians.The ACO REACH Model makes important changes to the previous Global and Professional Direct Contracting (GPDC) Model which include: Promote Provider Leadership and Governance. The ACO REACH Model includes policies to ensure doctors and other health care providers continue to play a primary role in accountable care. At least 75% control of each ACO's governing body generally must be held by participating providers or their designated representatives, compared to 25% during the first two Performance Years of the GPDC Model. In addition, the ACO REACH Model goes beyond prior ACO initiatives by requiring at least two beneficiary advocates on the governing board (at least one Medicare beneficiary and at least one consumer advocate), both of whom must hold voting rights. Protect Beneficiaries and the Model with More Participant Vetting, Monitoring and Greater Transparency. CMS will ask for additional information on applicants’ ownership, leadership, and governing board to gain better visibility into ownership interests and affiliations to ensure participants’ interests align with CMS’s vision. We will employ increased up-front screening of applicants, robust monitoring of participants, and greater transparency into the model’s progress during implementation, even before final evaluation results, and will share more information on the participants and their work to improve care. Last, CMS will also explore stronger protections against inappropriate coding and risk score growth. Resources for Using and Understanding the DataThis dataset is based on information submitted by ACOs via the 4innovation (4i) System and obtained by CMS during the application review process. Within this data, users can access overview information about REACH ACOs.

Medicare provides access to medical and hospital services for all Australian residents and certain categories of visitors to Australia. The Medicare Benefits Schedule (MBS) lists services that are subsidised by the Australian Government under Medicare. \r \r This data provides statistics on groups of MBS services. MBS groups (ie. category, groups and subgroup) are described in the MBS online.\r \r Data is provided in the following formats: \r \r Excel: The human readable data for the current year is provided in an individual excel file. Historical data (1993-2015) may be found in the excel zipped file. \r CSV: The machine readable data for the current year is provided in an individual csv file. Historical data (1993-2015) may be found in the csv zipped file. \r \r \r Additional Medicare statistics may be found on the Department of Human Services website.\r \r \r Disclaimer: The information and data contained in the reports and tables have been provided by Medicare Australia for general information purposes only. While Medicare Australia takes care in the compilation and provision of the information and data, it does not assume or accept liability for the accuracy, quality, suitability and currency of the information or data, or for any reliance on the information and data. Medicare Australia recommends that users exercise their own care, skill and diligence with respect to the use and interpretation of the information and data. \r \r \r \r \r \r

States have the option to provide children with 12 months of continuous coverage through Medicaid and CHIP, even if the family experiences a change in income during the year. Continuous eligibility is a valuable tool that helps States ensure that children stay enrolled in the health coverage for which they are eligible and have consistent access to needed health care services.

In the May 2020 CMS Interoperability and Patient Access final rule, CMS finalized the policy to publicly report the names and NPIs of those providers who do not have digital contact information included in the NPPES system (85 FR 25584). This data includes the NPI and provider name of providers and clinicians without digital contact information in NPPES.

Abstract

The MarketScan Commercial Database (previously called the 'MarketScan Database') contains real-world data for healthcare research and analytics to examine health economics and treatment outcomes.

This page also contains the MarketScan Commercial Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 188 million patients (medical, drug and dental)

%3C!-- --%3E

• Laboratory results

%3C!-- --%3E

• Hospital discharges

%3C!-- --%3E

The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers, and Medicare.

Usage

This page contains the MarketScan Commercial Database.

We also have the following on other pages:

• The MarketScan Medicare Supplemental Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

%3C!-- --%3E

**Starting in 2026, there will be a data access fee for using the full dataset **(though the 1% sample will remain free to use). The pricing structure and other **relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to support@stanfordphs.freshdesk.com 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

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Usage FAQs (Answers provided in User Guide starting on page 56)

Metadata access is required to view this section.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. 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. Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). 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 NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS 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 ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

All general (non-research, non-ownership related) payments from the 2021 program year [January 1 – December 31, 2021]

NOTE: This is a very large file and, depending on your network characteristics and software, may take a long time to download or fail to download. Additionally, the number of rows in the file may be larger than the maximum rows your version of Microsoft Excel supports. If you can't download the file, we recommend engaging your IT support staff. If you are able to download the file but are unable to open it in MS Excel or get a message that the data has been truncated, we recommend trying alternative programs such as MS Access, Universal Viewer, Editpad or any other software your organization has available for large datasets.