This dataset tracks the updates made on the dataset "Center for Medicare & Medicaid Services (CMS) , Medicare Claims data" as a repository for previous versions of the data and metadata.

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

2003 forward. CMS compiles claims data for Medicare and Medicaid patients across a variety of categories and years. This includes Inpatient and Outpatient claims, Master Beneficiary Summary Files, and many other files. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by location (national and state) and indicator. The data can be plotted as trends and stratified by sex and race/ethnicity.

The Medicare Home Health Agency tables provide use and payment data for home health agencies. The tables include use and expenditure data from home health Part A (Hospital Insurance) and Part B (Medical Insurance) claims. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR HHA 1. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR HHA 2. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR HHA 3. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Area of Residence MDCR HHA 4. Medicare Home Health Agencies: Persons with Utilization and Total Service Visits for Original Medicare Beneficiaries, Type of Agency and Type of Service Visit MDCR HHA 5. Medicare Home Health Agencies: Persons with Utilization and Total Service Visits for Original Medicare Beneficiaries, by Type of Control and Type of Service Visit MDCR HHA 6. Medicare Home Health Agencies: Persons with Utilization, Total Service Visits, and Program Payments for Original Medicare Beneficiaries, by Number of Service Visits and Number of Episodes

The Medicare Physician & Other Practitioners by Provider dataset provides information on use, payments, submitted charges and beneficiary demographic and health characteristics organized by National Provider Identifier (NPI). Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

Organizations can license synthetic, Medicare claims data generated by Syntegra.

Example fields in the claims data include: Patient demographics (gender, race, etc.), Diagnosis & procedure information (ICD-10 code, description, physician NPI, etc.), Encounter and discharge information, Payer coverage and payer type, and Claim information (claim ID, bill code, encounter ID, etc.).

The claims data is available in the following formats: Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format FHIR CARIN for Blue Button Medicare Standard CCLF

Our synthetic data engine is trained on a broadly representative dataset made up of diverse, realistic healthcare information of approximately 7 million unique patient records. Notably, synthetic data generation allows for the creation of any number of records needed to power your project.

The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained.

— Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability

Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning

This data package contains Medicare spending statistics for beneficiaries grouped according to their age, gender, race/ethnicity and geographical location. At the same time, it provides data about spendings taking into consideration provider specific coordinates like the Hospital Referral Region (HRR) or Hospital Service Area (HSA). The data package contains as well as spending statistics based on the payment system, like the Outpatient Prospective Payment System.

The Medicare Inpatient Hospitals by Provider and Service dataset provides information on inpatient discharges for Original Medicare Part A beneficiaries by IPPS hospitals. It includes information on the use, payment, and hospital charges for more than 3,000 U.S. hospitals that received IPPS payments. The data are organized by hospital and Medicare Severity Diagnosis Related Group (DRG). Hospitals determine what they will charge for items and services provided to patients, and these charges are the amount the hospital bills for an item or service. The Total Payment Amount includes the DRG amount, claim per diem amount, beneficiary primary payer claim payment amount, beneficiary Part A (Hospital Insurance) coinsurance amount, beneficiary deductible amount, beneficiary blood deductible amount and diagnosis related group outlier amount.

This series of files links two large population-based sources providing detailed data about Medicare beneficiaries with cancer. The SEER (Surveillance, Epidemiology, and End Results) program consists of clinical, demographic, and cause of death information collected from tumor registries beginning in January 1, 1973. The Medicare contribution includes all claims for covered health care services from beneficiaries’ time of eligibility until death. Linkage is processed biennially by SEER and Centers for Medicare and Medicaid Services (CMS) staff. 95% of individuals age 65 and older are included in the SEER files. Due to privacy concerns, access to this database requires an application, SEER-Medicare Data Use Agreement (DUA), and documentation of institutional review board approval. Additionally, the National Cancer Institute’s information technology contractor assesses a processing fee the amount of which is dependent upon the type and number of files requested.

Dataset updated Aug 11, 2023

Dataset provided by Centers for Disease Control and Prevention

Authors Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Heart Disease and Stroke Prevention (DHDSP), National Cardiovascular Disease Surveillance System.

Data Description

2003 forward. CMS compiles claims data for Medicare and Medicaid patients across a variety of categories and years. This includes Inpatient and Outpatient claims, Master Beneficiary Summary Files, and many other files. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by location (national and state) and indicator. The data can be plotted as trends and stratified by sex and race/ethnicity.

Topics Heart Disease & Stroke Prevention

The CMS Program Statistics - Medicare Part A & Part B - All Types of Service tables provide use and payment data by type of coverage and type of service. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR SUMMARY AB 1. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for All Original Medicare Beneficiaries, by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 2. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Aged Original Medicare Beneficiaries, by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 3. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Disabled Original Medicare Beneficiaries by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 4. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Coverage, Demographic Characteristics, and Medicare-Medicaid Enrollment Status MDCR SUMMARY AB 5. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Coverage and by Area of Residence MDCR SUMMARY AB 6. Medicare Part A and Part B Summary: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Entitlement, Amount of Program Payments, Type of Coverage, and Type of Service

This dataset shows the Medicare Heart Disease and Stroke Prevention Claims Data based on Center for Medicare & Medicaid Services (CMS), Medicare Claims data 2004 forward. CMS compiles claims data for Medicare and Medicaid patients across a variety of categories and years. This includes Inpatient and Outpatient claims, Master Beneficiary Summary Files, and many other files.

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.

Information on utilization and payment data for Home health agency, Hospice, skilled nursing facitlity. Information on Inpatient Prospective Payment System (IPPS) payments, Inpatient Rehabilitation Facilities (IRFs)

Individual outpatient claim records.

The table Outpatient Services Claims is part of the dataset MarketScan Medicare Supplemental, available at https://stanford.redivis.com/datasets/jv2x-25dm36err. It contains 2212479298 rows across 67 variables.

We present a synthetic medicare claims dataset linked to environmental exposures and potential confounders. In most environmental health studies relying on claims data, data restrictions exist and the data cannot be shared publicly. Centers for Medicare and Medicaid services (CMS) has generated synthetic publicly available Medicare claims data for 2008-2010. In this dataset, we link the 2010 synthetic Medicare claims data to environmental exposures and potential confounders. We aggregated the Medicare claims synthetic data for 2010 to the county level. Data is compiled for the contiguous United States, which in 2010, included 3109 counties. We merged the Medicare claims synthetic data with air pollution exposure data, more specifically with estimates of 𝑃𝑀2.5 exposures obtained from Di et al., 2019, 2021, which provided daily and annual estimates of PM2.5 exposure at 1 km×1 km grid cells in the contiguous United States. We use Census Bureau (United States Census Bureau, 2021), the Center for Disease Control (Centers for Disease Control and Prevention (CDC), 2021), and GridMET (Abatzoglou, 2013) to obtain data on potential confounders. The mortality rate, as the outcome, was computed using the synthetic Medicare data (CMS, 2021). We use the average of surrounding counties to impute missing observations, except in the case of the CDC confounders, where we imputed missing values by generating a normal distribution for each state and randomly imputing from this distribution. The steps for generating the merged dataset are provided at NSAPH Synthetic Data Github Repository (https://github.com/NSAPH/synthetic_data). Analytic inferences based on this synthetic dataset should not be made. The aggregated dataset is composed of 46 columns and 3109 rows.

NCHS has linked various surveys with Medicaid enrollment and claims records collected from the Centers for Medicare & Medicaid Services (CMS) Transformed Medicaid Statistical Information System (T-MSIS). Linkage of the NCHS survey participants with the CMS T-MSIS data creates a new data resource that can support research studies focused on a wide range of patient health outcomes and the association of means-tested government insurance programs on health and health outcomes.

analyze the basic stand alone medicare claims public use files (bsapufs) with r and monetdb the centers for medicare and medicaid services (cms) took the plunge. the famous medicare 5% sample has been released to the public, free of charge. jfyi - medicare is the u.s. government program that provides health insurance to 50 million elderly and disabled americans. the basic stand alone medicare claims public use files (bsapufs) contain either person- or event-level data on inpatient stays, durable medical equipment purchases, prescription drug fills, hospice users, doctor visits, home health provision , outpatient hospital procedures, skilled nursing facility short-term residents, as well as aggregated statistics for medicare beneficiaries with chronic conditions and medicare beneficiaries living in nursing homes. oh sorry, there's one catch: they only provide sas scripts to analyze everything. cue the villian music. that bored old game of monopoly ends today. the initial release of the 2008 bsapufs was accompanied by some major fanfare in the world of health policy , a big win for government transparency. unfortunately, the final files that cleared the confidentiality hurdles are heavily de-identified and obfuscated. prime examples: none of the files can be linked to any other file. not across years, not across expenditure categories costs are rounded to the nearest fifth or tenth dollar at lower values, nearest thousandth at higher values ages are categorized into five year bands so these files are baldly inferior to the unsquelched, linkable data only available through an expensive formal application process. any researcher with a budget flush enough to afford a sas license (the only statistical software mentioned in the cms official documentation) can probably also cough up the money to buy the identifiable data through resdac (resdac, btw, rocks). soapbox: cms released free public data sets that could only be analyzed with a software package costing thousands of dollars. so even though the actual data sets were free, researchers still needed deep pock ets to buy sas. meanwhile, the unsquelched and therefore superior data sets are also available for many thousands of dollars. researchers with funding would (reasonably) just buy the better data. researchers without any financial resources - the target audience of free, public data - were left out in the cold. no wonder these bsapufs haven't been used much. that ends now. using r, monetdb, and the personal computer you already own (mine cost $700 in 2009), researchers can, for the first time, seriously analyze these medicare public use files without spending another dime. woah. plus hey guess what all you researcher fat-cats with your federal grant streams and your proprietary software licenses: r + monetdb runs one heckuva lot faster than sas. woah^2. dump your sas license water wings and learn how to swim. the scripts below require monetdb . click here for step-by-step instructions of how to install it on windows and click here for speed tests. vroom. since the bsapufs comprise 5% of the medicare population, ya generally need to multiply any counts or sums by twenty. although the individuals represented in these claims are randomly sampled, this data should not be treated like a complex survey sample, meaning that the creation of a survey object is unnecessary. most bsapufs generalize to either the total or fee-for-service medicare population, but each file is different so give the documentation a hard stare before that eureka moment. this new github repository contains three scripts: 2008 - download all csv files.R loop through and download every zip file hosted by cms unzip the contents of each zipped file to the working directory 2008 - import all csv files into monetdb.R create the batch (.bat) file needed to initiate the monet database in the f uture loop through each csv file in the current working directory and import them into the monet database create a well-documented block of code to re-initiate the monetdb server in the future 2008 - replicate cms publications.R initiate the same monetdb server instance, unsing the same well-documented block of code as above replicate nine sets of statistics found in data tables provided by cms < a href="https://github.com/ajdamico/usgsd/tree/master/Basic%20Stand%20Alone%20Medicare%20Claims%20Public%20Use%20Files">click here to view these three scripts for more detail about the basic stand alone medicare claims public use files (bsapufs), visit: the centers for medicare and medicaid's bsapuf homepage a joint academyhealth webinar given by the organizations that partnered to create these files - cms, impaq, norc notes: the replication script has oodles of easily-modified syntax and should be viewed for analysis examples. if you know the name of the data table you want to examine, you can quickly modify these general monetdb analysis examples too. just run sql queries - sas users, that's "proc...

United States Health Insurance: Claims Per Member Per Month: Medicare data was reported at 1,111.000 USD in 2023. This records an increase from the previous number of 1,012.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicare data is updated yearly, averaging 791.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 1,111.000 USD in 2023 and a record low of 746.230 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicare data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

Here, you will find resources to use the Bynum-Standard 1-Year Algorithm including a README file that accompanies SAS and Stata scripts for the 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data. There are seven script files (plus a parameters file for SAS [parm.sas]) for both SAS and Stata. The files are numbered in the order in which they should be run; the five “1” files may be run in any order.The full algorithm requires access to a single year of Medicare Claims data for (1) MedPAR, (2) Home Health Agency (HHA) Claims File, (3) Hospice Claims File, (4) Carrier Claims and Line Files, and (5) Hospital Outpatient File (HOF) Claims and Revenue Files. All Medicare Claims files are expected to be in SAS format (.sas7bdat).For each data source, the script will output three files*:Diagnosis-level file: Lists individual ADRD diagnoses for each beneficiary for a given visit. This file allows researchers to identify which ICD-9-CM or ICD-10-CM codes are used in the claims data.Service Date-level file: Aggregated from the Diagnosis-level file, this file includes all beneficiaries with an ADRD diagnosis by Service Date (date of a claim with at least one ADRD diagnosis).Beneficiary-level file: Aggregated from the Service Date-level file, this file includes all beneficiaries with at least one* ADRD diagnosis at any point in the year within a specific file* The algorithm combines the Carrier and HOF files at the Service Date-level. The final combined Carrier and HOF Beneficiary-level file includes those with at least two (2) claims that are seven (7) or more days apart.​A final combined file is created by merging all Beneficiary-level files. This file is used to identify beneficiaries with ADRD and can be merged onto other files by the Beneficiary ID (BENE_ID).​With appreciation & acknowledgement to colleagues at the NIA IMPACT Collaboratory for their involvement in development & validation of the Bynum-Standard 1-Year Algorithm: