Datasets show Medicaid claims segmented by various demographic, geographic, provider, and diagnostic factors.

The New York State Department of Health (NYS DOH) shares de-identified and aggregated metrics on the NYS Medicaid program through the Health Data NY catalog and as summary statistics on DOH website. Datasets vary by subject/scope, unit of analysis, years of data collection, and update frequency. Publicly-available datasets in the Health Data NY catalog address topics including:

• Medicaid Program Enrollment Rates and Demographics
• Health Home Quality and Utilization
• Inpatient Admissions and Emergency Room Visits
• Incentive Payments
• Inpatient Potentially Preventable Readmission (PPR) Rates
• Newborn Low Birth Weight Rates
• Pediatric and Adult Discharge Quality Indicators
• Potentially Preventable Emergency Visit (PPV) Rates
• Potentially Avoidable Antibiotic Prescribing Rates

For a fee, researchers at NYU Langone Health may acquire NYS Medicaid claims data by submitting a study proposal to the Health Evaluation and Analytics Lab (HEAL). For more information, click on the link to the NYS Medicaid Claims File under the Related Datasets section or search for the NYS Medicaid Claims File in the NYU Data Catalog.

2016-2019. This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the Medicaid Analytic eXtract (MAX) data. Medicaid MAX are a set of de-identified person-level data files with information on Medicaid eligibility, service utilization, diagnoses, and payments. The MAX data contain a convenience sample of claims processed by Medicaid and Children’s Health Insurance Program (CHIP) fee for service and managed care plans. Not all states are included in MAX in all years, and as of November 2019, 2014 data is the latest available. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS Medicare analyses can be found on the VEHSS Medicaid MAX webpage (cdc.gov/visionhealth/vehss/data/claims/medicaid.html). Information on available Medicare claims data can be found on the ResDac website (www.resdac.org). The VEHSS Medicaid MAX dataset was last updated May 2023.

Datasets show maternal health-related claims by recipient county, broken down by disease states and service types.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Datasets show Transportation related claims broken down by subcategories

United States Health Insurance: Claims Per Member Per Month: Medicaid data was reported at 398.000 USD in 2023. This records an increase from the previous number of 375.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicaid data is updated yearly, averaging 291.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 398.000 USD in 2023 and a record low of 182.340 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicaid 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.

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.

United States Health Insurance: Claims Adjustment Expenses: Medicaid data was reported at 8.394 USD bn in 2023. This records an increase from the previous number of 7.762 USD bn for 2022. United States Health Insurance: Claims Adjustment Expenses: Medicaid data is updated yearly, averaging 4.872 USD bn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 8.394 USD bn in 2023 and a record low of 907.000 USD mn in 2007. United States Health Insurance: Claims Adjustment Expenses: Medicaid 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.

These are peer-reviewed supplementary materials for the article 'Evaluation of inpatient and emergency department healthcare resource utilization and costs pre- and post-nusinersen for the treatment of spinal muscular atrophy using United States claims' published in the Journal of Comparative Effectiveness Research.Supplementary Figure 1: Mean (SD) number of inpatient admissions per patient in individuals with SMA in the 12 months before and after nusinersen treatment. Mean (SD) number of days spent in hospital per patient in individuals with SMA in the 12 months before and after nusinersen treatment.Supplementary Figure 2: Mean (SD) ED visits and costs per patient in individuals with SMA in the 12 months before and after nusinersen treatment.Supplementary Table 1: Patient baseline characteristics of cohorts aligned with steps of patient selection criteria (who were ultimately excluded) in comparison to final cohort.Aim: Nusinersen, administered by intrathecal injection at a dose of 12 mg, is indicated across all ages for the treatment of spinal muscular atrophy (SMA). Evidence on real-world healthcare resource use (HRU) and costs among patients taking nusinersen remains limited. This study aimed to evaluate real-world HRU and costs associated with nusinersen use through US claims databases. Patients & methods: Using the Merative™ MarketScan R ? Research Databases, patients with SMA receiving nusinersen were identified from commercial (January 2017 to June 2020) and Medicaid claims (January 2017 to December 2019). Those likely to have complete information on the date of nusinersen initiation and continuous enrollment 12 months pre- and post-index (first record of nusinersen treatment) were retained. Number and costs (US$ 2020) of inpatient admissions and emergency department (ED) visits, unrelated to nusinersen administration, were evaluated for 12 months pre- and post-nusinersen initiation and stratified by age: pediatric (

Datasets show claims servicing mental health patients, segmented by provider, recipient race, gender, and ZIP code

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.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of dental services provided to Medicaid and CHIP beneficiaries under the age of 19 (as of the first day of the month), by state. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating dental services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Procedure Codes - OT Professional, Claims Volume - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

This dataset tracks the updates made on the dataset "NCHS Survey Data Linked to Centers for Medicare & Medicaid Services (CMS) Medicaid Enrollment and Claims Files" as a repository for previous versions of the data and metadata.

The Analysis of Longitudinal Claims Databases (R1 Part B): Effect of Variation in Health Coverage, Employment, and Community Resources on Adverse Events and Healthcare Costs and Utilization, United States is the second of a three-part project that examined claims data from Medicare, Medicaid, and/or Optum databases to explore aging trajectories, use of preventative services, and healthcare outcomes for individuals with several types of physical disabilities. This study made use of existing national databases to examine various health outcomes among individuals with disability. Using 2007-2016 Medicaid and Medicare Data, the researchers conducted three separate types of analyses: At the state level, examine the effect of variation in health coverage and related health policies on adverse health events and health outcomes among youth and adults with disability. At the county level, examine the variation in employment and community participatory living on adverse health and health outcomes among youth and adult with disability. At the state level, examine the effect of variation in Medicaid long-term care and community centers on health outcomes among youth and adult with disability.

This table presents the number of beneficiaries with a delivery, the number of beneficiaries with any SMM condition, and the rate of SMM conditions per 10,000 deliveries, 2017 - 2021. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for identifying this population. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Data from Maryland, Tennessee, and Utah are omitted from the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Characteristics of Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania USA, divided into training, testing, and validation samples.

This dataset tracks the updates made on the dataset "Medicaid Claims (MAX) - Vision and Eye Health Surveillance" as a repository for previous versions of the data and metadata.

Full Description The data indicate the number of individuals enrolled in the Medicaid (Title 19) public health insurance program, including the State Supplement program. Medicaid is the primary federal/state program to provide health care coverage for individuals who need assistance. Medicaid recipients are measured by individuals. Medicaid cases are measured by "assistance units" which are roughly comparable to households and can include multiple recipients. Connecticut Department of Social Services collects monthly and annual data and reports it annually. CTdata.org carries annual data

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...