The CMS Program Statistics - Medicare Outpatient Facility tables provide use and payment data for all outpatient facilities, including hospitals providing outpatient services, rural health clinics, community mental health centers, federally qualified health centers, outpatient dialysis facilities, comprehensive outpatient rehabilitation facilities, and other outpatient facilities.

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 OUTPATIENT 1. Medicare Outpatient Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR OUTPATIENT 2. Medicare Outpatient Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR OUTPATIENT 3. Medicare Outpatient Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR OUTPATIENT 4. Medicare Outpatient Facilities: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Outpatient Facility MDCR OUTPATIENT 5. Medicare Outpatient Facilities: Utilization for Original Medicare Beneficiaries, by Type of Outpatient Facility and Type of Service MDCR OUTPATIENT 6. Medicare Outpatient Prospective Payment System Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR OUTPATIENT 7. Medicare Outpatient Prospective Payment System Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR OUTPATIENT 8. Medicare Outpatient Prospective Payment System Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR OUTPATIENT 9. Medicare Outpatient Critical Access Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR OUTPATIENT 10. Medicare Outpatient Critical Access Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR OUTPATIENT 11. Medicare Outpatient Critical Access Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence

This map shows where people have Medicaid or means-tested healthcare coverage in the US (ages under 65). This is shown by State, County, and Census Tract, and uses the most current ACS 5-year estimates.

This layer shows the market potential for an adult to carry medical/hospital/accident insurance in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to carry medical/hospital/accident insuranceMarket Potential Index and count of adults expected to carry different types of medical insurance (HMO, PPO, etc)Market Potential Index and count of adults expected to carry insurance from various sources (Medicare, place of work, etc)Esri's 2016 Market Potential (MPI) data measures the likely demand for a product or service in an area. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. An MPI compares the demand for a specific product or service in an area with the national demand for that product or service. The MPI values at the US level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the US average; an index of 80 implies that demand is 20 percent lower than the US average. See Market Potential database to view the methodology statement and complete variable list.Esri's Financial & Insurance Data Collection includes data that measures the likely demand for financial and insurance products and services, including health insurance. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product, activity, or service. See the United States Data Browser to view complete variable lists for each Esri demographics collection.Additional Esri Resources:U.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

Description2019 - 2022, county-level U.S. stroke hospitalization rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: I60-I69; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteria.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall   BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

2018 - 2020, county-level U.S. stroke hospitalization rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and sex.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: I60-I69; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteria.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall   BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Objective: A one third reduction of premature deaths from non-communicable diseases by 2030 is a target of the United Nations Sustainable Development Goal for Health. Unlike in other developed nations, premature mortality in the United States (US) is increasing. The state of Oklahoma suffers some of the greatest rates in the US of both all-cause mortality and overdose deaths. Medicaid opioids are associated with overdose death at the patient level, but the impact of this exposure on population all-cause mortality is unknown. The objective of this study was to look for an association between Medicaid spending, as proxy measure for Medicaid opioid exposure, and all-cause mortality rates in the 45–54-year-old American Indian/Alaska Native (AI/AN45-54) and non-Hispanic white (NHW45-54) populations.Methods: All-cause mortality rates were collected from the US Centers for Disease Control & Prevention Wonder Detailed Mortality database. Annual per capita (APC) Medicaid spending, and APC Medicare opioid claims, smoking, obesity, and poverty data were also collected from existing databases. County-level multiple linear regression (MLR) analyses were performed. American Indian mortality misclassification at death is known to be common, and sparse populations are present in certain counties; therefore, the two populations were examined as a combined population (AI/NHW45-54), with results being compared to NHW45-54 alone.Results: State-level simple linear regressions of AI/NHW45-54 mortality and APC Medicaid spending show strong, linear correlations: females, coefficient 0.168, (R2 0.956; P < 0.0001; CI95 0.15, 0.19); and males, coefficient 0.139 (R2 0.746; P < 0.0001; CI95 0.10, 0.18). County-level regression models reveal that AI/NHW45-54 mortality is strongly associated with APC Medicaid spending, adjusting for Medicare opioid claims, smoking, obesity, and poverty. In females: [R2 0.545; (F)P < 0.0001; Medicaid spending coefficient 0.137; P < 0.004; 95% CI 0.05, 0.23]. In males: [R2 0.719; (F)P < 0.0001; Medicaid spending coefficient 0.330; P < 0.001; 95% CI 0.21, 0.45].Conclusions: In Oklahoma, per capita Medicaid spending is a very strong risk factor for all-cause mortality in the combined AI/NHW45-54 population, after controlling for Medicare opioid claims, smoking, obesity, and poverty.