Context

In the United States, Medicare is a single-payer, national social insurance program administered by the U.S. federal government since 1966. It provides health insurance for Americans aged 65 and older who have worked and paid into the system through the payroll tax. Source: https://en.wikipedia.org/wiki/Medicare_(United_States)

Content

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 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.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:medicare

https://cloud.google.com/bigquery/public-data/medicare

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

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Inspiration

What is the total number of medications prescribed in each state?

What is the most prescribed medication in each state?

What is the average cost for inpatient and outpatient treatment in each city and state?

Which are the most common inpatient diagnostic conditions in the United States?

Which cities have the most number of cases for each diagnostic condition?

What are the average payments for these conditions in these cities and how do they compare to the national average?

Key Table Information.Table Title.Medicaid/Means-Tested Public Coverage by Sex by Age.Table ID.ACSDT1Y2024.C27007.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and ...

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...

2008-2024. American Lung Association. Cessation Coverage. Medicaid data compiled by the Centers for Disease Control and Prevention’s Office on Smoking and Health were obtained from the State Tobacco Cessation Coverage Database, developed and administered by the American Lung Association. Data from 2008-2012 are reported on an annual basis; beginning in 2013 data are reported on a quarterly basis. Data include state-level information on Medicaid coverage of approved medications by the Food and Drug Administration (FDA) for tobacco cessation treatment; types of counseling recommended by the Public Health Service (PHS) and barriers to accessing cessation treatment. Note: Section 2502 of the Patient Protection and Affordable Care Act requires all state Medicaid programs to cover all FDA-approved tobacco cessation medications as of January 1, 2014. However, states are currently in the process of modifying their coverage to come into compliance with this requirement. Data in the STATE System on Medicaid coverage of tobacco cessation medications reflect evidence of coverage that is found in documentable sources, and may not yet reflect medications covered under this requirement.

ImportanceThe Affordable Care Act (ACA) has expanded access to health insurance for millions of Americans, but the impact of Medicaid expansion on healthcare delivery and utilization remains uncertain.ObjectiveTo determine the early impact of the Medicaid expansion component of ACA on hospital and ED utilization in California, a state that implemented the Medicaid expansion component of ACA and Florida, a state that did not.DesignAnalyze all ED encounters and hospitalizations in California and Florida from 2009 to 2014 and evaluate trends by payer and diagnostic category. Data were collected from State Inpatient Databases, State Emergency Department Databases and the California Office of Statewide Health Planning and Development.SettingHospital and ED encounters.ParticipantsPopulation-based study of California and Florida state residents.ExposureImplementation of Medicaid expansion component of ACA in California in 2014.Main outcomes or measuresChanges in ED visits and hospitalizations by payer, percentage of patients hospitalized after an ED encounter, top diagnostic categories for ED and hospital encounters.ResultsIn California, Medicaid ED visits increased 33% after Medicaid expansion implementation and self-pay visits decreased by 25% compared with a 5.7% increase in the rate of Medicaid patient ED visits and a 5.1% decrease in rate of self-pay patient visits in Florida. In addition, California experienced a 15.4% increase in Medicaid inpatient stays and a 25% decrease in self pay stays. Trends in the percentage of patients admitted to the hospital from the ED were notable; a 5.4% decrease in hospital admissions originating from the ED in California, and a 2.1% decrease in Florida from 2013 to 2014.Conclusions and relevanceWe observed a significant shift in payer for ED visits and hospitalizations after Medicaid expansion in California without a significant change in top diagnoses or overall rate of these ED visits and hospitalizations. There appears to be a shift in reimbursement burden from patients and hospitals to the government without a dramatic shift in patterns of ED or hospital utilization.

Key Table Information.Table Title.Allocation of Medicaid/Means-Tested Public Coverage.Table ID.ACSDT1Y2024.B992707.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and...

Context

The Healthcare Common Procedure Coding System (HCPCS, often pronounced by its acronym as "hick picks") is a set of health care procedure codes based on the American Medical Association's Current Procedural Terminology (CPT).

HCPCS includes three levels of codes: Level I consists of the American Medical Association's Current Procedural Terminology (CPT) and is numeric. Level II codes are alphanumeric and primarily include non-physician services such as ambulance services and prosthetic devices, and represent items and supplies and non-physician services, not covered by CPT-4 codes (Level I). Level III codes, also called local codes, were developed by state Medicaid agencies, Medicare contractors, and private insurers for use in specific programs and jurisdictions. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) instructed CMS to adopt a standard coding systems for reporting medical transactions. The use of Level III codes was discontinued on December 31, 2003, in order to adhere to consistent coding standards.

Content

Classification of procedures performed for patients is important for billing and reimbursement in healthcare. The primary classification system used in the United States is Healthcare Common Procedure Coding System (HCPCS), maintained by Centers for Medicare and Medicaid Services (CMS). This system is divided into two levels: level I and level II.

Level I HCPCS codes classify services rendered by physicians. This system is based on Common Procedure Terminology (CPT), a coding system maintained by the American Medical Association (AMA). Level II codes, which are the focus of this public dataset, are used to identify products, supplies, and services not included in level I codes. The level II codes include items such as ambulance services, durable medical goods, prosthetics, orthotics and supplies used outside a physician’s office.

Given the ubiquity of administrative data in healthcare, HCPCS coding systems are also commonly used in areas of clinical research such as outcomes based research.

Update Frequency: Yearly

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Acknowledgements

https://bigquery.cloud.google.com/table/bigquery-public-data:cms_codes.hcpcs

https://cloud.google.com/bigquery/public-data/hcpcs-level2

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What are the descriptions for a set of HCPCS level II codes?

The U.S. Department of Health and Human Services (HHS) via the Health Resources and Services Administration (HRSA) is releasing American Rescue Plan payments to providers and suppliers who have served rural Medicaid, Children's Health Insurance Program (CHIP), and Medicare beneficiaries from January 1, 2019 through September 30, 2020. The dataset will be updated as additional payments are released. Data does not reflect recipients’ attestation status, returned payments, or unclaimed funds.

This indicator provides information about medically underserved areas and/or populations (MUA/Ps), as determined by the federal Health Resources and Services Administration (HRSA). Each designated area includes multiple census tracts.State Primary Care Offices submit applications to HRSA to designate specific areas within counties as MUA/Ps. The MUA/P designation is made using the Index of Medical Underservice (IMU) score, which includes four components: provider per 1,000 population, percent of population under poverty, percent of population ages 65 years and older, and infant mortality rate. The IMU scores ranges from 0-100. Lower scores indicate higher needs. An IMU score of 62 or below qualifies for designation as an MUA/P. Note: if an area is not designated as an MUA/P, it does not mean it is not underserved, only that an application has not been filed for the area and that official designation has not been given.The MUAs within Los Angeles County consist of groups of urban census tracts (namely service areas). MUPs have a shortage of primary care health services for a specific population within a geographic area. These populations may face economic, cultural, or language barriers to health care, such as: people experiencing homelessness, people who are low-income, people who are eligible for Medicaid, Native Americans, or migrant farm workers. All the MUPs that have been designated within Los Angeles County are among low-income populations of selected census tract groups. Due to the nature of the designation process, a census tract may be designated as both an MUA and an MUP and as multiple MUAs. MUA/P designations help establish health maintenance organizations or community health centers in high-need areas.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
. This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description(updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN hospital data reporting guidance at https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6 , 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics

This measure answers the question of what number and percentage of residents are living below the federal poverty level, which means they meet certain threshold set by a set of parameters and computation performed by the Census Bureau. Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). Data collected from the U.S. Census Bureau, American Communities Survey (1yr), Poverty Status in the Past 12 Months (Table S1701). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

Patient socio-demographic and health characteristics.

This dataset includes the primary language of newly Medi-Cal eligible individuals who identified their primary language as English, Spanish, Vietnamese, Mandarin, Cantonese, Arabic, Other Non-English, Armenian, Russian, Farsi, Korean, Tagalog, Other Chinese Languages, Hmong, Cambodian, Portuguese, Lao, French, Thai, Japanese, Samoan, Other Sign Language, American Sign Language (ASL), Turkish, Ilacano, Mien, Italian, Hebrew, and Polish, by reporting period. The primary language data is from the Medi-Cal Eligibility Data System (MEDS) and includes eligible individuals without prior Medi-Cal eligibility. This dataset is part of the public reporting requirements set forth in California Welfare and Institutions Code 14102.5.

Adjusted odds ratios of prevalence and persistence of CRN during pandemic and correlates.

We are releasing data that compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of May 15, 2020. This data is already available on other websites, but this chart brings the information together into one view for comparison. You can find additional information on the Accelerated and Advance Payments at the following links:

Fact Sheet: https://www.cms.gov/files/document/Accelerated-and-Advanced-Payments-Fact-Sheet.pdf;

Zip file on providers in each state: https://www.cms.gov/files/zip/accelerated-payment-provider-details-state.zip

Medicare Accelerated and Advance Payments State-by-State information and by Provider Type: https://www.cms.gov/files/document/covid-accelerated-and-advance-payments-state.pdf.

This file was assembled by HHS via CMS, HRSA and reviewed by leadership and compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of December 4, 2020.

HHS Provider Relief Fund President Trump is providing support to healthcare providers fighting the coronavirus disease 2019 (COVID-19) pandemic through the bipartisan Coronavirus Aid, Relief, & Economic Security Act and the Paycheck Protection Program and Health Care Enhancement Act, which provide a total of $175 billion for relief funds to hospitals and other healthcare providers on the front lines of the COVID-19 response. This funding supports healthcare-related expenses or lost revenue attributable to COVID-19 and ensures uninsured Americans can get treatment for COVID-19. HHS is distributing this Provider Relief Fund money and these payments do not need to be repaid. The Department allocated $50 billion of the Provider Relief Fund for general distribution to Medicare facilities and providers impacted by COVID-19, based on eligible providers' net reimbursement. It allocated another $22 billion to providers in areas particularly impacted by the COVID-19 outbreak, rural providers, and providers who serve low-income populations and uninsured Americans. HHS will be allocating the remaining funds in the near future.

As part of the Provider Relief Fund distribution, all providers have 45 days to attest that they meet certain criteria to keep the funding they received, including public disclosure. As of May 15, 2020, there has been a total of $34 billion in attested payments. The chart only includes those providers that have attested to the payments by that date. We will continue to update this information and add the additional providers and payments once their attestation is complete.

CMS Accelerated and Advance Payments Program On March 28, 2020, to increase cash flow to providers of services and suppliers impacted by the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Medicare & Medicaid Services (CMS) expanded the Accelerated and Advance Payment Program to a broader group of Medicare Part A providers and Part B suppliers. Beginning on April 26, 2020, CMS stopped accepting new applications for the Advance Payment Program, and CMS began reevaluating all pending and new applications for Accelerated Payments in light of the availability of direct payments made through HHS’s Provider Relief Fund.

Since expanding the AAP program on March 28, 2020, CMS approved over 21,000 applications totaling $59.6 billion in payments to Part A providers, which includes hospitals, through May 18, 2020. For Part B suppliers—including doctors, non-physician practitioners and durable medical equipment suppliers— during the same time period, CMS approved almost 24,000 applications advancing $40.4 billion in payments. The AAP program is not a grant, and providers and suppliers are required to repay the loan.

CMS has published AAP data, as required by the Continuing Appropriations and Other Extensions Act of 2021, on this website: https://www.cms.gov/files/document/covid-medicare-accelerated-and-advance-payments-program-covid-19-public-health-emergency-payment.pdf. Requests for additional data related to the program must be submitted through the CMS FOIA office. For more information on how to submit a FOIA request please visit our website at https://www.cms.gov/Regulations-and-Guidance/Legislation/FOIA. The PRF is administered by the Health Resources & Services Administration (HRSA). For more information on how to submit a request for unpublished program data from HRSA, please visit https://www.hrsa.gov/foia/index.html.

Provider Relief Fund Data - https://data.cdc.gov/Administrative/Provider-Relief-Fund-COVID-19-High-Impact-Payments/b58h-s9zx

BackgroundHealth literacy experts and the American Medical Association have developed recommended communication techniques for healthcare providers given that effective communication has been shown to greatly improve health outcomes. The purpose of this study was to determine the number and types of communication techniques routinely used by Maryland physicians.MethodsIn 2010, a 30-item survey was mailed to a random sample of 1,472 Maryland family physicians and pediatricians, with 294 surveys being returned and usable. The survey contained questions about provider and practice characteristics, and 17 items related to communication techniques, including seven basic communication techniques. Physicians’ use of recommended communication techniques was analyzed using descriptive statistics, analysis of variance, and ordinary least squares regression.ResultsFamily physicians routinely used an average of 6.6 of the 17 total techniques and 3.3 of the seven basic techniques, whereas pediatricians routinely used 6.4 and 3.2 techniques, respectively. The use of simple language was the only technique that nearly all physicians routinely utilized (Family physicians, 91%; Pediatricians, 93%). Physicians who had taken a communications course used significantly more techniques than those who had not. Physicians with a low percentage of patients on Medicaid were significantly less likely to use the recommended communication techniques compared to those providers who had high proportion of their patient population on Medicaid.ConclusionsOverall, the use of recommended communication techniques was low. Additionally, many physicians were unsure of the effectiveness of several of the recommended techniques, which could suggest that physicians are unaware of valuable skills that could enhance their communication. The findings of this study suggest that communications training should be given a higher priority in the medical training process in the United States.

Demographic Characteristics of Patient Subgroups with the most predictive sequences.