Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. As of August 2024, CMS is no longer releasing the “HealthCare.gov” metrics. Historical data between July 2023-July 2024 will remain available. The “HealthCare.gov Transitions” metrics, which are the CAA, 2023 required metrics, will continue to be released. Sources: HealthCare.gov application and policy data through May 5, 2024, and T-MSIS Analytic Files (TAF) through March 2024 (TAF version 7.1 with T-MSIS enrollment through the end of March 2024). Data include consumers in HealthCare.gov states where the first unwinding renewal cohort is due on or after the end of reporting month (state identification based on HealthCare.gov policy and application data). State data start being reported in the month when the state's first unwinding renewal cohort is due. April data include Arizona, Arkansas, Florida, Indiana, Iowa, Kansas, Nebraska, New Hampshire, Ohio, Oklahoma, South Dakota, Utah, West Virginia, and Wyoming. May data include the previous states and the following new states: Alaska, Delaware, Georgia, Hawaii, Montana, North Dakota, South Carolina, Texas, and Virginia. June data include the previous states and the following new states: Alabama, Illinois, Louisiana, Michigan, Missouri, Mississippi, North Carolina, Tennessee, and Wisconsin. July data include the previous states and Oregon. All HealthCare.gov states are included in this version of the report. Notes: This table includes Marketplace consumers who: 1) submitted a HealthCare.gov application on or after the start of each state’s first reporting month; and 2) who can be linked to an enrollment record in TAF that shows Medicaid or CHIP enrollment between March 2023 and the latest reporting month. Cumulative counts show the number of unique consumers from the included population who had a Marketplace application submitted or a HealthCare.gov Marketplace policy on or after the start of each state’s first reporting month through the latest reporting month. Net counts show the difference between the cumulative counts through a given reporting month and previous reporting months. The data used to produce the metrics are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. For example, the April 2023 reporting period extends from Monday, April 3 through Sunday, April 30. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. Data do not represent Marketplace consumers who had a confirmed Medicaid/CHIP loss. Future reporting will look at coverage transitions for people who lost Medicaid/CHIP. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data on 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in December 2023, data from December 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through November 2023. The report may include negative 'net counts,' which reflect that there were cumulatively fewer counts from one month to the next. Wyoming has negative ‘net counts’ for most of its metrics in March 2024, including 'Marketplace Consumers with Previous M

Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

Content

This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

Acknowledgements

The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

Inspiration

How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

--- Original source retains full ownership of the source dataset ---

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. Sources: HealthCare.gov application and policy data through October 6, 2024, HealthCare.gov inbound account transfer data through November 7, 2024, and T-MSIS Analytic Files (TAF) through July 2024 (TAF version 7.1). The table includes states that use HealthCare.gov. Notes: This table includes Marketplace consumers who submitted a HealthCare.gov application from March 6, 2023 - October 6, 2024 or who had an inbound account transfer from April 3, 2023 - November 7, 2024, who can be linked to an enrollment record in TAF that shows a last day of Medicaid or CHIP enrollment from March 31, 2023 - July 31, 2024. Beneficiaries with a leaving event may have continuous coverage through another coverage source, including Medicaid or CHIP coverage in another state. However, a beneficiary that lost Medicaid or CHIP coverage and regained coverage in the same state must have a gap of at least 31 days or a full calendar month. This table includes Medicaid or CHIP beneficiaries with full benefits in the month they left Medicaid or CHIP coverage. ‘Account Transfer Consumers Whose Medicaid or CHIP Coverage was Terminated’ are consumers 1) whose full benefit Medicaid or CHIP coverage was terminated and 2) were sent by a state Medicaid or CHIP agency via secure electronic file to the HealthCare.gov Marketplace in a process referred to as an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. 'Marketplace Consumers Not on Account Transfer Whose Medicaid or CHIP Coverage was Terminated' are consumers 1) who applied at the HealthCare.gov Marketplace and 2) were not sent by a state Medicaid or CHIP agency via an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. Marketplace consumers counts are based on the month Medicaid or CHIP coverage was terminated for a beneficiary. Counts include all recent Marketplace activity. HealthCare.gov data are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. HealthCare.gov data are through Sunday, October 6. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: The percentages for the 'Marketplace Consumers Not on Account Transfer whose Medicaid or CHIP Coverage was Terminated' data record group are marked as not available (NA) because the full population of consumers without an account transfer was not available for this report. Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data about 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in November 2023, data from November 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through October 2023. APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; QHP: Qualified Health Plan; NA: Not Available

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

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.

State Health IT Policy Levers

300+ Examples of Advancing Interoperability and Promoting Health IT

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains over 300 examples of health IT policy levers used by states to advance interoperability, promote health IT and support delivery system reform. The U.S Government's Office of National Coordinator for Health Information Technology (ONC) has curated this catalog as part of its Health IT State Policy Levers Compendium. It provides an exhaustive directory on the policy levers being utilized, along with information on the state enacting them and their official sources. This collection seeks to act as a comprehensive guide for government officials and healthcare providers who are interested in state-based initiatives for optimizing health information technology. Explore the strategies your own state might be using to unlock improved patient outcomes!

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides information on policy levers used by various states in the United States to promote health IT and advance interoperability. The comprehensive list includes over 300 documented examples of health IT policy levers used by these states. This catalog can be used to identify which specific policy levers are being used, as well as what activities they are associated with.

If you're interested in learning more about how states use health IT policy levers, this dataset is a great resource. It contains detailed information on each entry, including the state where it's being used, the status of that activity, a description of the activity and its purpose, and an official source for additional information about that particular entry.

Using this data set is easy - simply search for specific states or find out which kinds of activities each state is using their health IT policy levers for. You can also look up any specific application or implementation detail from each record by opening up its corresponding source URL link . With all this information at hand you can better understand how states use their health IT tools to make a difference in advancing interoperability within healthcare systems today!

Research Ideas

• It can be used to provide states with potential models of successful health IT policy levers, allowing them to learn from the experiences of other states in developing and implementing health IT legislation.
• The dataset can also be used by researchers looking to study the effectiveness of existing health care policy levers, as well as to identify any gaps that need to be filled in order for certain policies to have a greater overall impact.
• Additionally, it could be used by industry stakeholders such as hospitals or other healthcare organizations for benchmarking their own efforts related to IT implementation, such as understanding what activities are being undertaken and which sources are being used for best practices or additional resources when making decisions related to new technology implementations into an organization's operations and services

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: policy-levers-activities-catalog-csv-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------------------------| | state | The state in which the policy lever is being used. (String) | | policy_lever | Type of policy lever being used. (String) | | activity_status | Status of activity (e.g., active or inactive). (String) | | activity_description | Description of activity. (String) | | source | Source from where data is gathered from. (String) | | source_url | A link that points directly back to an original sources with additional information. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels 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. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hosp

United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data was reported at 2,469,000.000 Person in 2013. This records a decrease from the previous number of 2,689,000.000 Person for 2012. United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data is updated yearly, averaging 2,639,500.000 Person from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 3,041,000.000 Person in 2000 and a record low of 2,201,000.000 Person in 2008. United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Sum;

Users can access data related to quality of health care for each state and the District of Columbia. Background 2010 State Snapshots is a database maintained by the Agency for Healthcare Research and Quality (AHRQ). Data is based on information collected from the National Healthcare Quality Report (NHQR). 2010 State Snapshots database offers an in-depth analysis of the quality of care – by type of condition, level of care, treatment setting, race and income, and insurance status. User functionality Users can search for state data by using the interactive map and cl icking on the state. Users are given data for the most recent year (2010) and baseline data year which varies by state. Each state has information on the state dashboard which includes information on types of care, settings of care, and care by clinical area. Users also have the option to focus on diabetes care, asthma care, healthy people 2010 goals, clinical preventative services, disparities in care, payer, and variation in overtime. Data is presented in tables or visual charts. Data is available for download using excel and XML format. Data Notes Detailed information about the data is available under the “Methods” section. The website does not indicate when new data will become available.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States as of the initial date of reporting for each weekly metric. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels 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. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hosp

This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States.

For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses.

For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data.

Source: CDC National Healthcare Safety Network (NHSN).

• Data source description (updated November 15, 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 (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
• NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access 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.
• 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 reported as of December 1, 2020 are subject to thorough, routine data quality review procedures, including identifying and excluding invalid values from metric calculations and application of error correction methodology; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable. As a result of data quality implementation and submission of any backfilled data, data and metrics might fluctuate or change week-over-week after initial posting.
• Inclusion criteria and metric calculations:
• Facility types and status: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations displayed on this page. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations. Number of reporting hospitals is determined based on the NHSN unique hospital identifier and not aggregated to the CMS certification number (CCN). Only hospitals indicated as active reporters in NHSN are included.
• For occupancy metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly averages.
• For occupancy metrics beginning week ending October 12, 2024: hospitals that reported those data for Wednesday during a given week are included in the metric calculation, which are displayed as single day (i.e. Wednesday) values.
• For new hospital admissions metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly totals. Under previous reporting requirements, new hospital admissions data were reported daily to NHSN, as the number of new hospital admissions for the previous day.
• For new hospital admissions metrics beginning week ending October 12, 2024: hospitals that reported those data for an entire reporting week are included in the metric calculation, which are displayed as weekly totals. Under current reporting requirements, new admissions data are reported to represent the number of new admissions occurring on a given reporting date (rather than previous day) or during a given reporting week.
• Find full details on NHSN Hospital Respiratory Data (HRD) reporting guidance, including additional information on bed type definitions at https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Archived datasets updated during the mandatory hospital reporting period from August 1, 2020, to April 30, 2024:

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/82ci-krud/about_data
3. https://data.cdc.gov/Public-Health-Surveillance/Respiratory-Virus-Response-RVR-United-States-Hospi/9t9r-e5a3/about_data
4. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/7dk4-g6vg/about_data
5. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-Hospitalization-Metrics-by-/39z2-9zu6/about_data

Archived datasets updated during the voluntary hospital reporting period from May 1, 2024, to October 31, 2024:

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-Hospitalization-Metrics-by-Ju/ype6-idgy

Note: June 13th, 2025: Data for American Samoa (AS) for the June 1st, 2025 through June 7th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on June 13th, 2025.

June 6th, 2025: Data for American Samoa (AS) for the May 25th, 2025 through May 31th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on June 6th, 2025.

May 30th, 2025: Data for American Samoa (AS) for the May 18th, 2025 through May 24th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on May 30th, 2025.

May 23rd, 2025: Data for American Samoa (AS) for the May 11th, 2025 through May 17th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on May 23rd, 2025.

April 25th, 2025: Data for American Samoa (AS) for the April 13th, 2025 through April 19th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 25th, 2025.

April 18th, 2025: Data for American Samoa (AS) for the April 6th, 2025 through April 12th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 18th, 2025.

April 11th, 2025: Data for American Samoa (AS) for the March 30th, 2025 through April 5th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 11th, 2025.

March 28th, 2025: Data for Guam (GU) for the March 16th, 2025 through March 22nd, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on March 28th, 2025.

March 21st, 2025: Data for the Commonwealth of the Northern Mariana Islands (CNMI) for the March 9th, 2025 through March 15th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on March 21st, 2025.

March 14th, 2025: Data for American Samoa (AS) and the Commonwealth of the Northern Mariana Islands (CNMI) for the March 2nd, 2025 through March 8th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report

The USRDS is the largest and most comprehensive national ESRD surveillance system in the US (Collins et al., 2015). The USRDS contains data on all ESRD cases in the US through the Medical Evidence Report CMS-2728 which is mandated for all new patients diagnosed with ESRD (Foley and Collins, 2013). Detailed information about the USRDS can be found on their website (http://www.usrds.org). The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that _domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each _domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and _domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data stored as csv files. This dataset is associated with the following publication: Kosnik, M., D. Reif, D. Lobdell, T. Astell-Burt, X. Feng, J. Hader, and J. Hoppin. Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 14(3): e0214094, (2019).

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. ELMS records healthcare service provider applications, issues licenses, generates license renewal notices, determines license fees, issues and tracks State enforcement actions, and generates management reports. This file lists the services that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility services file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

Introduction

This US Spanish Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Spanish speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native US Spanish speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data was reported at 15,100,000.000 Person in 2013. This records a decrease from the previous number of 15,700,000.000 Person for 2012. United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data is updated yearly, averaging 16,450,000.000 Person from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 21,800,000.000 Person in 1998 and a record low of 13,900,000.000 Person in 2008. United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Sum;

This dataset includes individual catastrophic health plans available outside the Marketplace. They are available to people whose individual health plans have been cancelled and who believe that bronze-level plans in the Marketplace are too expensive. These people may apply for a hardship exemption that allows them to buy one of these plans. Not all states offer catastrophic plans outside the Marketplace. People who live in states that run their own Marketplaces may be able to participate in this program. In states with state-based Marketplaces that do offer catastrophic plans, the dataset includes listings for state departments of insurance, which can provide more information.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

Context

XYZ Pvt Ltd is an E-Commerce Company dealing in a wide range of Healthy Products combined with the power of Artificial Intelligence. But recently it has started facing an issue of HIGH Return Rates throughout India. (A return order is when the order is in transit but a customer refuses to accept it sighting different reasons)

Content

The dataset has 1600 orders with every detail ranging from city and state for geographical analysis or dates for time-series analysis, each product's category, name, cost and ID has also been given for more detailed analysis.

If there are columns you would like me to add please let me know in the comments.

The latest data has been cleaned.

Inspiration

Study the dataset to figure out the Return Rate Patterns amongst the customers. Every column has been carefully added for you to analyze which may/may not directly influence the return rates.