Most nonelderly Americans purchase health insurance through their employers, which sponsor a limited number of plans. Using a panel dataset representing over ten million insured lives, we estimate employees' preferences for different health plans and use the estimates to predict their choices if more plans were made available to them on the same terms, i.e., with equivalent subsidies and at large-group prices. Using conservative assumptions, we estimate a median welfare gain of 13 percent of premiums. A proper accounting of the costs and benefits of a transition from employer-sponsored to individually-purchased insurance should include this nontrivial gain. (JEL G22, I13, J32)

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

This dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Population Health Area (PHA) …Show full descriptionThis dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SR" - Indirectly age-standardised ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

Your Client FinMan is a financial services company that provides various financial services like loan, investment funds, insurance etc. to its customers. FinMan wishes to cross-sell health insurance to the existing customers who may or may not hold insurance policies with the company. The company recommend health insurance to it's customers based on their profile once these customers land on the website. Customers might browse the recommended health insurance policy and consequently fill up a form to apply. When these customers fill-up the form, their Response towards the policy is considered positive and they are classified as a lead.

Once these leads are acquired, the sales advisors approach them to convert and thus the company can sell proposed health insurance to these leads in a more efficient manner.

Now the company needs your help in building a model to predict whether the person will be interested in their proposed Health plan/policy given the information about:

Demographics (city, age, region etc.) Information regarding holding policies of the customer Recommended Policy Information

Data Dictionary Train Data Variable Definition ID Unique Identifier for a row City_Code Code for the City of the customers Region_Code Code for the Region of the customers Accomodation_Type Customer Owns or Rents the house Reco_Insurance_Type Joint or Individual type for the recommended insurance
Upper_Age Maximum age of the customer Lower _Age Minimum age of the customer Is_Spouse If the customers are married to each other (in case of joint insurance) Health_Indicator Encoded values for health of the customer Holding_Policy_Duration Duration (in years) of holding policy (a policy that customer has already subscribed to with the company) Holding_Policy_Type Type of holding policy Reco_Policy_Cat Encoded value for recommended health insurance Reco_Policy_Premium Annual Premium (INR) for the recommended health insurance Response (Target) 0 : Customer did not show interest in the recommended policy, 1 : Customer showed interest in the recommended policy

Test Data Variable Definition ID Unique Identifier for a row City_Code Code for the City of the customers Region_Code Code for the Region of the customers Accomodation_Type Customer Owns or Rents the house Reco_Insurance_Type Joint or Individual type for the recommended insurance Upper_Age Maximum age of the customer Lower _Age Minimum age of the customer Is_Spouse If the customers are married to each other (in case of joint insurance) Health_Indicator Encoded values for health of the customer Holding_Policy_Duration Duration (in years) of holding policy (a policy that customer has already subscribed to with the company) Holding_Policy_Type Type of holding policy Reco_Policy_Cat Encoded value for recommended health insurance Reco_Policy_Premium Annual Premium (INR) for the recommended health insurance

Variable Definition ID Unique Identifier for a row Response (Target) Probability of Customer showing interest (class 1)

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

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

Fork this kernel to get started.

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?

Explore World Bank Health, Nutrition and Population Statistics dataset featuring a wide range of indicators such as School enrollment, UHC service coverage index, Fertility rate, and more from countries like Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

School enrollment, tertiary, UHC service coverage index, Wanted fertility rate, People with basic handwashing facilities, urban population, Rural population, AIDS estimated deaths, Domestic private health expenditure, Fertility rate, Domestic general government health expenditure, Age dependency ratio, Postnatal care coverage, People using safely managed drinking water services, Unemployment, Lifetime risk of maternal death, External health expenditure, Population growth, Completeness of birth registration, Urban poverty headcount ratio, Prevalence of undernourishment, People using at least basic sanitation services, Prevalence of current tobacco use, Urban poverty headcount ratio, Tuberculosis treatment success rate, Low-birthweight babies, Female headed households, Completeness of birth registration, Urban population growth, Antiretroviral therapy coverage, Labor force, and more.

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

Snapshot img

Healthcare Fraud Detection Market Size 2025-2029

The healthcare fraud detection market size is forecast to increase by USD 1.09 billion at a CAGR of 11.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing number of patients seeking health insurance and the emergence of social media's influence on the healthcare industry. The rise in healthcare fraud cases, driven by the influx of insurance claims, necessitates robust fraud detection solutions. Social media's impact on healthcare extends to fraudulent activities, with fake claims and identity theft posing challenges. However, the deployment of healthcare fraud detection systems remains a time-consuming process, and the need for frequent upgrades to keep up with evolving fraud schemes adds complexity.
Additionally, collaborating with regulatory bodies and industry associations can help stay informed of the latest fraud trends and best practices. Overall, the market presents opportunities for innovation and growth, as the demand for effective solutions to combat fraudulent activities continues to rise. Companies must navigate these challenges by investing in advanced technologies, such as machine learning and artificial intelligence, to streamline deployment and enhance fraud detection capabilities.

What will be the Size of the Healthcare Fraud Detection Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market encompasses various solutions and services designed to mitigate fraudulent activities in Medicaid services and health insurance. Data analytics plays a pivotal role in this domain, with statistical methods and data science techniques used to identify fraudulent healthcare activities. Prescriptive analytics and machine learning algorithms enable the prediction of potential fraudulent claims and billing schemes. Medical services, including pharmacy billing fraud and prescription fraud, are prime targets for offenders. Identity theft and social media are also significant contributors to healthcare fraud costs. Payment integrity is crucial for insurers to minimize financial losses, making fraud detection a priority.

On-premise and cloud-based solutions offer analytics capabilities to combat fraud. Descriptive analytics provides insights into historical data, while predictive analytics and prescriptive analytics offer proactive fraud detection. Despite the advancements in fraud detection, data limitations pose challenges. The use of artificial intelligence and machine learning in fraud detection is increasing, providing more accurate and efficient solutions. Insurance claims review is a critical component of fraud detection, with fraudulent claims costing billions annually. Fraudsters continue to evolve their tactics, necessitating the need for advanced fraud detection solutions.

How is this Healthcare Fraud Detection Industry segmented?

The healthcare fraud detection industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Descriptive analytics
  Predictive analytics
  Prescriptive analytics


End-user

  Private insurance payers
  Third-party administrators (TPAs)
  Government agencies
  Hospitals and healthcare providers


Delivery Mode

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The Descriptive analytics segment is estimated to witness significant growth during the forecast period. In the dynamic landscape of healthcare, Anomalies Detection and Healthcare Fraud Analytics play a pivotal role in safeguarding Financial Resources from Fraudulent Healthcare Activities. Descriptive analytics, a foundational type of analytics, forms the backbone of this industry. With its ability to aggregate and examine vast healthcare data, descriptive analytics identifies trends and operational performance insights. It is widely used in various departments, from Healthcare IT adoption to Urgent care, and supports Insurance Claims Review processes. Cloud-Based Solutions and On-Premises Solutions are two delivery models that cater to diverse organizational needs. Machine Learning and Statistical Methods are integral to advanced analytics, including Prescriptive analytics and Predictive analytics, which uncover intricate patterns and prevent Fraudulent Claims.

Social Media and Data Analytics offer valuable insights into potential Fraudulent Activities, while Real-Time Analytics ensure Payment Integrity in Healthca

Success.ai’s Healthcare Industry Leads Data for the North American Healthcare Sector provides businesses with a comprehensive dataset designed to connect with healthcare organizations, decision-makers, and key stakeholders across the United States, Canada, and Mexico. Covering hospitals, pharmaceutical firms, biotechnology companies, and medical equipment providers, this dataset delivers verified contact information, firmographic details, and actionable business insights.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is your key to success in the North American healthcare market.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, clinical managers, procurement officers, and compliance leaders.
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   • Includes profiles of organizations such as hospitals, private clinics, research facilities, biotech firms, and medical supply distributors.
   • Gain visibility into the unique healthcare dynamics of the United States, Canada, and Mexico, including regional trends, regulatory differences, and market opportunities.

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   • Real-time updates reflect changes in leadership, organizational structures, service offerings, and market activities.
   • Ensure your outreach and strategy stay relevant and aligned with the rapidly evolving healthcare industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and compliant use of data for your campaigns.

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• 170M+ Verified Professional Profiles: Engage with decision-makers and influencers across North America’s healthcare sector.
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• Decision-Maker Contacts: Connect with CEOs, CMOs, clinical directors, R&D leaders, and procurement managers shaping healthcare strategies.
• Operational Insights: Understand supply chains, service lines, and product pipelines within the healthcare ecosystem.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and connect with healthcare leaders driving innovation, procurement decisions, and patient care delivery.
   • Engage with professionals responsible for technology adoption, regulatory compliance, and resource management.

2. Advanced Filters for Precision Targeting

   • Filter companies by sector (hospitals, biotech, pharma, medical devices), geographic location, revenue size, or workforce composition.
   • Tailor your outreach to align with the unique needs and priorities of North American healthcare organizations.

3. Market and Operational Insights

   • Analyze trends such as telemedicine adoption, value-based care initiatives, and investments in AI and automation.
   • Leverage these insights to position your solutions effectively within a rapidly transforming industry.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight your value propositions, and improve engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, medical devices, or consulting services to healthcare organizations seeking operational improvements.
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2. Marketing and Demand Generation

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   • Leverage verified contact data to launch impactful email and multi-channel marketing campaigns.

3. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal teams responsible for adhering to healthcare regulations and standards.
   • Present solutions for streamlined reporting, risk management, and quality assurance processes.

4. Recruitment and Workforce Optimization

   • Engage HR professionals and hiring managers in recruiting healthcare talent, from clinical staff to administrative roles.
   • Provide staffing solutions, training platforms, or workforce management tools tailored to healthcare environments.

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   • Access premium-q...

Social and economic situation of old people living in private households. Topics: Residential furnishings and size of residence; family sharing a residence; satisfaction with residence; frequency and quality of contact with children and environment; concepts of old people´s homes; reasons for and against a residence for older people and an old people´s home; subjective judgement on economic situation; cost of living; sources of financial support; health situation; complaints; illnesses; need of care and medical care; significance of work and employment in old age; attitude to work, retirement and old age (scales); identification with an age group; isolation; problems of provision for one´s old age; health insurance; use of advice centers for old people; membership in clubs and organizations; satisfaction with traffic conditions; religiousness; local residency. Demography: age (classified); sex; marital status; number of children; religious denomination; occupation; employment; professional career; income; household income; size of household; regional origins; refugee status; social surroundings. Interviewer rating: situation of residence in the city and in the building; size of residence and condition of residence; weekday of interview; city size. Soziale und wirtschaftliche Situation alter Menschen, die in Privathaushalten leben. Themen: Wohnungsausstattung und Wohnungsgröße; Wohngemeinschaft der Familie; Zufriedenheit mit der Wohnung; Häufigkeit und Qualität des Kontaktes zu den Kindern und zur Umwelt; Vorstellungen von Altenheimen; Gründe für und gegen Altenwohnung und Altenheim; subjektive Beurteilung der wirtschaftlichen Lage; Lebenshaltungskosten; Quellen finanzieller Unterstützung; gesundheitliche Situation; Beschwerden; Krankheiten; Pflegebedürftigkeit und ärztliche Versorgung; Bedeutung von Arbeit und Beschäftigung im Alter; Einstellung zur Arbeit, zum Ruhestand und zum Alter (Skalen); Identifizierung mit einer Altersgruppe; Isolation; Probleme der Altersversorgung; Krankenversicherung; Nutzung von Beratungsstellen für alte Menschen; Mitgliedschaft in Vereinen und Organisationen; Zufriedenheit mit den Verkehrsverhältnissen; Religiosität; Ortsansässigkeit. Demographie: Alter (klassiert); Geschlecht; Familienstand; Kinderzahl; Konfession; Beruf; Berufstätigkeit; Berufslaufbahn; Einkommen; Haushaltseinkommen; Haushaltsgröße; regionale Herkunft; Flüchtlingsstatus; soziales Umfeld. Interviewerrating: Lage der Wohnung in der Stadt und im Haus; Wohnungsgröße und Zustand der Wohnung; Wochentag des Interviews; Ortsgröße.

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).

description: This dataset contains aggregate Medicaid payments, and counts for eligible recipients and recipients served by month and county in Iowa, starting with month ending 1/31/2011. Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.; abstract: This dataset contains aggregate Medicaid payments, and counts for eligible recipients and recipients served by month and county in Iowa, starting with month ending 1/31/2011. Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: 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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Request I believe the above scheme needs to be put in place urgently. Can you please answer the following questions: 1. How many people have applied to you for Ill Health Retirement with Long Covid? 2. How many people have been rejected for Tier One and/or Tier Two levels of IHR when applying with Long Covid? 3. What evidence (listing guidance and research evidence) are being used to reject or confirm applications for IHR with Long Covid? Response Question 1 & 2 A copy of the information is attached. Question 3 Each Scheme Medical Adviser (SMA) is expected to adopt evidence-based practice in arriving at a decision. They do this by combining the following: Medical evidence provided in the Scheme member’s application, Further medical evidence that the SMA may have requested from the Scheme member’s treating healthcare professionals, Information that the employer may have provided in Part A of Form AW33E (e.g. demands of the work duties, any workplace adjustments tried, and the effectiveness of such adjustments), Information that the Scheme member may have provided in Part B of Form AW33E (for example, how long COVID affects them), Current medical literature on long COVID, And the SMA’s occupational health expertise. When assessing ill-health retirement applications from scheme members who have long COVID, the SMA might consult the following guidance and research evidence: • The Society of Occupational Medicine (SOM): ‘Long COVID and Return to Work – What Works?’ (https://www.som.org.uk/sites/som.org.uk/files/Long_COVID_and_Return_to_Work_What_Works_0.pdf) • The Faculty of Occupational Medicine (FOM): ‘Guidance for healthcare professionals on return to work for patients with post-COVID syndrome’ (https://www.fom.ac.uk/wp-content/uploads/FOM-Guidance-post-COVID_healthcare-professionals.pdf) • Occupational and Environmental Medicine (academic journal of the FOM: https://oem.bmj.com) • Occupational Medicine (academic journal of the SOM: https://academic.oup.com/occmed?login=false) • Industrial Injuries Advisory Council publication: ‘COVID-19 and Occupational Impacts’ (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1119955/covid-19-and-occupational-impacts.pdf) • NICE: https://cks.nice.org.uk/topics/long-term-effects-of-coronavirus-long-covid • Nature. An example of a recent publication in this journal is Davis, H., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Long covid: major findings, mechanisms and recommendations. Nature Reviews Microbiology, 21(3), 133-146. Full text available at https://www.nature.com/articles/s41579-022-00846-2 • British Medical Journal (BMJ) • Journal of the American Medical Association (JAMA) • The Lancet • New England Journal of Medicine In summary, the SMA is expected to adopt an individual approach to each case and use careful clinical judgement when applying the medical research literature and guidance to the specific medical circumstances of a Scheme member with long COVID. Data Queries If you have any queries regarding the data provided, or if you plan on publishing the data please contact foirequests@nhsbsa.nhs.uk ensuring you quote the above reference. This is important to ensure that the figures are not misunderstood or misrepresented. If you plan on producing a press or broadcast story based upon the data please contact communicationsteam@nhsbsa.nhs.uk This is important to ensure that the figures are not misunderstood or misrepresented.

Healthcare NLP Solution Market Outlook

The global Healthcare NLP Solution market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach around USD 7.5 billion by 2032, exhibiting a CAGR of 17.1% during the forecast period. This impressive growth trajectory is primarily driven by the increasing adoption of advanced technologies in healthcare, such as natural language processing (NLP), aimed at improving patient care and operational efficiency.

One significant growth factor for the Healthcare NLP Solution market is the rising volume of unstructured clinical data. Healthcare organizations generate massive amounts of data, including clinical notes, patient records, and research papers. Traditional data processing methods are often inadequate to handle this unstructured data efficiently. NLP solutions can process, analyze, and interpret this data to extract meaningful insights, thus supporting clinical decision-making and improving patient outcomes. Consequently, the demand for NLP solutions in healthcare is surging.

Another crucial growth driver for the market is the increasing focus on precision medicine and personalized healthcare. NLP solutions enable healthcare providers to analyze large datasets to identify patterns and trends that can help in personalized treatment plans. By leveraging NLP technologies, clinicians can tailor treatments to individual patient profiles, thus enhancing the effectiveness of medical interventions. This personalized approach not only improves patient care but also contributes to the rapid growth of the Healthcare NLP Solution market.

Moreover, the integration of NLP solutions with electronic health records (EHRs) is significantly boosting market growth. EHRs have become ubiquitous in healthcare settings, and the addition of NLP capabilities enhances their utility by enabling more effective data retrieval and analysis. This integration facilitates better patient management, reduces the likelihood of errors, and improves clinical workflows. As healthcare providers continue to adopt EHR systems, the demand for integrated NLP solutions is anticipated to grow, further propelling market expansion.

Natural Language Processing (NLP) Software is at the forefront of transforming the healthcare industry by enabling the efficient processing of unstructured data. This software leverages advanced algorithms to understand and interpret human language, making it possible to extract valuable insights from clinical notes, patient feedback, and research articles. By automating these processes, NLP software reduces the time and effort required for data analysis, allowing healthcare professionals to focus more on patient care. The integration of NLP software into healthcare systems is not only enhancing operational efficiency but also paving the way for more personalized and precise medical treatments. As the demand for data-driven decision-making grows, the role of NLP software in healthcare is becoming increasingly indispensable.

From a regional perspective, North America currently holds the largest market share in the Healthcare NLP Solution market, driven by the early adoption of advanced healthcare technologies and substantial investments in healthcare infrastructure. However, the Asia Pacific region is expected to exhibit the highest CAGR during the forecast period. Factors such as increasing healthcare expenditures, growing awareness of advanced healthcare technologies, and supportive government initiatives are driving market growth in this region. Europe and Latin America are also showing significant growth potential, driven by improving healthcare systems and increasing adoption of digital health solutions.

Component Analysis

The component segment of the Healthcare NLP Solution market is bifurcated into software and services. The software segment includes NLP tools and platforms designed to analyze unstructured clinical data, while the services segment encompasses implementation, training, and maintenance services required to deploy these solutions effectively. The software segment is currently dominating the market, driven by the increasing need for advanced analytics tools to manage and interpret vast amounts of healthcare data.

NLP software solutions are gaining traction due to their ability to streamline clinical documentation processes. These tools can automatically transcribe and structure clinical notes, significantly reducing

No updated data on people living with HIV (PLHIV) in Japan have been available since 2015, leaving a critical gap in understanding the current status of care and treatment. Therefore, this study aimed to conduct a nationwide evaluation of the second and third goals of the “90-90-90 target” defined by UNAIDS between 2016 and 2020. The study utilized data from approximately 360 core hospitals through structured questionnaires and the National Database of Health Insurance Claims and Specific Health Checkups (NDB). Key findings revealed that over 95% of diagnosed outpatients were retained in care (second 90), and more than 99% achieved successful viral suppression (third 90). A significant transition to single-tablet regimens and newer, highly effective antiretroviral drugs was observed, optimizing treatment adherence and outcomes. These results underscore the efficacy of Japan’s universal health insurance system in ensuring consistent access to HIV care and treatment, supporting both individual patient outcomes and national surveillance efforts.

Introduction

The Vietnamese Healthcare Chat Dataset is a rich collection of over 10,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in Vietnamese-speaking regions.

Participant & Chat Overview

Topic Diversity

The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

Language Diversity & Realism

This dataset reflects the natural flow of Vietnamese healthcare communication and includes:

These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

Conversational Flow & Structure

Conversations range from simple inquiries to complex advisory sessions, including:

Each conversation typically includes these structural components:

This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

Data Format & Structure

Available in JSON, CSV, and TXT formats, each conversation includes:

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

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: 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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.