The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (2017, 2018 and 2019) concerning non-life health insurance portfolio. This dataset comprises 228,711 rows and 42 columns. Each row signifies a insured (individual) policy, while each column represents a distinct variable.

This dataset contains information on the relationship between personal attributes (age, gender, BMI, family size, smoking habits), geographic factors, and their impact on medical insurance charges. It can be used to study how these features influence insurance costs and develop predictive models for estimating healthcare expenses. Age: The insured person's age.

Sex: Gender (male or female) of the insured.

BMI (Body Mass Index): A measure of body fat based on height and weight.

Children: The number of dependents covered.

Smoker: Whether the insured is a smoker (yes or no).

Region: The geographic area of coverage.

Charges: The medical insurance costs incurred by the insured person.

Insurance Dataset for Predicting Health Insurance Premiums in the US" is a collection of data on various factors that can influence medical costs and premiums for health insurance in the United States. The dataset includes information on 10 variables, including age, gender, body mass index (BMI), number of children, smoking status, region, income, education, occupation, and type of insurance plan. The dataset was created using a script that generated a million records of randomly sampled data points, ensuring that the data represented the population of insured individuals in the US. The dataset can be used to build and test machine learning models for predicting insurance premiums and exploring the relationship between different factors and medical costs.

This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the 2016 MarketScan® Commercial Claims and Encounters Data (CCAE) is produced by Truven Health Analytics, a division of IBM Watson Health. The CCEA data contain a convenience sample of insurance claims information from person with employer-sponsored insurance and their dependents, including 43.6 million person years of data. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS MarketScan analyses can be found on the VEHSS MarketScan webpage (cdc.gov/visionhealth/vehss/data/claims/marketscan.html). Information on available Medicare claims data can be found on the IBM MarketScan website (https://marketscan.truvenhealth.com). The VEHSS MarketScan summary dataset was last updated November 2019.

According to a global survey in 2024, ** percent of health insurers had partially employed artificial intelligence in customer service areas. Furthermore, ** percent of health insurers had AI technology in the area of claims management.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

United States Health Insurance: Premium Per Member Per Month data was reported at 364.000 USD in Sep 2024. This stayed constant from the previous number of 364.000 USD for Jun 2024. United States Health Insurance: Premium Per Member Per Month data is updated quarterly, averaging 262.000 USD from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 364.000 USD in Sep 2024 and a record low of 178.000 USD in Sep 2013. United States Health Insurance: Premium Per Member Per Month data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

Graph and download economic data for Producer Price Index by Industry: Direct Health and Medical Insurance Carriers: Indemnity Health Insurance Plans (PCU5241145241142) from Dec 2002 to Sep 2025 about medical, health, insurance, PPI, industry, inflation, price index, indexes, price, and USA.

Introduction

Critical Illness Insurance Statistics: Critical illness insurance is a form of coverage that provides a one-time payout when the policyholder is diagnosed with specific severe medical conditions like cancer, heart attack, or stroke.

Unlike regular health insurance which covers medical expenses, critical illness insurance offers financial support for non-medical costs related to the illness. Such as lost income or additional expenses not covered by standard insurance.

The lump-sum payment is given upon the diagnosis of a covered critical illness, offering flexibility in how the funds are used.

This type of insurance aims to ease financial burdens during a challenging period, allowing individuals to focus on recovery without the added stress of economic consequences.

It's essential to carefully review policy details, as coverage can vary in terms of conditions covered, exclusions, and other specifics.

Context

People are always confused about their medical insurance and don't know the cost of insurance at different ages and conditions. This data is useful for these people and is useful to make predictions of the insurance cost they will have to pay.

Content

The data provider is unknown and all credit goes to the person. Data may not be sufficient for practical purpose and is solely for education and practice.

Acknowledgements

Data collection is one thing and data cleaning and preprocessing is other. The resources on YouTube is enough to learn these basics.

Inspiration

The KAGGLE community is very inspiring and is the best way to learn everything we need to know in Data Science and I love it.

According to a survey on financial literacy conducted in Malaysia, as of September 2024, ** percent of the respondents said they owned personal medical insurance. Meanwhile, ** percent of respondents claimed they did not have either personal or company issued medical insurance.

This statistic shows the number of people covered by the basic medical insurance program in China from 2014 to 2024. In 2024, about **** billion people had a basic medical insurance in China, which was around 95 percent of the total population.

Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

The India Health and Medical Insurance Market is Segmented by Policy Type (Individual Health Insurance, Family Floater & Group Health, and More), Coverage Type (In-Patient Hospitalization, Out-Patient & Day-Care, and More), Demographic (0-18, 19-45 Years, and More), Provider Type (Public, Private Sector, and More), Distribution Channel (Agents & Brokers, and More), and Region. The Market Forecasts are Provided in Value (USD).

Business Analyst Report

The US Health and Medical Insurance Market is Segmented by Coverage Type (Employer-Sponsored, Individual (ACA / Non-Group), and More), Plan Type (HMO, PPO, EPO, and More), Insurance Type (Major Medical (Comprehensive), Medicare Supplement, and More), Distribution Channel (Direct To Consumer, Brokers & Agents, and More), and Region (Northeast, Midwest, and More). The Market Forecasts are Provided in Terms of Value (USD).

Graph and download economic data for Producer Price Index by Industry: Direct Health and Medical Insurance Carriers: Individual Comprehensive Medical Service Plans (PCU52411452411410103) from Dec 2000 to Aug 2025 about individual, medical, health, insurance, services, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Medical Malpractice Insurance (PCU924126924126402) from Jun 1998 to Sep 2025 about property-casualty, premium, medical, insurance, PPI, industry, inflation, price index, indexes, price, and USA.

United States Health Insurance: Profit Margin data was reported at 1.900 % in Sep 2024. This records a decrease from the previous number of 2.700 % for Jun 2024. United States Health Insurance: Profit Margin data is updated quarterly, averaging 3.000 % from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 5.300 % in Jun 2020 and a record low of -2.100 % in Mar 2016. United States Health Insurance: Profit Margin data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

Japan Life Insurance: Premium: Medical data was reported at 35.895 JPY bn in 2018. This records an increase from the previous number of 35.592 JPY bn for 2017. Japan Life Insurance: Premium: Medical data is updated yearly, averaging 33.471 JPY bn from Mar 1990 (Median) to 2018, with 29 observations. The data reached an all-time high of 35.895 JPY bn in 2018 and a record low of 13.598 JPY bn in 1990. Japan Life Insurance: Premium: Medical data remains active status in CEIC and is reported by The Life Insurance Association of Japan. The data is categorized under Global Database’s Japan – Table JP.Z021: Life Insurance Statistics.