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.

Aims/IntroductionThis study aimed to estimate the prevalence of gestational diabetes mellitus (GDM) and use of anti-diabetic medications for patients with GDM in Korea, using data of the period 2007–2011 from the Health Insurance Review and Assessment (HIRA) database, which includes the claims data of 97% of the Korean population.Materials and MethodsWe used the Healthcare Common Procedure Coding System codes provided by the HIRA to identify women with delivery in the HIRA database between 2009 and 2011. GDM was defined according to ICD-10 codes, and patients with pre-existing diabetes between January 1, 2007 and pregnancy were excluded. A Poisson regression was performed to evaluate the trends in annual prevalence rates.ResultsThe annual numbers of deliveries in 2009–2011 were 479,160 in 2009, 449,747 in 2010, and 377,374 in 2011. The prevalence of GDM during that period was 7.5% in 2009–2011: 5.7% in 2009, 7.8% in 2010, and 9.5% in 2011. The age-stratified analysis showed that the prevalence of GDM was highest in women aged 40–44 years, at 10.6% in 2009–2011, and that the annual prevalence significantly increased even in young women aged 20–29 years during that period (P < 0.05). More than 95% of the patients with GDM did not take any anti-diabetic medication. Among the anti-diabetic medications prescribed for patients with GDM, insulin was most commonly prescribed (for >98% of the patients with GDM on medication).ConclusionsThe prevalence of GDM in Korean women recently reached 5.7–9.5% in recent years. This represents a public health concern that warrants proper screening and medical care for GDM in women during the childbearing years.

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

BackgroundIn terms of medical costs, prostate cancer is on the increase as one of the most costly cancers, posing a tremendous economic burden, but evidence on the health care utilization and medical expenditure of prostate cancer has been absent in China.ObjectiveThis study aimed to analyze health care utilization and direct medical costs of patients with prostate cancer in China.MethodsHealth care service data with a national representative sample of basic medical insurance beneficiaries between 2015 and 2017 were obtained from the China Health Insurance Association database. We conducted descriptive and statistical analyses of health care utilization, annual direct medical costs, and composition based on cancer-related medical records. Health care utilization was measured by the number of hospital visits and the length of stay.ResultsA total of 3,936 patients with prostate cancer and 24,686 cancer-related visits between 2015 and 2017 were identified in the database. The number of annual outpatient and inpatient visits per patient differed significantly from 2015 to 2017. There was no obvious change in length of stay and annual direct medical costs from 2015 to 2017. The number of annual visits per patient (outpatient: 3.0 vs. 4.0, P < 0.01; inpatient: 1.5 vs. 2.0, P < 0.001) and the annual medical direct costs per patient (US$2,300.1 vs. US$3,543.3, P < 0.001) of patients covered by the Urban Rural Resident Basic Medical Insurance (URRBMI) were both lower than those of patients covered by the Urban Employee Basic Medical Insurance (UEBMI), and the median out-of-pocket expense of URRBMI was higher than that of UEBMI (US$926.6 vs. US$594.0, P < 0.001). The annual direct medical costs of patients with prostate cancer in Western regions were significantly lower than those of patients in Eastern and Central regions (East: US$4011.9; Central: US$3458.6; West: US$2115.5) (P < 0.001).ConclusionsThere was an imbalanced distribution of health care utilization among regions in China. The direct medical costs of Chinese patients with prostate cancer remained stable, but the gap in health care utilization and medical costs between two different insurance schemes and among regions still needed to be further addressed.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. Public Use Tape 16 is the second public use data release from the NMES Health Insurance Plans Survey (HIPS). The purpose of the HIPS was to verify information reported by respondents to two components of the NMES, the Household Survey and the Survey of American Indians and Alaska Natives (SAIAN), about their health insurance coverage. Additional details were also obtained from the employers, unions, and insurance companies through which coverage was provided. Parts 1 and 2 of Public Use Tape 16 are files that can be used to link data to Household Survey policyholders in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: POLICYHOLDERS OF PRIVATE INSURANCE: PREMIUMS, PAYMENT SOURCES, AND TYPES AND SOURCE OF COVERAGE PUBLIC USE TAPE 15. These link files permit identification of the records in the Private Health Insurance Benefit Database (Parts 3-17 of this collection) that describe the specific benefits held by the policyholders. These files also permit linkage to the personal and socioeconomic characteristics for these policyholders found in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: HOUSEHOLD SURVEY, POPULATION CHARACTERISTICS AND PERSON-LEVEL UTILIZATION, ROUNDS 1-4 PUBLIC USE TAPE 13. Future link files will permit linkage of the Benefit Database to persons in the SAIAN and to dependents of policyholders in the Household Survey. The section files of the Benefit Database, Parts 4-13, contain information on Health Maintenance Organizations (HMOs), copayments, basic coverage, hospital and medical services, cost-containment provisions, major medical coverage, dental care, prescription drugs, vision and hearing care, and Medicare benefits. The schedule files, Parts 14-17, contain specific deductible amounts, dollar benefits, coinsurance provisions, maximum benefits, and benefit periods. Wherever possible, copies of policies or booklets describing the coverage and benefits were obtained in order to abstract this information.

Demographic characteristics of physicians and comparison subjects (n = 2852).

The Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of institutional medical billing services (SV2). It contains charges, payments, and treatments billed on a CMS-1450 form (UB-92, UB-04) by hospitals and medical facilities that treat injured employees, excluding ambulatory surgical centers, with dates of service more than five years old. For datasets from the past five years, see institutional medical billing services (SV2) header information. The header identifies insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill header information groups individual line items reported in the detail section. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our institutional medical billing services (SV2) header data dictionary. See institutional medical billing services (SV2) detail information- historical for the corresponding detail records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

This study contains the New York State Medical Professional Liability Insurance Claims Database: DOH-787, 1986-1995. Restricted access. Permission to use must be obtained from NY State Department of Health.

United States Health Insurance: Claims Per Member Per Month: Medicare data was reported at 1,111.000 USD in 2023. This records an increase from the previous number of 1,012.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicare data is updated yearly, averaging 791.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 1,111.000 USD in 2023 and a record low of 746.230 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicare 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.RG022: Health Insurance: Operations by Lines of Business.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

United States Health Insurance: Accident and Health: Net Incurred Claims data was reported at 1,094.702 USD bn in 2023. This records an increase from the previous number of 994.634 USD bn for 2022. United States Health Insurance: Accident and Health: Net Incurred Claims data is updated yearly, averaging 805.750 USD bn from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 1,094.702 USD bn in 2023 and a record low of 640.025 USD bn in 2015. United States Health Insurance: Accident and Health: Net Incurred Claims 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.RG020: Health Insurance: Accident and Health: Net Incurred Claims by Lines of Business.

Abstract

The MarketScan Medicare Supplemental Database provides detailed cost, use and outcomes data for healthcare services performed in both inpatient and outpatient settings.

It Include Medicare Supplemental records for all years, and Medicare Advantage records starting in 2020. This page also contains the MarketScan Medicare Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 250 million patients (medical, drug and dental)

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• Laboratory results

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• Hospital discharges

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The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers and Medicare.

Usage

This page contains the MarketScan Medicare Database.

We also have the following on other pages:

• The MarketScan Commercial Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **

(though the 1% sample will remain free to use). The pricing structure and other

**relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

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.

United States Health Insurance: Total Hospital & Medical Expenses data was reported at 772.127 USD bn in Sep 2024. This records an increase from the previous number of 508.201 USD bn for Jun 2024. United States Health Insurance: Total Hospital & Medical Expenses data is updated quarterly, averaging 371.947 USD bn from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 946.824 USD bn in Dec 2023 and a record low of 91.079 USD bn in Mar 2012. United States Health Insurance: Total Hospital & Medical Expenses 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.

Use and costs (US$) of healthcare services in the year 2010 by physicians and comparison subjects.

The Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of professional medical billing services (SV1). It contains charges, payments, and treatments billed on a CMS-1500 form by doctors and other health care professionals who treat injured employees, including ambulatory surgical centers, with dates of service for the last five years. For datasets going back to 2010, see professional medical billing services (SV1) detail information – historical. The detail contains information to identify insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill details are individual line items that are grouped in the header section of a single bill. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our professional medical billing services (SV1) detail data dictionary. See professional medical billing services (SV1) header information for the corresponding header records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

The Global Health Expenditure Database (GHED) provides internationally comparable data on health spending for close to 190 countries. The database is open access and supports the goal of Universal Health Coverage by helping monitor the availability of resources for health and the extent to which they are used efficiently and equitably. This, in turn, helps ensure health services are available and affordable when people need them...WHO works collaboratively with Member States and updates the database annually using available data such as government budgets and health accounts studies. Where necessary, modifications and estimates are made to ensure the comprehensiveness and consistency of the data across countries and years. GHED is the source of the health expenditure data republished by the World Bank and the WHO Global Health Observatory. (from website)

United States Health Insurance: Accident and Health: Net Incurred Claims: Medicare Supplement data was reported at 29.576 USD bn in 2023. This records an increase from the previous number of 27.703 USD bn for 2022. United States Health Insurance: Accident and Health: Net Incurred Claims: Medicare Supplement data is updated yearly, averaging 24.672 USD bn from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 29.576 USD bn in 2023 and a record low of 20.466 USD bn in 2015. United States Health Insurance: Accident and Health: Net Incurred Claims: Medicare Supplement 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.RG020: Health Insurance: Accident and Health: Net Incurred Claims by Lines of Business.