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 |

Project Objectives Provider Fraud is one of the biggest problems facing Medicare. According to the government, the total Medicare spending increased exponentially due to frauds in Medicare claims. Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims.

Rigorous analysis of Medicare data has yielded many physicians who indulge in fraud. They adopt ways in which an ambiguous diagnosis code is used to adopt costliest procedures and drugs. Insurance companies are the most vulnerable institutions impacted due to these bad practices. Due to this reason, insurance companies increased their insurance premiums and as result healthcare is becoming costly matter day by day.

Healthcare fraud and abuse take many forms. Some of the most common types of frauds by providers are:

a) Billing for services that were not provided.

b) Duplicate submission of a claim for the same service.

c) Misrepresenting the service provided.

d) Charging for a more complex or expensive service than was actually provided.

e) Billing for a covered service when the service actually provided was not covered.

Problem Statement The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them.along with this, we will also discover important variables helpful in detecting the behaviour of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behaviour of providers.

Introduction to the Dataset For the purpose of this project, we are considering Inpatient claims, Outpatient claims and Beneficiary details of each provider. Lets s see their details :

A) Inpatient Data

This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code.

B) Outpatient Data

This data provides details about the claims filed for those patients who visit hospitals and not admitted in it.

C) Beneficiary Details Data

This data contains beneficiary KYC details like health conditions,regioregion they belong to etc.

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

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

Context

The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.

Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:

• Big Data: The explosion of unstructured data in the form of images, videos, text, emails, social media
• AI: The recent advances in Machine Learning and Deep Learning that can enable businesses to gain insight, do predictive analytics and build cost and time - efficient innovative solutions
• Real time Processing: Ability of real time information processing through various data feeds (for ex. social media, news)
• Increased Computing Power: a complex ecosystem of new analytics vendors and solutions that enable carriers to combine data sources, external insights, and advanced modeling techniques in order to glean insights that were not possible before.

This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.

Content

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.

Inspiration

This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.

Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression

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

The website shows data on the plan and implementation of the health services program by individual health activities (VZD) :

• hospital medical activity,
• general outpatient medical activity,
• specialist outpatient medical activity,
• dental practice,
• other health activities,
• activity of accommodation facilities for patient care,
• social care without accommodation for the elderly and disabled,
• production of pharmaceutical preparations,
• retail trade in specialized stores with pharmaceutical products,
• compulsory social security activity.

Within the framework of each activity, the data for each period are shown separately by contractors and together, the activity by regional units of ZZZS and the activity data at the level of Slovenia together.

Data on the plan and implementation of the health services program are shown in the accounting unit (e.g. points, quotients, weights, groups of comparable cases, non-medical care day, care, days...), which are used to calculate the work performed in the field of individual activities.

The publication of information about the plan and implementation of the program on the ZZZS website is primarily intended for the professional public. The displayed program plan for an individual contractor refers to the defined billing period. (example: The plan for the period 1-3 201X is calculated as 3/12 of the annual plan agreed in the contract).

The data on the implementation of the program represents the implementation of the program at an individual provider for insured persons who benefited from medical services from him during the accounting period. Data on the realization of the program do not refer to persons insured in accordance with the European legal order and bilateral agreements on social security. Data for individual contractors are classified by regional units based on the contractor's headquarters. The content of the data on the "number of cases" is defined in the Instruction on recording and accounting for medical services and issued materials.

The institute reserves the right to change the data, in the event of subsequently discovered irregularities after already published on the Internet.

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.

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.

Context 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.

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

Acknowledgements This is dataset is released as part of a hackathon conducted by Analytics Vidhya. Visit https://datahack.analyticsvidhya.com/contest/job-a-thon/#ProblemStatement for more information.

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.

Tracking United HealthCare Stock Performance Since IPO

Dataset Description

This dataset provides historical stock data for UnitedHealth Group (UHG), one of the largest healthcare and insurance companies in the world. It covers stock prices, market capitalization, and trading volumes from the company's IPO to the present. As a Fortune 500 company with a significant market presence, analyzing UHG's stock performance can provide valuable insights into healthcare market trends, investment opportunities, and economic indicators.

Dataset Features

• Date – The trading date for the stock data.
• Open Price – Stock price at market open.
• Close Price – Stock price at market close.
• High – Highest stock price during the trading day.
• Low – Lowest stock price during the trading day.
• Volume – The number of shares traded on that day.
• Market Cap – The total market capitalization of UnitedHealth Group.

Dataset Distribution

• Data Volume: Number of records depends on trading days from IPO to present.
• Format: CSV, Excel, or other structured data formats.
• Update Frequency: Weekly.

Usage

This dataset is useful for:

• Stock Market Analysis – Analyzing historical stock price trends.
• Financial Forecasting – Predicting future stock price movements using machine learning.
• Investment Research – Assessing UnitedHealth Group’s stock as part of a portfolio.
• Market Trends – Understanding broader trends in the healthcare insurance sector.

Coverage

• Geographic Coverage: United States (NYSE).
• Time Range: From IPO to present.
• Economic Indicators: Healthcare sector, insurance market trends.

License

CC0 (Public Domain) – This dataset is freely available for public and commercial use.

Who Can Use This Dataset?

• Investors & Traders – To analyze market trends and make informed decisions.
• Economists & Researchers – To study healthcare market impacts.
• Data Scientists – To develop predictive stock models.

The size of the Healthcare Data Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 16.20% during the forecast period. Data in healthcare signifies all the information that is created or gathered in the healthcare industry. These include patient records, electronic health records, genomic data, health insurance claims, medical images, and all other clinical trial data. All this stands at the back of modern healthcare and could support many critical applications. First and foremost, health data improves patient care. Pattern analysis for patient records is simplified by health care providers in ensuring accurate disease diagnosis and application of personalized treatment plans. Medical field images, such as X-rays and MRIs, are helpful in finding abnormalities and useful in surgical methods. Genomic data insights comprise susceptibility from a genetic view point, which therefore enables coming up with a customised treatment plan for diseases such as cancer. Then, the health information data is very crucial in conducting research and developing new medical knowledge. Researchers analyze epidemiology of diseases by adopting massive datasets, manufacture new drugs and treatments, and analyze effectiveness of health care programs by such datasets. For instance, the medical trials dataset helps in the development of evidence about the safety and efficiency of new treatment options. The health insurance claims dataset can help assess healthcare utilization patterns so as to identify areas in need of improvement. Therefore, health care data also enables administrative and operational functions of health care organizations. EHRs allow easy maintenance of the patient data, enable sound communications among healthcare providers, and minimize errors. Apart from this, analytics on health insurance claims are performed to make possible billing and reimbursement services to ensure the payment of the healthcare provider in the right amount of their rendered service. Further, analytics data could be used for optimization of resource utilization, in identifying potential cost savings, and making health care organizations efficient as a whole. Healthcare information is one of those precious assets that propel innovation, promote better patient outcomes, and support the coherent functioning of the healthcare system. Therefore, improving the quality and efficiency in which care delivery is offered can be achieved through the effective use of healthcare information by healthcare providers, researchers, and administrators for a better state of health among individuals and communities. Recent developments include: March 2022: Microsoft launched Azure Health Data Services in the United States. It is a platform as a service (PAAS) offering designed exclusively to support protected health information (PHI) in the cloud., March 2022: The government of Thailand launched a big data portal for healthcare facilities. The National Reforms Committee on Public Health recently joined hands with 12 government agencies to improve the quality of healthcare services by implementing digital technologies.. Key drivers for this market are: Increase in Demand for Analytics Solutions for Population Health Management, Rise in Need for Business Intelligence to Optimize Health Administration and Strategy; Surge in Adoption of Big Data in the Healthcare Industry. Potential restraints include: Security Concerns Related to Sensitive Patients Medical Data, High Cost of Implementation and Deployment. Notable trends are: Cloud Segment is Expected to Register a High Growth Rate Over the Forecast Period.

Context: This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration: The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information: Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset -

Name: This column represents the name of the patient associated with the healthcare record. Age: The age of the patient at the time of admission, expressed in years. Gender: Indicates the gender of the patient, either "Male" or "Female." Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. Date of Admission: The date on which the patient was admitted to the healthcare facility. Doctor: The name of the doctor responsible for the patient's care during their admission. Hospital: Identifies the healthcare facility or hospital where the patient was admitted. Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. Room Number: The room number where the patient was accommodated during their admission. Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test. Usage Scenarios: This dataset can be utilized for a wide range of purposes, including:

Developing and testing healthcare predictive models. Practicing data cleaning, transformation, and analysis techniques. Creating data visualizations to gain insights into healthcare trends. Learning and teaching data science and machine learning concepts in a healthcare context. You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive). Acknowledgments: I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations. I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Original Data Source: Healthcare Dataset

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.

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.

The health care insurance company aims to leverage the Big Data Ecosystem to address two key challenges: revenue enhancement and customer understanding. By analyzing data from various sources, including scraping competitor's company data and third-party sources, they intend to gain insights into customer behavior and conditions. This data analysis will enable them to tailor personalized offers to customers, encouraging them to purchase insurance policies. Additionally, the company plans to calculate royalties for customers who have previously bought policies. These strategies are expected to boost the company's revenue by attracting more customers and enhancing customer satisfaction through customized offers and incentives.

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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

The Medicare Home Health Agency tables provide use and payment data for home health agencies. The tables include use and expenditure data from home health Part A (Hospital Insurance) and Part B (Medical Insurance) claims. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR HHA 1. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR HHA 2. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR HHA 3. Medicare Home Health Agencies: Utilization and Program Payments for Original Medicare Beneficiaries, by Area of Residence MDCR HHA 4. Medicare Home Health Agencies: Persons with Utilization and Total Service Visits for Original Medicare Beneficiaries, Type of Agency and Type of Service Visit MDCR HHA 5. Medicare Home Health Agencies: Persons with Utilization and Total Service Visits for Original Medicare Beneficiaries, by Type of Control and Type of Service Visit MDCR HHA 6. Medicare Home Health Agencies: Persons with Utilization, Total Service Visits, and Program Payments for Original Medicare Beneficiaries, by Number of Service Visits and Number of Episodes

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events.

The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits.

Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system.

Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data.

Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department.

Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Abstract

The Colorado All Payer Claims Database (CO APCD) is a state-legislated, secure health care claims database compliant with all federal privacy laws. It contains nearly 920 million claims for approximately 65 percent of insured lives in Colorado, with information from 42 commercial health insurance plans. Health insurance payers submit data monthly and the entire database is refreshed every other month, so the CO ACPD is continually evolving and being enhanced.

Usage

The dataset was extracted by the Center for Improving Value in Health Care (CIVHC) to support Stanford University COVID Long Haul Analysis. It includes medical, pharmacy, and dental claims files with coverage dates from 01/01/2012 to 08/31/2021.

For more information of CO APCD please refer to https://www.civhc.org/get-data/whats-in-the-co-apcd/

Before Manuscript Submission

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

phsdatacore@stanford.edu 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:

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Data Documentation

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This dataset includes information related to network adequacy waiver requests filed by major medical health benefit plans. It includes data on physicians, providers, and facilities, other than facility-based physicians and providers. Related datasets are available for major medical (facility-based providers) and vision plans: • Facility-based Physicians & Providers: Network Adequacy Waiver Request - Facility based Physicians & Providers.This dataset relates to waiver requests for networks used for major medical PPO and EPO plans and includes data on facility-based physicians and providers. • Vision: Network Adequacy Waiver Request - Vision. This dataset relates to waiver requests for networks used for vision PPO and EPO plans. Insurers offering health benefits through a preferred or exclusive provider benefit plan (also called PPO and EPO plans) are required to demonstrate that the health insurance network meets Texas network adequacy standards. When a network does not meet these requirements and has a deficiency in a county for a specific physician or provider specialty type, an insurer may apply for a waiver to continue operating within its service area. The commissioner of the Texas Department of Insurance (TDI) may grant the waiver following a public hearing and consideration of relevant testimony and information. Anyone may attend the public hearing and offer testimony. Learn more about how to submit information related to a waiver request or participate in a hearing here: Network Adequacy Standards Waivers.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.