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Healthcare Provider Fraud Detection Dataset
Public Health & Epidemiology
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About
Focused on detecting fraud within Medicare, this dataset addresses a significant challenge that results in substantial financial losses across the healthcare system. By analyzing claim data, the project seeks to identify potentially fraudulent healthcare providers and uncover patterns and variables commonly associated with fraudulent behavior.
Project Objectives:
Medicare fraud is a critical issue impacting both the healthcare system and insurance premiums. This dataset is designed to:
- Identify patterns of fraudulent behavior among healthcare providers.
- Highlight common types of fraud, including:
- Billing for services not provided.
- Duplicate claim submissions.
- Misrepresentation of services.
- Overcharging for services.
- Billing for services not covered by insurance.
Problem Statement:
The dataset is intended for developing predictive models that identify potentially fraudulent providers based on claim data. These insights could significantly enhance fraud detection measures in healthcare.
Dataset Features:
- Inpatient Claims Data: Information on claims filed for hospitalized patients, including admission/discharge dates and diagnosis codes.
- Outpatient Claims Data: Details of claims for patients treated without hospital admission.
- Beneficiary Details Data: Information about beneficiaries, including demographic data and health conditions.
Usage:
This dataset is ideal for:
- Fraud detection analysis to improve Medicare's ability to detect and prevent fraud.
- Pattern recognition to identify and analyze common fraud tactics.
- Machine learning models aimed at predicting fraudulent behaviors based on claim and provider data.
Coverage:
The dataset covers various aspects of Medicare claims, including inpatient and outpatient records and beneficiary details, to enable a comprehensive approach to fraud detection.
License:
CC0 (Public Domain)
Who can use it:
This dataset is beneficial for data scientists, healthcare providers, insurance fraud investigators, and policy analysts aiming to reduce fraudulent claims and improve fraud detection methods in healthcare.
How to use it:
- Develop predictive models to identify fraud-prone providers based on submitted claims.
- Perform exploratory data analysis to uncover patterns of fraud in Medicare claims.
- Contribute to fraud detection strategies by analyzing claim submission behaviors for anomalies.
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