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Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session
Course Overview:
This 1 Day course delivers a practical and structured understanding of how data science is applied within healthcare claims. It covers key components of claims data, including data preparation, transformation, fraud detection indicators, trend analysis, KPI interpretation, and basic forecasting techniques—all presented in a clear and actionable format.
The course bridges foundational claims knowledge with intermediate-level analytics, enabling you to interpret and analyze claims data with greater accuracy and confidence. Through real-world examples, logical frameworks, and guided activities, you will learn how claims data can support decision-making, reduce errors, and generate meaningful financial and operational insights for both payers and providers.
Learning Objectives
By the end of the course, you will be able to:
Understand the structure and lifecycle of healthcare claims
Prepare, clean, and validate complex claims datasets
Develop meaningful features for claims analysis
Identify fraud indicators using rule-based and pattern-driven approaches
Analyze trends and perform basic forecasting of claims cost and volume
Interpret key performance indicators (KPIs) for business decision-making
Build a simple, end-to-end healthcare claims analytics workflow
Target Audience
This course is designed for:
Healthcare analysts and reporting professionals
Claims processing and billing teams
Payer and TPA operations staff
Healthcare IT professionals
Entry-level data scientists entering the healthcare domain
Students pursuing healthcare analytics
Professionals transitioning into health data roles
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