Healthcare Claims Data Science – 1 Day Training in New York City, NY
Description
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 one-day training provides a practical and structured introduction to how data science is applied within healthcare claims. Participants will explore key claims components, data preparation techniques, fraud detection indicators, trend analysis, KPI interpretation, and forecasting fundamentals.
The course connects foundational knowledge of healthcare claims with intermediate-level analytics, enabling participants to understand and analyze claims data with greater accuracy and confidence. Through clear explanations, real-world examples, and guided activities, you will learn how claims data can support better decision-making, reduce errors, and deliver valuable financial and operational insights for payers and providers.
Learning Objectives
By the end of this course, you will be able to:
Understand the structure and lifecycle of healthcare claims
Prepare, clean, and validate multi-line claims datasets
Engineer meaningful features for claims data analysis
Identify potential fraud indicators using rule-based and pattern analysis
Analyze trends and perform basic forecasting of claims costs or volumes
Interpret key claims KPIs to support operational and strategic decisions
Develop a simple end-to-end workflow for claims data analysis
Target Audience
This course is ideal for:
Healthcare analysts and reporting professionals
Claims processing and medical billing teams
Payer and TPA operations staff
Healthcare IT professionals
Junior data scientists entering the healthcare industry
Students pursuing healthcare analytics
Professionals transitioning into health data and analytics roles
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Why Choose This Course?
This program simplifies complex healthcare claims analytics concepts into clear and practical insights that can be applied immediately in the workplace. The training is led by an experienced professional in healthcare analytics, fraud detection, and claims data workflows, ensuring that each topic is explained in a structured and practical way.
By combining foundational knowledge with intermediate-level analytics concepts, the course helps participants build confidence in analyzing claims data and contributing effectively to payer, provider, or healthcare analytics teams.
📧 Contact us today to schedule a customized in-house session: corporate@mangates.com
Description
Agenda
Module 1: Understanding Healthcare Claims Data
• Structure of claims: header, line items, coding fields • Diagnosis–procedure relationships • Claims life cycle: submission to adjudication • Icebreaker
Module 2: Claims Data Preparation & Cleaning
• Standardizing coding fields (ICD, CPT, NPI, POS) • Identifying anomalies, invalid values, and missingness • Cleaning & validating multi-line claims • Case Study
Module 3: Feature Engineering for Claims Analytics
• Creating utilization, cost, and risk features • Denial-related feature extraction • Identifying high-impact cost drivers • Brainstorm Activity
Module 4: Fraud, Waste & Abuse Indicators
• Detecting suspicious patterns like upcoding or billing spikes • Understanding rule-based and statistical flags • Using simple thresholds for anomaly detection • Simulation
Module 5: Trend Analysis & Forecasting
• Monthly/quarterly pattern interpretation • Volume & cost forecasting techniques (moving averages) • Identifying seasonal shifts and utilization surges • Activity
Module 6: Claims KPIs & Performance Insights
• Key KPIs: denial rate, allowed cost, paid-to-billed ratio • Identifying trends driving claim variations • Connecting KPIs with operational decisions • Role Play
Module 7: Putting It All Together: Claims Analysis Workflow
• Building a simple claims analysis blueprint • Steps for reviewing claims issues • Linking findings to decision-making • Action Plan Review
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