Data Science for Healthcare Claims: 1 Day Training in Calgary

Unlock the power of healthcare data! 📊🏥 Join our Data Science for Healthcare Claims: 1 Day Training in Calgary and develop practical data science skills, gain valuable industry insights, and learn techniques to analyze healthcare claims with confidence.

About the course:
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 provides a practical, structured understanding of how data science is applied to healthcare claims. You explore real claims components, data transformations, fraud indicators, trend patterns, KPI interpretation, and forecasting essentials, all explained in a simple, actionable way. The course bridges foundational claims knowledge with intermediate-level analytics, helping you analyze claims with accuracy and clarity. Using clear logic, real examples, and guided activities, you learn how claims data can drive decisions, reduce errors, and support financial and operational insights across payers and providers.

Learning Objectives:
By the end of the course, you will be able to:

• Understand how healthcare claims are structured and processed.
• Prepare, clean, and validate multi-line claims datasets.
• Engineer meaningful features for claims analytics.
• Recognize fraud indicators using rule-based and pattern analysis.
• Interpret trends and forecast claims costs or volumes.
• Evaluate key claims KPIs to support business decisions.
• Build a simple, end-to-end claims analysis workflow.

Target Audience:
This course is ideal for:

• Healthcare analysts & reporting executives
• Claims processing & billing teams
• Payer and TPA operations staff
• Healthcare IT professionals
• Junior data scientists entering the healthcare domain
• Students pursuing healthcare analytics
• Professionals transitioning into health data roles

Why Choose This Course?
This course simplifies complex claims data science concepts into structured, accurate insights that you can apply immediately. The trainer brings deep experience in healthcare analytics, fraud detection, and claims data workflows, ensuring each topic is explained in a clear, practical manner. With a blend of foundation and intermediate-level learning, you gain the confidence to analyze claims intelligently and contribute meaningfully to payer, provider, or analytics teams.

©2026 Mangates Tech Solutions Pvt Ltd. This content is protected by copyright law. Copy or Reproduction without permission is prohibited

Looking to strengthen claims analytics across your organization?
This course can be delivered as a customized in-house program tailored to your claims volume, data formats, coding structures, and workflow challenges. We adapt the modules to your real datasets, enabling teams to work on practical examples and refine accuracy. In-house training helps build consistent analytical skills and improves coordination between claims, billing, and reporting teams.

📧 Contact us today to schedule a customized in-house session: corporate@mangates.com

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