Data Science for Healthcare Claims – 1 Day Workshop in Calgary
About this Event
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Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, beverages, and light snacks included
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 this 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
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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.
📧 Contact us today to schedule a customized in-house, face-to-face session: [email protected]
Agenda
Module 1: Understanding Healthcare Claims Data
Info: • Structure of claims: header, line items, coding fields
• Diagnosis–procedure relationships
• Claims life cycle: submission to adjudication
• Icebreaker
Module 2: Claims Data Preparation & Cleaning
Info: • 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
Info: • Creating utilization, cost, and risk features
• Denial-related feature extraction
• Identifying high-impact cost drivers
• Brainstorm Activity
Module 4: Fraud, Waste & Abuse Indicators
Info: • 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
Info: • Monthly/quarterly pattern interpretation
• Volume & cost forecasting techniques (moving averages)
• Identifying seasonal shifts and utilization surges
• Activity
Module 6: Claims KPIs & Performance Insights
Info: • 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
Info: • Building a simple claims analysis blueprint
• Steps for reviewing claims issues
• Linking findings to decision-making
• Action Plan Review
Where is it happening?
Event Location & Nearby Stays:
CAD 497.06 to CAD 600.56
