Participants learn the structure of the U.S. system, key stakeholders, and why coverage and cost-sharing work the way they do—building the foundation needed to interpret claims reporting correctly.

Learning objectives include:

• Describe the roles of employers, payers, providers, regulators, and employees

• Compare insurance types and plan designs, including cost-sharing and access pathways

• Explain how premiums, deductibles, and enrollment tiers affect employers and employees

• Identify HR’s strategic and compliance responsibilities in benefits management

• Recognize how analytics supports decision-making, compliance, and engagement

A step-by-step view of what happens between care delivery and what shows up in reporting—so teams know what they’re looking at, what can lag, and where errors and denials occur.

Learning objectives include:

• Describe the lifecycle of a claim from submission to payment/denial

• Identify key stakeholders and their roles in adjudication and payment

• Explain how plan rules (network, benefits, cost share) impact payment decisions

• Interpret EOBs and distinguish denied vs rejected claims

• Understand how claims data is used to evaluate programs and cost trends across benefit types

What gets coded becomes what gets analyzed. This module explains core code sets and how coding accuracy affects payment and analytics.

Learning objectives include:

• Explain the purpose of coding in reimbursement and reporting

• Differentiate key code types (ICD, CPT, HCPCS, DRG, modifiers) and other domains (CDT, NDC, vision codes)

• Recognize how coding accuracy impacts payment and downstream analytics

• Identify common coding-related issues that lead to denied claims or inaccurate reports

How raw claims become reporting categories and metrics (episodes, risk scores, PMPM)—and how to spot limitations in vendor logic.

Learning objectives include:

• Explain how groupers convert raw claims into usable reporting categories

• Identify key grouper types used in employer reporting (DRGs, HCCs, episodes)

• Use metrics like PMPM and risk scores to evaluate trends and performance

• Spot limitations in vendor logic—and ask the right questions

How “who/what/where/when” transforms totals into insight—service categories, demographics, provider views, and time.

Learning objectives include:

• Understand what dimensions are and how they add context to claims data

• Recognize common healthcare dimensions (time, service categories, roll-ups, demographics, provider)

• Learn how dimensions reveal patterns and drivers that raw numbers can’t show

• See how applying dimensions turns claims into actionable insights

How to interpret (and not misinterpret) utilization, counts, financial measures, ratios, durations—and how measures + dimensions tell the full story.

Learning objectives include:

• Understand types of measures in claims data (counts, utilization, financial, ratios, durations)

• Learn to calculate and interpret measures, including common pitfalls

• Recognize how measures interact with dimensions

• Apply measures to real-world questions about cost, quality, and utilization

What a “warehouse” actually is (and isn’t), what good looks like, and how environment choices affect what you can confidently report.

Learning objectives include:

• Understand the role and purpose of a data warehouse in benefits analytics

• Identify features that make a warehouse usable for HR and analyst teams

• Describe trade-offs between internal builds, outsourced platforms, and hybrid models

• Recognize common data gaps and pitfalls that limit insight generation

• Evaluate your current environment and identify questions to guide improvements

How to translate vague questions into defensible analysis: definitions, measures/dimensions, timeframes, validation, and communication.

Learning objectives include:

• Translate stakeholder questions into clear analytic problems

• Design an approach by selecting dimensions, measures, timeframes, and financial definitionsModule 6 _Health Care Analytics…

• Evaluate data availability/limitations and identify when questions can’t be fully answered

• Execute and validate analyses for reasonability and consistency

• Communicate findings effectively, including limitations and follow-up questions