Healthcare Analytics Foundations℠

Training for professionals responsible for interpreting, evaluating, and communicating healthcare analytics — without formal training in how healthcare analytics actually works.

Understand how healthcare data is created.
Interpret reports with confidence.
Ask better questions.

The Challenge

Professionals across the healthcare ecosystem are increasingly expected to interpret claims reports, evaluate performance, and explain cost drivers to leadership or clients — often using reports they did not design and logic they do not control.

Yet most were never formally trained in how healthcare data is created, structured, and reported. Without that foundation, it becomes difficult to validate results, challenge assumptions, or shape reporting that truly answers leadership questions. Teams may confidently present findings they cannot technically defend.

Common outcomes include:

  • Vendor reports that don’t address the right questions

  • Limited ability to validate methods or reporting logic

  • Increased risk of misinterpretation in high-stakes decisions

Demonstrated Outcomes

When organizations address this capability gap directly, the impact is measurable — in vendor conversations, reporting confidence, and leadership decision-making.

Challenge

A large, self-insured employer was investing heavily in healthcare benefits but lacked a shared analytic foundation across its HR and benefits team. Vendor reports were reviewed regularly, yet cost drivers, utilization shifts, and ROI claims were not consistently understood or challenged. Leadership discussions relied heavily on external interpretation.

Solution

Healthcare Analytics Foundations was implemented as a structured internal capability program for internal stakeholders. The curriculum built a practical understanding of how claims data is generated, grouped, and measured — equipping the team to interpret reporting logic rather than simply consume dashboards.

Results

Within months, vendor conversations became more structured and data-driven. Teams demonstrated improved rigor in interpreting PMPM trends, risk scores, and performance metrics. Questions shifted from “What does this mean?” to “Is this the right analytic approach?”

Business Impact

The organization strengthened internal oversight of healthcare reporting and reduced reliance on external narrative framing. Leadership decisions were grounded in clearer understanding of assumptions, limitations, and measurable impact — building durable internal analytic capability.

“As we build our internal data warehouse and expand our analytic capabilities, Healthcare Analytics Foundations℠ provided exactly the structure our team needed. It established a shared understanding of how claims data is generated and reported — critical as we scale our internal reporting function. We see this as foundational not only for our core team, but for HR professionals across the organization who rely on this information.”
— Director of Benefits, Large Self-Insured Employer

What This Program Delivers

Healthcare Analytics Foundations℠ is a structured, self-paced program designed to build analytic literacy — not technical coding skills, but practical understanding. It equips professionals to interpret and evaluate healthcare reporting with confidence.

A Practical Understanding of Claims Data

How healthcare data is generated, adjudicated, coded, and structured before it appears in reporting.

Clarity on Reporting Logic

How groupers, financial definitions, and measurement frameworks shape the conclusions drawn from reports.

Confidence In Interpreting Measures and Trends

How to evaluate PMPM, utilization, risk scores, and performance metrics without overreacting to noise.

A Framework for Asking Better Questions

How to translate stakeholder concerns into defensible analytic approaches — before decisions are made.

Curriculum Overview

The curriculum progresses from foundational healthcare system concepts through applied analytics and reporting evaluation — building the knowledge required to interpret healthcare reporting with confidence.

  • 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

Healthcare Analytics Foundations℠ is designed for professionals accountable for healthcare performance, reporting, and decision-making — yet never formally trained in how claims data is constructed.

Ideal participants include:

HR & Benefits Leaders
Responsible for evaluating cost trends, utilization patterns, and program performance — and communicating findings to leadership.

Finance & Total Rewards Professionals
Interpreting healthcare spend, ROI analyses, and financial performance metrics that inform benefits strategy.

Vendor Oversight & Governance Roles
Overseeing external reporting partners and accountable for validating analytic methods and conclusions.

Internal Analytics Teams Expanding Healthcare Capability
Building or refining healthcare reporting functions and seeking stronger domain grounding in claims data logic.

Consultants Supporting Employer Strategy
Advising employer clients and translating reporting outputs into defensible, actionable recommendations.

Designed For

Frequently Asked Questions

Answers to common questions about format, audience, implementation, and what to expect from the program.

  • No—this program focuses on analytic literacy: how healthcare data is generated and how reporting logic is constructed, so teams can interpret outputs and ask better questions regardless of tools.

  • Getting started is simple. Reach out through our contact form —we’ll walk you through the next steps and answer any questions along the way.

  • Yes. Training is delivered through self-paced, video-based modules with knowledge checks throughout.

  • Each module includes embedded knowledge checks and a required 20-question module quiz (per your program design). Certificates of completion are available upon successful completion of module assessments.

  • Anyone who interprets claims reports, evaluates performance, explains trends, or manages vendor reporting—across employer, health plan, TPA, and consulting settings.

  • The program is self-paced and designed for working professionals. Most participants complete all modules within several weeks, depending on scheduling and organizational rollout.

  • Yes. Organizations often implement the program across HR, benefits, finance, and analytics teams to establish a shared analytic foundation. The structure supports scalable, distributed learning.

  • Absolutely. The program is particularly valuable for organizations investing in internal reporting capability. It ensures stakeholders understand how claims data is structured and how reporting logic influences conclusions.

  • Core curriculum is standardized to ensure consistency. Implementation discussions can address sequencing, pacing, and internal alignment needs.

  • No. The program is designed for professionals who work with healthcare reporting but do not have formal analytic or technical training.

  • This program focuses specifically on healthcare claims data — including coding, adjudication, groupers, measures, and reporting governance — rather than generic analytics tools or statistics.