Maximizing HEDIS Performance with Smarter Data Integration

How Health Plans Can Maximize HEDIS Performance with Smarter Data Integration

Health plans don’t struggle with care delivery—they struggle with documentation.

Across the country, teams are implementing evidence-based programs, aligning with providers, and investing in outreach. But when the HEDIS scores come back, the results often don’t reflect the work. Not because care wasn’t delivered. But because it wasn’t captured in a way the system recognizes.

This isn’t a clinical problem. It’s a data problem.

Why Traditional HEDIS Strategies Fall Short

For years, health plans approached HEDIS like a reporting exercise. Pull the claims. Clean the data. Run the queries. But as quality measures evolve and CMS expectations grow more sophisticated—especially with equity stratification and encounter validation—those legacy strategies are falling apart.

Here’s the uncomfortable truth: most plans are sitting on a goldmine of clinical data they don’t use.

The problem is fragmentation. Critical insights are buried in CCDs, handwritten case notes, assessment forms, and unstructured addendums that live outside the core data ecosystem. And most plans haven’t built the infrastructure to identify, extract, and normalize that value.

What does that mean in practice?

• Care that happened—but wasn’t documented in the right system.
• Forms that were filled out—but never structured for reporting.
• Visits that qualified—but weren’t linked to the measure logic.

The result? Artificially low scores, frustrated provider networks, and missed incentives.

Common Data Blind Spots—and Hidden Value

If your HEDIS scores aren’t improving, the first place to look isn’t your programs. It’s your data pipelines. Specifically, the blind spots that sabotage reporting:

• CCDs (Continuity of Care Documents)
  Packed with encounter data, screenings, lab results, and medication lists. But unless CCDs are mapped correctly—and ingestion processes can handle variations in format—they’re often incomplete or ignored altogether.
• Case Management Notes
  These notes include care coordination details, discharge planning, and social determinant data. But if stored in isolated care management systems or vendor platforms, they’re invisible to central reporting engines.
• ONA Forms and Addendums
  Obstetric Needs Assessments (ONAs), postpartum visit summaries, and supplemental provider notes contain exactly the data needed for prenatal/postpartum and chronic condition measures. But they rarely conform to standard formats and often live in PDFs or scanned images.
• Race/Ethnicity Data
  CMS now requires stratification by race and ethnicity across multiple HEDIS measures. But if that data is collected inconsistently—free text fields, optional entries, different coding standards—it can’t be used reliably.

These aren’t edge cases. These are the difference between a 3.5 and a 4.0. Between passing a CMS audit and scrambling during validation.

Case Examples: Closing the Gap with Smarter Integration

We’ve seen this play out across multiple engagements.

Prenatal/Postpartum (PPC) Measures:
A regional health plan was underperforming despite strong provider relationships. A deep dive revealed that over 40% of qualifying prenatal visits were documented only in OB intake forms submitted as PDFs. By implementing OCR and mapping logic, the plan captured an additional 18% of eligible events—resulting in a 12-point PPC score increase within one reporting cycle.

Race/Ethnicity Stratification:
A Medicaid MCO struggled to meet new stratification mandates. They had race and ethnicity fields in their core platform, but 60% were blank. A targeted integration of intake systems and member outreach tools enabled them to pre-fill missing values and standardize entries—allowing stratified reporting across five key measures.

These results didn’t require rethinking the program. Just rethinking the pipelines.

Scalable Approaches to Future-Proofing HEDIS Data Pipelines

Fixing data blind spots isn’t a one-time cleanup. It’s an operational shift.

The goal is not just better reports. It’s a more responsive, future-proof architecture that grows with your organization’s quality goals. Here’s what that looks like:

1. Map Before You Move

Before building anything, conduct a full inventory of your data assets. Where do clinical documents live? What systems collect assessments? Where are case notes stored? Identify everything that could impact HEDIS performance—and track whether it’s currently integrated.

2. Structure at the Source

Standardize data entry forms, enforce field validation, and limit free text. Make it easier for intake teams and providers to input data in ways that support downstream use—starting with the highest-volume measures.

3. Modular ETL Pipelines

Don’t build one giant pipeline. Build modular components that can be added or swapped as new forms, formats, or systems come online. This increases agility and minimizes rework when CMS adds new requirements.

4. Metadata and Lineage

Track every transformation. Know where each data point came from, how it was mapped, and when it was updated. This is essential for audit prep and root cause analysis.

5. Measure the Gaps

Don’t just report on measure performance. Report on documentation gaps, ingestion failures, and unmapped sources. Visibility into failure points is the fastest way to improve scores.

Building the Bridge: The Role of ETL, Snowflake, and Informatica

Technology doesn’t solve the problem—but it does enable the solution.

• ETL Frameworks: Whether built in-house or powered by tools like Informatica, robust ETL is essential for translating non-standard data into measure-ready fields. It needs to handle complex mappings, variable formats, and edge cases—without constant rework.
• Snowflake: As a data platform, Snowflake’s ability to process structured and semi-structured data at scale makes it ideal for integrating clinical documents, CCDs, and JSON-formatted assessments. With proper modeling, Snowflake can serve as both staging and reporting engine for HEDIS.
• Informatica: Plays a key role in data quality, MDM, and governance. Especially useful for validating race/ethnicity fields, de-duplicating members, and ensuring data consistency across source systems.

Together, these tools create the infrastructure that transforms care delivery into measurable performance.

Conclusion: You Can’t Improve What You Can’t See

HEDIS isn’t just about care—it’s about proof.

If your plan is doing the work but not seeing the scores, chances are the data’s the problem. But with the right integration strategy, you can turn blind spots into breakthroughs.

Smarter pipelines lead to clearer reporting. Clearer reporting leads to higher scores. And higher scores lead to real, measurable gains—for your members, your providers, and your bottom line.

You Delivered the Care. Now Deliver the Score.

If your quality programs are stuck, the issue isn’t effort—it’s access. Most plans already have the data they need to improve performance. It’s just not structured, integrated, or accessible in time.

That’s where we come in.

At Data Ideology, we help health plans uncover hidden data, integrate high-value sources, and build the pipelines that turn care delivery into quality performance.

Ready to close the gap? Schedule a strategy session to learn how we can help you unlock measurable HEDIS gains.