Payers have access to more clinical data than ever—but more data doesn’t automatically mean better quality measurement for HEDIS. These large volumes of data come from a variety of different EHRs, practice management systems, laboratories, and other sources, each with its own codes and documentation practices. Without the right processes in place, valuable information may be missed, misinterpreted, or filtered out before it can contribute to HEDIS reporting. Those gaps can lead to missed care opportunities, incomplete member records, and weaker HEDIS scores.
To get a true picture of quality across their member populations, health plans must ensure their HEDIS data is complete, accurate, standardized, and validated. And as HEDIS reporting moves closer to becoming fully digital, the quality of the data behind each measure will matter more than ever. Building confidence in that data starts with understanding where quality can break down and how to address those issues before they affect measurement.
What Does “Good Data” Look Like?
Several characteristics contribute to high-quality HEDIS data:
- Accurate: Data reflects what actually occurred during a member’s care. A clinical note mentioning a colonoscopy, for example, doesn’t necessarily establish that the procedure was performed. Reviewing the clinical context helps distinguish completed care from procedures that were only ordered, discussed, or referenced.
- Complete: Data contains the critical elements required to support measurement. Identifying missing elements early gives practices an opportunity to address documentation gaps before the measurement window closes.
- Standardized: Data has been consistently interpreted across different sources. Providers may use different codes, fields, and formats, while laboratories may use custom codes rather than standards such as LOINC.
- Validated: Data has been evaluated against quality rules that identify nonstandard codes, unexpected formats, implausible values, and other potential problems.
Together, these attributes give payers confidence in the data they use for HEDIS. To consistently deliver high-quality HEDIS data, payers and any data partners they work with should take a proactive approach to ensuring data quality. The processes below help payers identify issues early, address them at the source, and maintain data quality over time.
Start With Data Quality at the Source
Clinical data quality problems often originate upstream. A vendor may transmit a nonstandard code, a practice may miss a critical element, or a laboratory may use a custom code that doesn’t translate directly to a HEDIS standard.
Finding these issues requires having strong relationships with the vendors and practices contributing data—and an understanding of how their systems work. This enables teams to identify problems at the source and work with vendors and practices to resolve them, which can significantly improve the quality of data entering the payer.
For payers that don’t have the time or resources to manage these issues across various vendors and practices, a data management partner can take on that work. For example, MRO has established relationships with a large variety of healthcare organizations and works closely with their vendors to identify and correct data errors where they originate.
Validate Before the Data Starts Flowing
Validating data during provider onboarding helps identify and address issues before they affect HEDIS measurement. Primary source verification (PSV) compares sampled medical records and visits with data extracted from the source EHR, which can uncover discrepancies, missing information, and opportunities to map additional clinical elements.
Critical data element validation identifies the data that practices need to document for measurement and confirms that it’s captured in the appropriate fields and formats. Addressing these gaps during onboarding gives practices time to adjust workflows and ensure critical data elements are captured throughout the measurement year, rather than chasing missing data later.
Keep Monitoring After Onboarding
Ongoing monitoring helps catch issues that emerge over time. Providers change workflows, vendors update systems, and new data can introduce problems that weren’t present initially.
Data validation tools can identify:
- Nonstandard or invalid codes
- Values outside expected clinical ranges
- Unexpected data formats
- Missing critical elements
- Other issues that could affect measurement
Monitoring flagged data also reveals patterns that warrant investigation. For example, an unusually high volume of nonstandard laboratory codes from one practice could indicate a mapping issue or a code input mistake, such as a double-hyphened LOINC pattern. Teams can use these findings to investigate the source, work with a practice or vendor, or make mapping adjustments.
Normalize Data While Preserving Clinical Accuracy
Payers aggregate clinical data from hundreds of EHRs and practice management systems, each with its own structures and conventions. Mapping helps covert that data into consistent, standardized formats for measurement.
Code crosswalks can translate custom lab codes or null codes into recognized standards when the underlying clinical data supports the selected code. Reliable mapping rules also ensure that data is interpreted consistently across different sources.
At this scale, payers benefit from mapping expertise that spans the systems contributing their data. That knowledge helps identify where relevant data resides and how it should be represented for consistent downstream use.
Preparing for the Future of Digital HEDIS
The importance of these processes will only grow in the coming years. As HEDIS relies more heavily on digital measures, payers will need to ensure they have high-quality data across their member populations.
For payers that rely on external data partners, data quality capabilities are a critical consideration when evaluating those partnerships. Look for partners that validate data before it reaches the payer, monitor it continuously, understand the nuances of hundreds of source systems, and maintain relationships with providers and vendors to address issues when they arise.
NCQA Data Aggregator Validation (DAV) designation is another indicator of a data partner’s commitment to data quality. DAV evaluates the quality and integrity of clinical data from ingestion through transmission, including the processes used to safeguard processes and manage source issues. Working with a DAV-validated partner gives health plans added confidence in the clinical data they use for HEDIS reporting and can reduce resources needed for primary source verification during the HEDIS audit process.
When health plans can trust the data behind their HEDIS measures, they’ll be better equipped to enhance member outcomes, boost HEDIS and star ratings, and turn their data into action.
MRO helps payers access, validate, and standardize clinical data across their provider networks to support HEDIS measurement and reporting. Contact us to learn more.
Why does HEDIS data quality matter now?
As HEDIS reporting makes a shift toward fully digital measures, payers can no longer rely on manual review to catch gaps or errors. The underlying clinical data itself needs to be accurate, complete, and standardized from the start, since digital measurement leaves less room to catch and correct issues after the fact.
What makes HEDIS data “good” or high-quality?
High-quality HEDIS data has four key attributes: it’s accurate (reflecting what actually happened in a member’s care, not just what was documented or ordered), complete (containing all elements needed for measurement), standardized (consistently coded across different source systems), and validated (checked against quality rules that catch errors or implausible values).
Where do HEDIS data quality problems typically originate?
Data quality issues often start upstream, before information reaches the payer. A vendor might submit a nonstandard code, a practice might miss documenting a required element, or a lab might use a custom code that doesn’t map cleanly to HEDIS standards. Addressing issues at the source is more effective than trying to fix them later.
What is primary source verification (PSV) and why does it matter for HEDIS?
PSV compares a sample of medical records and visit data against what’s extracted from the source EHR. It’s typically done during provider onboarding and helps surface discrepancies, missing information, or mapping opportunities before they can affect a full measurement year.
How does ongoing data monitoring help after onboarding?
Provider workflows and vendor systems change over time, which can introduce new data issues even after onboarding is complete. Continuous monitoring helps catch problems like nonstandard codes, out-of-range values, or missing elements as they emerge, rather than discovering them when it’s too late to correct course.
