Benjamin Franklin famously wrote, “In this world nothing can be said to be certain, except death and taxes.” He died in 1790, long before electronic medical records (EMRs) existed. Had he worked with today’s clinical data, he might have added a third certainty: data quality issues. For health plans, those issues can feel inevitable. Clinical information originates across disparate systems, provider workflows vary, and the data needed for quality measurement may not arrive complete, standardized or ready to use. 

Health plans cannot control every source of variation. But they can control how clinical data is validated, enriched and put to work. 

The opportunity is to move beyond treating data quality as a year-end exercise and instead build quality into the clinical data lifecycle. 

Clinical Data Quality Matters More Than Data Volume 

Access to clinical data is only the beginning. 

EHR data may include clinical terms and descriptions without corresponding standardized codes. There may also be missing codes, missing code categories or custom codes that are not accepted for measure calculation. MRO addresses these challenges through code crosswalks aligned with HEDIS® measure criteria. 

For health plans looking to use clinical information for HEDIS, risk adjustment, care coordination and other initiatives, the distinction matters. The goal is not simply more data. It is usable, reliable data that can support the intended purpose. 

Continuous Data Quality Monitoring for Health Plans 

Data quality is a journey, not a destination—and a one-time checkpoint cannot tell health plans whether they are staying on course. That journey needs mile markers: clear benchmarks for completeness, accuracy and usability that make progress visible over time. It also needs speed limits: agreed-upon quality thresholds that keep the push for faster data delivery from outpacing validation. Regular checkpoints help teams assess progress, identify detours and course correct before small issues become larger obstacles. The goal is not simply to reach a year-end submission deadline, but to keep improving the reliability of clinical data throughout its lifecycle. 

MRO’s approach incorporates an ongoing cycle of assessing data quality, correcting structural errors, identifying missing information, quarantining outliers, removing duplicate or inappropriate entries and validating cleansed data. Provider education, onboarding validation, monitoring and mapping optimization support that process. 

Discovering missing or unusable information late in the submission cycle creates an avoidable challenge for health plans. Continuous monitoring creates opportunities to identify issues earlier, when there is more time to address them. Strong data governance and a true payer-vendor partnership are key to staying on top of data disruptors. When subject matter experts from both organizations work together across the data spectrum, issues are spotted sooner and resolved faster.  

It also helps shift the conversation from “How do we clean up the data we received?” to “How do we improve the quality of the data throughout its journey?” 

EHR Data Validation: Fixing Clinical Data Quality at the Source 

Connectivity alone is not enough. 

Data can originate in hundreds of different EHR and practice-management environments, each with its own configurations and workflows. MRO’s approach combines data validation with vendor collaboration, provider education, and primary source verification to identify discrepancies and mapping opportunities. 

This is an important distinction for payers. Simply connecting to a source does not guarantee that everything flowing through that connection is fit for measurement. If providers are not aware of the discrete fields required by their vendor needed to be included in the EHR outbound file option, you miss that valuable historical screening scanned report sitting in a document file. Clinical data needs to be evaluated in the context of how it was captured, mapped, and ultimately intended to be used. If the recipients of an EHR file option do not validate and investigate outliers or discrepancies, payers will miss measure capture and true member clinical information.  

Turn HEDIS Supplemental Data into a Continuous Resource 

That same philosophy can change how health plans approach HEDIS supplemental data. 

Rather than relying exclusively on retrospective retrieval, MRO’s model enables participating sites to be monitored for new clinical information, with data streamed timely and in standardized formats. That clinical information can supplement claims and provide a more complete view of member care. 

More continuous access to clinical data can support earlier identification of care and risk gaps, timely interventions, and proactive member outreach. It can also reduce reliance on manual chart retrieval and abstraction by providing HEDIS-engine-ready files. The providers and their practice staff can focus on member care and outreach instead of filling in supplemental data spreadsheets.  

The question for health plans becomes less about retrieving everything at the end of the year and more about establishing a dependable clinical data flow throughout the year. 

Closing Care and Risk Gaps at the Point of Care 

The value of clinical data increases when information can move in both directions. 

Providers may not have access to current quality and risk-adjustment information when they are caring for a patient. MRO’s Clinical Gap Connector is designed to surface care and risk gaps within the provider workflow when the patient’s chart is opened, while supporting the exchange of updated clinical information back into the payer ecosystem. 

That helps move gap management closer to the patient’s encounter rather than relying solely on information that arrives later through traditional claims processes. 

It also supports a more connected payer-provider model: health plans make gap information available, providers can address opportunities within their workflows, and updated clinical information can flow back to support health plan initiatives. 

From More Clinical Data to Smarter, More Usable Data 

Health plans do not need another pile of clinical data. They need information they can trust and put to work. 

That requires more than connectivity. It requires validation, thoughtful mapping, provider collaboration, and an infrastructure designed to move reliable clinical information where it can create value. 

MRO works across the healthcare ecosystem to securely exchange clinical data and unlock its value. Its experience spans more than 2,000 hospitals and health systems, 170,000 providers, 35,000 clinics and 200 health plans. 

Data quality issues may never disappear completely. But health plans can take greater control over when issues are identified, how they are addressed and how quickly trustworthy clinical data can be transformed into action. 

Ready to Put Smarter Clinical Data to Work? 

Learn how MRO helps health plans access, validate and activate clinical information to support quality, risk and care gap initiatives. Contact us today.