Navigating the Storm of Data Quality
October 9, 2025 · Guest Contributor
Health care is facing a perfect storm of data challenges. Every day about 10,000 Americans turn 65, driving unprecedented demand for Medicare risk adjustment and value-based care arrangements. Plans are requesting 20%–30% more data from their provider networks each year, while administrative costs keep rising and provider organizations struggle with staffing shortages.
The industry’s response has been predictable: Deploy more APIs, build better integrations, leverage AI. But although technology plays a crucial role, it’s only solving about one-tenth of the real problem. The fundamental issue isn’t only data integration, it’s also data quality. Health plans need more than “available” data; they also need to capture critical data elements for accurate quality measurement.
Data Quality Obstacles Health Plans Face
Information doesn’t always reside in one system in an organization. There might be multiple data silos across platforms—and even when an organization uses one EHR across locations, every clinician documents differently. Even data that come from the same EHR can contain different data elements and formatting.
In addition, performance-based contracts and quality measures evolve, changing the required data elements health plans need for reporting. Data quality issues are often identified too late. Health plans typically discover gaps during HEDIS® reporting, when there’s no time for meaningful intervention.
The Data Quality Life Cycle: Beyond Basic Integration
Effective data quality requires multiple teams, working strategically: Engineers build tools for providers and internal reporting, expert mapping teams provide knowledge on various EHRs, clinical staff guide the coding and auditing processes.
The key is to build data quality checks into multiple phases of the process so each team can flag potential data issues and send them back to providers for correction. Here are some proven strategies for a comprehensive approach to improving data quality.
- Eliminate duplicate and inappropriate entries. Vendor collaboration can help fix common issues such as duplicate records, invalid entries and structural errors; for example, if a provider’s impression was mistakenly entered in the “Lab” field, making it appear that a lab test was completed when it wasn’t.
- Perform primary source verification. Organizations select 5–25 member visits—depending on clinic size—and review every status column, date field, description and value in the medical record. This can reveal workflow issues, such as when practices enter the date they received results, rather than the actual procedure date.
- Data validation. Data validation tools can categorize questionable information into warnings or hard errors. A warning might include data that should be reviewed, while a hard error highlights incorrect data—like a blood pressure reading of 5,000.
- Create a clinical data crosswalk. Many EHRs contain valuable clinical data that are improperly coded, but could be converted into a standardized format through a simple crosswalk. For example, using a numeric value for depression screening (e.g., PHQ-9), rather than a generic assessment.
- Leverage clinical expertise. EHRs were primarily designed for billing, not for comprehensive clinical documentation. Working with clinical experts who understand both the documentation process and clinical context can strengthen data quality improvement efforts.
- Educate providers. Successful programs frame conversations around education and support, rather than approaching care delivery organizations with complaints about missing data. Providers are invested in value-based arrangements, and are generally receptive to learning effective documentation practices.
Value of Quality Data for Health Plans
As the health care industry accelerates its shift toward value-based care, the consequences of poor data quality become more severe. Star ratings, HEDIS scores and risk adjustment accuracy all depend on reliable clinical data. More importantly, accurate data enable better clinical decisions, reduce duplicate testing and help get patients into care programs more quickly.
Health plans that focus on data quality, in addition to integration, see meaningful improvements—sometimes a half-point or more in Star ratings.
Building a Sustainable Data Quality Strategy
Data quality is an ongoing journey requiring clinical expertise, provider education and continuous monitoring. Thoughtful, comprehensive data quality programs that address the full life cycle of clinical information can be a competitive advantage that separates successful health plans from plans that struggle with the same data challenges year after year.
This blog is brought to you by MRO Corp and the views expressed are solely those of the sponsor.
HEDIS® is a registered trademark of the National Committee for Quality Assurance (NCQA).
