Healthcare 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. Health plans are requesting 20-30% more data year over year from their provider networks, while administrative costs continue climbing and provider organizations struggle with staffing shortages.
The industry’s response has been predictable: deploy more APIs, build better integrations and leverage artificial intelligence. But here’s what industry experts are discovering: while technology plays a crucial role, it’s only solving about one-tenth of the real problem.
The fundamental issue isn’t solely about data integration; it’s also about data quality. Health plans continuously report that it’s not enough to have data readily available, but they have a need to capture all the critical data elements required for accurate quality measurement.
The Data Quality Obstacles Health Plans Face
Within provider organizations, information doesn’t always reside in just one system. Multiple silos of data exist across different platforms, and even when an organization uses one EHR across multiple locations, every individual provider documents differently.
This creates significant challenges for health plans as the standardized data feed from two different providers might contain completely different data elements and formatting, even if they are both using the same EHR. Data sets required for health plans also differ with performance-based contracts and quality measures that evolve over time.
One of the most concerning challenges is that data issues are often identified too late. Health plans typically discover gaps during HEDIS season, when there’s no time for meaningful intervention.
The Data Quality Lifecycle: Beyond Basic Integration
Effective data quality requires multiple teams to work together strategically. For example, engineers build tools for providers and internal reporting, expert mapping teams provide knowledge on various EHRs (some organizations work with over 50 different combinations), and clinical staff guide the coding and auditing processes.
The key is having data quality checks at multiple phases since you might catch several issues through one form of validation, then catch others through auditing. This multi-pronged approach allows plans to continuously monitor data and capture issues to take back to providers for correction. Possible approaches include:
Eliminate Duplicates and Inappropriate Entries
Vendor collaboration can help fix common issues such as duplicate records, invalid entries, and structural errors. For example, labs that appeared complete but were never performed. This can happen when a provider’s impression is mistakenly input into the lab field. Another example is when codes are mis-programmed or valuable data is incorrectly coded.
Primary Source Verification
To perform primary source verification, organizations pick 5-25 member visits, depending on the clinic’s size, and work through medical records. They examine every status column, date field, description, and value. Sometimes, this can reveal workflow issues, such as when practices enter the date they received results rather than the actual procedure date.
Data Validation
When validating data, tools can categorize questionable information into warnings or hard errors. Warning-level data might be suspicious but legitimate, while hard-level data, like blood pressure readings of 5,000, clearly shouldn’t pass through.
Clinical Data Crosswalk
Many EHRs contain valuable, discrete clinical data that isn’t properly coded but could be converted into a standardized format. Through a simple crosswalk of that data, improvements can be made. For example, depression screenings could have a numeric value associated, like the PHQ-9, rather than simply using a generic assessment.
How Data Validation Impacts Health Plan Performance
A distinguishing factor of effective data quality programs is clinical oversight from professionals who understand both the documentation process and clinical context. EHRs were designed primarily for billing, not comprehensive clinical documentation, and therefore require the perspective of someone who has actually used the EHR in practice.
The expertise of someone who understands the clinical context is invaluable when educating providers. Rather than approaching practices with complaints about missing data, successful programs frame conversations around education and support.
Technology + Clinical Expertise = Reliable Data Quality
While automation technology provides the necessary scale for handling large data volumes, clinical expertise ensures those automated processes incorporate appropriate safeguards. Taking it a step further, provider education creates sustainable documentation improvements.
Health plans that continue relying solely on data feeds and integration improvements will find themselves at a disadvantage compared to those investing in comprehensive data quality programs.
The Value of Quality Data for Health Plans
As the healthcare 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 enables better clinical decisions, reduces duplicate testing, and helps get patients into appropriate care programs more quickly.
Health plans seeing the most meaningful improvements (sometimes half a point or more in Star ratings year over year) are those implementing systematic data quality lifecycles rather than hoping that better integration will solve their problems.
Building a Sustainable Data Quality Strategy
Quality data isn’t a destination reached through better technology alone, but an ongoing journey requiring clinical expertise, provider education, and continuous monitoring. Providers are invested in value-based arrangements and generally receptive to education about effective documentation practices.
Thoughtful and comprehensive data quality programs that address the full lifecycle of clinical information are the competitive advantage that separates successful health plans from those still struggling with the same data challenges year after year.
Ready to transform your data quality approach?
Contact MRO to see how we advance data quality, help organizations overcome data challenges, and achieve seamless interoperability. Our comprehensive solutions deliver high-quality, accurate information when it’s needed most, streamline reporting for consistent and comprehensive insights, and empower smarter, faster decisions with reliable data.
Amy Hartje BSN, RN, Director of Client Account Management and Clinical Quality at MRO, contributed to the above blog post.
