Advancing Digital Quality: Addressing Data Challenges to Improve Performance
October 14, 2024 · Guest Contributor
By Yana Ankudinova, Vice President, Product Management, MRO
As health care continues its digital transformation, interoperability and data quality are critical to measuring and improving patient outcomes. Whether focused on HEDIS®, value-based care or broader quality measurement activities, reliable data—facilitated through Fast Healthcare Interoperability Resources (FHIR®) APIs and other interoperable systems—are essential for effective reporting, decision making and delivery of care. These elements enhance our understanding of patient care, and drive the industry toward a connected and efficient future.
But what does this really mean, and how do health care organizations go beyond just “managing” data to truly optimizing it for data exchange while increasing its quality?
The Challenge: Fragmented Data and Lost Opportunities
Fragmentation is not just a data risk; it is a risk to patient care and a barrier to quality reporting. Let’s say a patient with diabetes has switched health plans twice in the last year. Every new clinician and payer they encounter has only a piece of their health history. Without the full picture, critical details—such as lab results or medication adherence—may slip through the cracks.
And each clinician that patient visits might use a different electronic health record or other health IT system to capture vital information. Patient details like vital signs and lab test results are typically captured in structured fields, while other information like social determinants of health may be entered in a text-heavy, unstructured field, making it difficult for health IT systems to receive and process the data.
For health care organizations, the stakes are high. Inaccurate or missing data or a delay in reporting can lead to penalties, lower quality scores and missed opportunities to intervene at the right time.
How Digital Quality Enhances Performance Measurement
Digital quality represents a shift to more efficient and accurate health care performance measurement by automating data exchange and reporting.
- Efficiency Through Automation. Many organizations still rely on manual data entry, which increases the risk of errors (such as incorrect code entry), slows down reporting and places administrative burden on staff. Automated quality measures reduce the need for manual data entry, improving efficiency and accuracy.
- Timely Interventions. With access to timely and accurate data, care teams can act on insights quickly. Improvements in data accuracy led to a 25% increase in adult BMI reporting and a 40% improvement in childhood immunizations, highlighting the impact of up-to-date clinical information.1
- Comprehensive Reporting. Digital quality initiatives integrate data across systems, enabling more accurate measurement of quality across diverse metrics such as preventive care and chronic disease management.
Addressing Concerns About Data Quality
Data quality issues can hinder progress toward shared goals, such as the ability to accurately measure and report on care quality.
NCQA’s Data Aggregator Validation program ensures that data collected for quality reporting are accurate, reliable and validated against primary sources, such as EHR systems or lab data. By using validated data streams, health care organizations can be confident that performance metrics reflect true care delivery, minimize errors and comply with NCQA standards.
Primary source verification goes a step further by verifying data at the entry point, ensuring that all data are accurate from the moment of capture. This reduces the risk of errors and inconsistencies so data flowing through health IT systems are reliable for quality measurement and reporting.
Practical Solutions for Health Care Organizations
Organizations must consider a holistic approach that encompasses interoperability standards and data validation.
- Adopt Standardized Data Formats. Implementing standard data formats, such as HL7’s FHIR, ensures data can be shared across systems and organizations. Both payers and practitioners need to adopt new ways of sharing and comprehending unstructured data.
- Centralize Data Aggregation. Creating a single, centralized data repository for active member data helps reduce fragmentation by consolidating information from multiple sources, and allows incorporation of unstructured data such as clinical notes.
- Collaborate Across the Ecosystem. Strong relationships and shared goals between payers, clinicians and care delivery systems can improve collaboration, enhance data-sharing capabilities and build trust.
- Integration of Health IT Systems. By investing in health IT systems that speak the same language, health care organizations can ensure that patient data are available at the point of care, no matter where the patient has been treated.
Investing in reliable, high-quality data will enhance performance reporting and financial performance across the health care ecosystem. As health care organizations adopt digital quality initiatives, addressing data challenges is crucial for success. By centralizing data, automating data validation and improving interoperability, organizations can overcome the challenges of data inconsistency and fragmentation.
This blog is brought to you by MRO and the views expressed are solely those of the sponsor.
