Data quality standards are key for HIEs to achieve long-term success
Better data can improve outcomes, but exchange organizations need to adopt standardization and data quality measures to improve the system’s capabilities.
Digital health information is flooding the healthcare system, as patients, providers and facilities expand ways to treat medical conditions and promote wellness.
With this increase in data volume across healthcare technology systems, healthcare leaders must assess whether they’re placing adequate emphasis on the quality and standardization of patient data.
Standardizing healthcare data for more efficient data exchange is an ongoing initiative at local, state and national levels. To meet emerging healthcare data standards, such as the Fast Healthcare Interoperability Resources (FHIR) standard and the United States Core Data for Interoperability (USCDI), an organization’s data foundation must be comprehensive and reliable before it can make a clinical, financial and equitable care impact for healthier communities.
Data quality must be top-of-mind for healthcare organizations investing in programs that reward higher-quality, cost-effective care. However, the pandemic shed light on inequities in healthcare delivery and the alarming data access disconnect hindering disease response times.
Three HIE strategies for success
Providing value to healthcare data quality discussion is critical for health information exchanges (HIEs). What steps can HIEs take to become a primary driver to enable population health with high-value data? Adopting three organizational HIE strategies that address the emerging data use cases, data quality and strategic partnerships will help communities and HIEs thrive beyond 2023.
Strategy 1: Adopt data quality standards for high-value care
Healthcare continues to see an exponential increase in the need for data quality standards. For example, NCQA supports a CMS initiative to transition all quality measures used in its reporting programs to digital quality measures (DQMs) by 2025. This creates a greater emphasis on data standards to support FHIR, Trusted Exchange Framework and Common Agreement (TEFCA) and other programs.
Healthcare systems are eager to advance their technologies. However, budget and resource constraints can stall their data quality efforts. HIEs can become trusted partners as clinical data quality experts for downstream data utilization that benefit many stakeholders.
The National Committee for Quality Assurance (NCQA) launched its Data Aggregator Validation (DAV) program in 2021 to drive quality measurement toward a digital future. According to NCQA, data hubs that earn NCQA DAV status become reliable data sources, as their processes have demonstrated the ability to maintain data integrity, making data aggregators attractive partners for health plans and trusted sources for state and federal authorities.
One HIE is making significant strides in advancing data quality for its exchange participants. The Wisconsin Statewide Health Information Network (WISHIN) is one of the nation’s first HIEs to become Data Partner certified. Their joint efforts proved valuable in several ways.
The NCQA DAV program can support health equity initiatives at local and state levels by providing complete and verified patient data. An opportunity for HIEs is in the collection and standardization of race and ethnicity data for their participants. In addition, collecting other data elements from numerous source systems provides HIE value and marketability for participants who need more technical means or resources to align this type of data for health equity improvement. Data analytics and reports can also help identify disparities, close care gaps, and enable providers to monitor diseases over time.
Strategy 2: Integrate data standards to support public health
Outcomes from the COVID-19 pandemic highlight the need for better data standards to support public health efforts. While the transition to digital healthcare has enabled providers to connect with patients in multiple ways, tracking disease patterns, test results and population impact requires data that is consistently captured and standardized across technology systems.
HIEs can pinpoint opportunities to support public health more comprehensively. For example, through syndromic (early-stage) surveillance beyond COVID-19, analytics tools for public health agencies can present vaccination rates and help understand disease prevalence and health disparities. One example of this in practice is how health data from Hawaii HIE combined health data with Social Vulnerability Index data power an analytics platform that helps the county of Hawaii understand the current impact of disparities. The analytics also help evaluate the success of various programs and initiatives in decreasing the impact of health disparities.
HIEs are at a point of inflection to refine their role and position to support value-based care efforts for many stakeholders. Moving beyond transactional data intermediaries to population health enablers and health data utility (HDU) models enables HIEs to diversify partnerships and revenue streams. HIEs that double down on quality data initiatives not only advance the industry by addressing the digital evolution that is needed but, more critically, they facilitate data-driven healthcare improvement.
HDUs, for example, utilize existing technical and relationship infrastructures within and across communities to bring together personally identifiable health data as well as population and public health data more comprehensively. An HDU drives value back to participants through data exchange, analytics, reports and other strategic insights from community-wide data points to support public health use cases.
HIEs can advance HDU in the following ways.
Inbound and outbound agnostic data capabilities. HIEs that address industry standards in their technology — like FHIR, APIs, database connections, CCD parsing and outbound services to push NCQA DAV-validated CCDs — will quickly prove their commitment to interoperability.
An extensible data model. By creating infrastructure flexibility, HIEs can support more than just a community health record solution and maximize the data for countless use cases.
Strong alliances with public health departments or state agencies. High-quality data with solid data governance drives effective clinical use cases like understanding health trends, social determinants of health (SDOH) impact and the effectiveness of cross-community initiatives.
HIEs are well-positioned to further demonstrate their value by improving technical infrastructures, aligning with advancing data standards, and partnering closely with their participants and stakeholders.
Strategy 3: Make data the driver for future success
Emerging delivery models for at-home care, mobile device wearing, concierge medicine, and analytics like artificial intelligence and machine learning will continue to generate new patient data points. Innovative strategies to create robust data exchange are only the first part of the journey for HIEs. Increasing the utility of the data becomes the driver for future success.
Suppose an HIE can offer measure calculation or advanced analytics, such as stratifying a population based on risk to meet health equity or quality objectives. In that case, use cases become repeatable and scalable.
Moving HIEs beyond a sustainable exchange and into a thriving pillar of the healthcare community can be achieved with an emphasis on quality data, modern technology and strategic partnerships that deliver a return on value-based improvements.
HIEs should address the following critical success questions as part of a long-term strategy:
Renee Towne is vice president of population health at KPI Ninja by Health Catalyst.