Healthcare organizations must achieve mastery of high-quality data and analytics to thrive within value-based care models. Today’s IT professionals are challenged to design systems that improve data exchange with industry stakeholders as well as acquire more complete and accurate patient information for quality measures reporting. Without a strategy in place that addresses each of these key areas, hospitals and health systems face significant barriers to achieving their overall population health or financial goals.
A recent webinar hosted by Health Data Management demonstrated how one statewide Health Information Exchange (HIE) successfully addressed this reality through the right infrastructure deployment and workflows, and helped a member ACO make substantial improvements in the accuracy of reporting measures. Michael Gagnon, CTO of Vermont Information Technology Leaders (VITL), was joined by Health Language executives Brian Laberge, Director of Client Services Strategy, and Celeste Adams, Clinical Application Analyst, for an analysis of reporting challenges and a presentation of best practices for improving data accuracy and completeness. The speakers provided an overview of the new end-to-end data quality model used to elevate quality reporting and advanced population health initiatives.
VITL was tasked with addressing the clinical and technical challenges of reporting quality measures as the state of Vermont shifts from fee-for-service reimbursements to an ACO-based model. Encompassing one academic medical center, 13 community-based and critical access hospitals, and two psychiatric hospitals, all major players within the ACO are currently connected through the state HIE operated by VITL. Negotiations are also underway with CMS to establish the All Payer Model (APM).
Completeness, accuracy, and consistency became VITL’s mantra for data quality. Following an analysis that distinguished the volume and variability of data issues and identified the amount of data needed for accurate capture and aggregation, VITL determined that an effective strategy must address three stages of data quality:
- Data quality at the source: VITL partnered with member organizations to improve data collection into their EHRs.
- Data quality in the network: VITL leveraged a specialized Master Patient Indexes (MPIs) and comprehensive terminology management tools, such as Health Language’s LEAP Map Manager to clean and normalize data for analytics and reporting.
- End-point analysis: VITL created a feedback loop by having the organizations performing the analytics assess their data analytics needs and report back to VITL what is needed.
VITL had an HIE technology infrastructure in place for successfully transmitting information to be used at the point of care; however the solution was not sufficient for meeting the analytics needs of quality reporting and population health management. Therefore VITL invested in a number of solutions to lay the needed foundation for data quality, including an integration terminology management solution, data warehouse solution, separate master patient index (MPI), and business intelligence tools. The VITL team also built gateways and filtering mechanisms to identify populations, and established a mechanism for parsing disparate continuity of care documents (CCDs) down to the individual element level.
Health Language® solutions were an integral part of this infrastructure. Specifically, data quality dashboards are used to help sites identify missing or non-standard data, and the terminology management solution is used to map local codes to national standards.
The ACO is now collecting data on over 75,000 beneficiaries, which is greatly improved by VITL’s data management and quality solutions. By normalizing lab, drug, and care summary (CCD data) from hospitals, the ACO reports on ACO measures HbA1C (ACO measures 22 and 27) and blood pressure (ACO measures 24 and 28).
Using Health Language to manage clinical data, VITL now has the ability to:
- Create data quality dashboards to help sites identify missing or non-standard data
- Map local codes to national standards
- Implement an MPI at the warehouse for improved probabilistic matching of patients and managing cohorts
- Use BI tools for data analysis and graphical reporting
- Prepare the data mart for ongoing analysis
Contact us today to learn how the Health Language Enterprise Terminology Platform is positioning today’s healthcare organizations for forward-looking initiatives and the future of healthcare.