- Automating patient data cleanup with IMO Precision Normalize reduces manual effort and supports greater focus on innovation and user satisfaction
- Seamless API integration helps restore and maintain data fidelity across systems and workflows
- Robust clinical terminology from IMO Health enhances the accuracy and value of clinical decision-making
In our recent webinar, Beyond data warehouses: CyncHealth’s path to improved data quality, IMO Health’s Senior Vice President of Customer Success, Matt Cardwell, met with CyncHealth’s Chief Data Officer, Naresh Sundar Rajan, to discuss how they addressed inconsistent data sources as Nebraska and Western Iowa’s health data exchange.
In the webinar, the two discuss CyncHealth’s journey in integrating IMO Precision Normalize to achieve 83.7% data normalization accuracy without human intervention, while streamlining pipelines, and reducing manual onboarding.
Short on time? Scroll down for quick highlights and key insights from this webinar.
Leveraging IMO Health’s comprehensive clinical terminology
By leveraging IMO Health’s modular solutions and terminology servers, CyncHealth was able to standardize data across multiple sources, enhancing interoperability, improving data quality, and optimizing workflows for scalable, high-quality data management.
Automating data standardization throughout processes
In this clip, CyncHealth discusses how they scaled data standardization and automated clinical data pipelines, achieving almost 84% hands-free normalization of uncoded data. By implementing real-time APIs (Application Programming Interface) and refining clients’ data onboarding processes, CyncHealth improved data quality and interoperability, enabling seamless reporting and considerable time savings across their operations.
Streamlining workflows with data normalization
In this clip, CyncHealth explains how automated normalization has streamlined workflows for analysts and informaticists. By using standard code sets for cohort selection and quality reporting, they reduced manual data mining and improved efficiency. Upstream validation now ensures consistent data quality, speeding up onboarding and integration processes.
Ensuring high confidence matches throughout operations
CyncHealth details the process of setting accurate thresholds for data normalization. Through rigorous testing with a 60,000-row cohort and collaboration between clinicians and informaticists, they refined IMO Health’s default thresholds, achieving high-confidence auto-matches for clinical data while continuing to fine-tune for better accuracy across domains.
Navigating solution integration in tech systems
CyncHealth shares how they integrated IMO Health technology to handle large-scale state-level data. They employed a dual approach—transactional stabilization and downstream analytics normalization—to manage millions of records daily. By leveraging batch processes and search APIs, they ensured seamless data validation across both transactional and analytical systems.
Why upstream validation is key to interoperability
In this clip, CyncHealth emphasizes that high-quality data is the foundation for effective analytics and interoperability. Despite modern technologies, achieving consensus on data quality across clinical and technical perspectives remains challenging. Upstream validation is crucial, as even minor issues can have significant downstream impacts on data accuracy and system interoperability.
