Population health management can help organizations boost quality performance and gain better insights into patient datasets. However, the use of unstructured text, missing standard codes, and other inconsistencies in clinical patient data can make it difficult to identify the right cohort of patients to include in a specific program.

Common workarounds to fill in the gaps – code crosswalks – often lose fidelity over time. Maintaining this data is a burden and requires additional manual effort from valuable staff resources.

Listen to this short 30-minute webinar to:

• Discuss the key challenges of cleaning up data for self-service analytics
• Discover how to enhance the specificity and quality of your foundational data to increase confidence in an underlying dataset
• Leverage normalized data to identify more precise patient populations, even with standards-based value sets