The True Patient Record: Integrating Multimodal Data for Complete and Accurate Clinical Measures

Clinical risk scores and quality measures often attempt to rely solely on structured EHR data, yet the quality of this data is severely lacking. Evidence shows that almost 40% of important diagnoses are mentioned only in free text notes, and structured records often capture only a fraction of critical information, such as Social Determinants of Health (SDoH), tumor properties, or family history. These gaps mean that widely used clinical measures, patient risk scores, and population health metrics based on structured data only may fail to predict outcomes that scores derived from a complete view of each patient that encapsulates information extracted from unstructured data.

This webinar explains why multimodal data integration is a must. We will introduce the John Snow Labs Patient Journeys platform’s solution for clinical measures, which is designed to overcome this challenge by synthesizing all historical patient information across all modalities (structured EHR, unstructured text, FHIR, PDF, and more). The platform enables technical and clinical teams to define, edit, and calculate high-fidelity measures at scale. Crucially, it maintains full provenance and confidence values across every calculation, ensuring that each clinical measure can be tracked directly to its source data for full auditability. The result is the ability to calculate clinical measures at much higher accuracy, at scale, updated every time a new piece of data about each patient is introduced.