AI-Accelerated Harmonization for Real-World Data

Harmonizing complex healthcare data has always been a labor-intensive process, as skilled data engineers and subject-matter experts must collaborate to write bespoke pipelines for each dataset. This process is highly technical and error-prone, requiring data engineers to have access to the environment in which the data lives. Rhino's Data Harmonization Engine (DHE) streamlines this process by using AI to enable data transformation workflows that can be executed remotely. In this session we’ll demonstrate how the DHE can transform complex real-world data (RWD) into both common data models such as OMOP as well as custom project-specific data models.

What you'll learn:

• Why existing approaches to data harmonization can’t scale without agentic AI
• How you can use Rhino’s semantic embeddings to harmonize local codes to standard vocabularies such as SNOMED and custom vocabularies.
• How Rhino integrates with frontier LLMs to transform local tables into common data models such as OMOP, FHIR, and custom data models without access to the underlying data.
• How you can design and execute data harmonization pipelines remotely with the work running where the data already sits rather than requiring the data to be moved.