WEBINAR | From 252 Days to 4.5: How AllianceChicago Automated 99% of Its Health Data Mapping with AI

Andrew Hamilton, RN, BSN, MS, Informatics Officer & Deputy Director, AllianceChicago; Guy Tsafnat, PhD, FAIDH, Founder & Chief Scientific Officer; and Davera Gabriel, RN, FHL7, FAMIA, Director of Client Success, Evidentli. AllianceChicago, a not-for-profit health network aggregating clinical data from 81 community health centers across the US, sought to improve the speed and efficiency of normalizing its data into common research data models required for national networks such as All of Us and CAPriCORN. Partnering with Evidentli, AllianceChicago piloted the AI-driven Piano platform to automate the ETL process of mapping their CAPriCORN Common Data Model onto the OMOP CDM. The results were striking: what previously required 252 person-days was completed in 4.5 person-days, with AI handling over 99% of all mappings with near-perfect accuracy.

Upon completing this webinar, participants will be able to describe the data normalization challenges facing community health networks participating in national research initiatives, explain how AI-driven automation can dramatically accelerate OMOP CDM mapping, and assess the applicability of similar approaches within their own data infrastructure.