Most duplicate records start the same way. A patient shows up with a slightly different name, address, or phone number, and a system built for exact matches creates a second record. From there, the mismatch spreads across the EHR, the CRM, and every analytics or AI model built on top.
This two-page guide gives health system data, analytics, and AI leaders 10 concrete steps to cut duplicates at the source, not just clean them up after the fact. It covers the whole path, from agreeing on one definition of a duplicate to tying duplicate rate to the numbers leadership already tracks. Work through the steps in order, or start wherever your team already is.