Optimizing Patient Record Deduplication in Diabetes Care with AI & ML

Duplicate and fragmented patient records are a major challenge in diabetes care, often causing delayed diagnoses, duplicate testing, incomplete patient histories, and higher administrative costs. This case study shows how AI & ML transformed Healthcare Data Management through intelligent Healthcare Data Deduplication using ML classification models. By identifying and merging duplicate records across EHR systems, labs, and outpatient facilities, the solution created a reliable single patient view, reduced manual review time by 60%, improved care quality by 25–40%, and generated $700K+ in annual savings.