The Problem and Solution of Duplicate Patient Records in Healthcare Analytics

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A foundational assumption in healthcare data analysis is that 'each patient identifier maps to exactly one individual.' However, this assumption frequently breaks down in real systems. Duplicate records arise from predictable vectors like initial registration errors or cross-institutional data integration without identity reconciliation, accumulating into serious problems. The consequences are not limited to inflated counts (e.g., cost per patient); longitudinal analysis—tracking disease progression and comorbidity rates—becomes impossible because the patient's history is fragmented across multiple IDs. Furthermore, in data warehouse joins, duplicate IDs cause row counts to multiply exponentially, leading to aggregated metrics (like average cost or total procedures) that are fundamentally incorrect. This issue severely impacts machine learning model training. Since models cannot see a patient's complete risk profile, the true risk associated with multiple comorbidities is systematically underestimated. The structural solution requires identity resolution upstream—before data reaches the warehouse—using mechanisms like a Master Patient Index (MPI) or an enterprise MDM layer. This process must involve probabilistic matching and explicit merge rules within a dedicated system, rather than being solved by ad-hoc SQL queries or pipeline patches.