Patient Identity Is Healthcare AI's Missing Control

As hospitals and payers race to deploy AI, a foundational problem is getting overlooked: the patient record feeding the model. Healthcare Dive argues that before asking if a model is ready for patients, organizations should ask if the patient record is ready for the model.

Patient identity is often fragmented across systems, with duplicate records, mismatched demographics, and merged files scattering a single person's data. When an AI model draws on an incomplete or wrong record, its output is compromised no matter how sophisticated the algorithm. Bad inputs produce bad predictions, and in clinical settings that carries real risk.

The practical takeaway is that identity resolution and record matching are prerequisites, not afterthoughts, for trustworthy AI. Health systems investing in models need parallel investment in the data infrastructure that ensures each record accurately reflects one patient. Governance of patient identity, in other words, is becoming a core control for safe and effective AI deployment across the industry.