Patient identity is the missing control in healthcare AI

With physician AI adoption reaching 81% for administrative and documentation workflows, health systems face escalating risks from poor data quality, including duplicate records that affect an estimated 8% to 10% of patient files. Inaccurate patient matching and fragmented records can cause clinical AI to trigger lethal dosing errors, incorrect risk scores, or misrouted alerts. Consequently, health administrators and clinicians must treat upstream patient identity resolution and continuous data governance as prerequisite safety controls before deploying AI tools.

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