Patient misidentification: what IAM teams need to fix at registration

TL;DR: Patient identity matching can be as low as 80% within a single care setting and 50% across shared health information exchanges, while healthcare organisations report $1.3M in annual identity resolution costs and $17.4M in denied claims, according to Imprivata. The problem is not just operational friction, it is a governance failure that treats identity confidence as optional instead of foundational.

NHIMG editorial — based on content published by Imprivata: patient misidentification and biometric patient identification

By the numbers:

• Matching patients to their medical records can be as low as 80% accurate within a single care setting.
• Hospitals face an average of $17.4 million per year in denied insurance claims due to inaccurate patient identification.
• Duplication rates in healthcare are as high as 30% in some organizations.

Questions worth separating out

Q: How should healthcare organisations reduce patient misidentification at registration?

A: They should strengthen identity proofing before the first record is created, because most downstream errors begin with weak intake.

Q: Why does patient misidentification create both safety and financial risk?

A: Because the same wrong identity link can affect clinical decisions, billing, and claims processing.

Q: What signals show that patient identity controls are not working?

A: High duplicate-record rates, repeated identity-resolution work, low matching accuracy across shared systems, and growing denied-claim costs are the clearest signals.

Practitioner guidance

• Tighten registration identity proofing Require stronger verification at the first patient touchpoint, especially where similar names, incomplete demographic data, or manual entry create ambiguity.
• Measure duplicate-record risk across systems Track matching accuracy, duplicate creation rates, and identity-resolution costs across EHR, lab, imaging, and exchange workflows so the team can see where trust breaks down.
• Use biometric matching where ambiguity is high Apply face matching or other biometric verification in environments where conventional demographic checks produce unacceptable mismatch rates.

What's in the full article

Imprivata's full article covers the operational detail this post intentionally leaves for the source:

• How biometric patient identification is applied at registration to reduce duplicate record creation.
• The specific way face-matching links patient identity to the enterprise master patient index and EHR.
• Why misidentification affects denied claims, operational efficiency, and patient experience in practice.

👉 Read Imprivata's analysis of patient misidentification and biometric identity matching →

Patient misidentification: what IAM teams need to fix at registration?

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