Continuous AI biometric identification: Why manual patient verification is not enough!

By Deepak Borole, Project Manager at Chetu

Making sure you are talking to the right patient is a cardinal rule in healthcare.

Manual ID failures have led to moms taking home the wrong babies, or human errors that led to a wrong leg amputation.

Every clinical decision starts with making sure that you have the right medical information for the correct patient. A mistake can occur at registration or the operating room. Continuous vigilance is a must.

Patient misidentification remains a persistent patient safety and operational challenge across U.S. healthcare systems. The AHIMA and the Patient ID Now coalition states that  misidentification “remains a pervasive challenge in healthcare. Confusing one patient for another due to this issue jeopardizes patient safety and privacy and imposes significant financial burdens on patients and healthcare providers.”

The Ponemon Institute’s National Patient Misidentification Report by Imprivata shows that 84% of medical professionals believe that misidentification can lead to adverse errors. 64% report that these incidents occur regularly. Healthcare Innovation highlights that it takes up to 30 minutes per shift to locate accurate patient details, costing hospitals millions of dollars and leading to potentially ineffective care.

Why are traditional identity checks failing modern healthcare?

Too many healthcare organizations rely on processes like patient identification wristbands or verbal confirmation between doctors, nurses and patients. While standard protocols exist, these processes can cause data-entry errors, where minor inconsistencies arise. This includes spelling errors, inconsistent records, and formatting differences that can fragment patient records. Harried healthcare professionals can forget to check the ID wristband, for example. Although hospitals build in redundancies, mistakes still happen, sometimes with severe consequences.

Challenges: Duplicate records lead to misidentification

The duplication of medical records is a persistent challenge for healthcare information management systems. Clinician.com cites a report by the American Health Information Management Association that 8-12% of electronic health records (EHR) are duplicates that can contribute to fragmentation and potentially incomplete clinical information.

With so many records located in different systems, healthcare professionals are confronted with disjointed medical histories. Patients also change names due to marriages and divorces, which further complicates record matching.

Manual verification: Necessary but not sufficient

In recent years, the healthcare industry has become increasingly digital and interconnected. This shift, combined with AI and agentic AI, is playing a growing role in patient safety. The industry is exploring AI-driven continuous biometric identity systems to verify patients’ identities. These systems also detect irregularities in real time and go beyond manual processes that are prone to human error.

Healthcare organizations are adopting AI-driven systems and emerging Agentic AI systems to provide more accurate patient records. They generate reliable insights and support clinical decisions.

The industry has taken notice of the risk to patient safety and the need for biometric ID verification.

Grand View Research reports that the global healthcare biometrics market totaled $9.45 billion in 2023, and is expected to reach $42 billion by 2030, a 23.8% CAGR from 2024 to 2030. Data Insights Market reports that the AI-based biometric market is “poised for significant expansion” in multiple industries, including healthcare. The market was expected to reach $15 billion in 2025 and exceed $50 billion by 2033, an 18% CAGR.

A new model for patient verification

Continuous identification shifts from manual verification to constant identity assurance via behavioral signals or biometrics. The new model enables identification across key touchpoints, including registration, diagnostics, and portals. This approach ensures continuous validation of identity instead of relying on assumptions.

Key technologies enable continuous digital identification, including:

•       Facial biometrics match patient images with stored records.
•       Behavioral biometrics analyze patterns like typing and device usage.
•       Passive authentication verifies identity in the background without disruption.
•       Multimodal systems combine signals such as facial recognition, voice, fingerprints, and device data.

AI-powered analytics will form the foundation of these systems, which detect identity anomalies in real time. Emerging technologies like agentic AI enable autonomous monitoring and dynamic identity verification. Together, these technologies create an identity layer that promotes safer and more secure delivery of care.

Real-world example: Biometric patient identification in healthcare

The Journal of the American Medical Informatics Association reported that the 2024 update to the SAFER Patient Identification guidelines reflects growing hospital adoption of advanced identity verification, including three key practices: incorporating patient photos into EHRs, using biometrics—including facial recognition—to verify identity, and expanding barcode or RFID checks before care. Together, these changes promote a more reliable, multi-factor approach to reducing wrong-patient errors in clinical workflows.

Implementation challenges and collaboration

Implementation is not only about adopting new technologies. It also demands strong integration, privacy, governance, and team member acceptance. Although agentic AI requires minimal human oversight, the healthcare sector will certainly require a human-in-the-loop for the foreseeable future.

Biometric systems introduce privacy considerations. Thus, healthcare organizations must ensure encryption, secure storage, and clear patient consent. Since healthcare systems often operate on fragmented platforms like EHRs and telehealth portals, integrating biometrics requires interoperability and robust governance across IT, clinical, and other teams. Identity management now becomes an enterprise-level responsibility.

Build, buy, or hybrid identity infrastructure

Organizations need to determine the strategic implementation of a continuous identity system, as it can differ based on their scale, digital maturity, and other needs, which usually means administrators must decide whether to build their system, buy a third-party product, or use an AI solution provider to customize the off-the-shelf product.

Build

Large healthcare systems may prefer developing proprietary identification platforms, aiming to achieve full control over customization and governance. However, this requires significant investment and usually longer deployment timelines. However, building a system gives them total control and often lowers future expenses like annual subscriptions.

Buy

Many healthcare organizations adopt commercial biometric identity platforms because they are industry-tested with prebuilt compliance capabilities and functions that enable quick deployment. But they may have recurring fees and limited customization options.

Hybrid

Some healthcare organizations are adopting hybrid models in which they combine the advantages of a ready-built product but use a software solution provider to customize the platform. This approach balances flexibility and speed.

The future: Identity as a continuous patient safety layer

As healthcare becomes more distributed across hospitals, telehealth, and remote monitoring, identity assurance is becoming essential to safe care delivery, leading to the popularity of continuous biometric identity, which can support:

•       Safer medication administration
•       Accurate EHR matching
•       Secure telehealth access
•       Fraud prevention
•       Reduced duplicate records

These capabilities elevate identity from manual oversight to a digital core component of patient safety. Advancements in AI, including agentic AI, will further enable real-time, autonomous identity monitoring. This allows healthcare systems to detect risks and trigger verification, which in the long term improves patient outcomes.

About the author

Deepak Borole is a Project Manager at Chetu, a global leader in AI and digital transformation solutions, where he oversees general healthcare, remote healthcare, and specialty healthcare portfolios.

Article Topics

biometrics  |  Chetu  |  digital identity  |  healthcare  |  identity verification  |  patient identification