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Why biometric data could be the answer to patient-matching problem

Facial scans and other forms of biometric data offer a viable solution to persistent challenges with matching patients to their records.
By admin
Dec 1, 2022, 5:04 PM

Healthcare is no stranger to biometric data. Metrics such as fluid retention and heart rhythm can help care teams monitor heart procedure patients for post-surgical complications. Biometric data also plays an important role in care-at-home programs, which enable patients to recover from hospitalization in a familiar setting.

A recent report from the Pew Charitable Trusts explores a different, operational use case for biometric data: Matching patients to electronic health record (EHR) data.

The need for accurate patient matching is straightforward. With a single patient’s records stored in EHR systems at different healthcare facilities, or even different EHRs within the same facility, providers must piece together records to get a complete medical history. All told, match rates can be as low as 50%, according to a separate report from Pew.

Matching is a challenge for a few reasons, Pew pointed out. Multiple patients may have the same personal details, such as name and birth date; details such as mailing address and phone number can change often; different EHR systems may format data differently; clinical or administrative staff may enter data incorrectly; and, the United States hasn’t adopted a unique identification number for each patient.

Enter biometrics. Fingerprints, facial scans and palm scans are unique to individuals, and they change little over time. Plus, Pew said, many consumers are familiar with using biometrics for purposes ranging from unlocking smartphones to completing financial transactions.

Along with the clinical use cases described above, biometrics can already be found in administrative healthcare workflows. As Definitive Healthcare noted, biometric data can expedite computer log-ins and provide secure access to restricted areas. Biometric data can also streamline patient sign-in by eliminating the need to fill out forms. This has the additional benefit of reducing fraud, since it’s much harder for patients to provide false information to obtain care services or medications.

In that sense, Pew said, it’s a natural extension of the use case to use biometric data to match health records to a unique patient.

Facial recognition the biometric data frontrunner

Based on discussions with more than 40 experts, Pew determined that face scans were most feasible for widespread implementation across healthcare. That’s because leading EHR systems are already equipped to capture and store images of a patient, while raw data that’s stored in the EHR is already protected under HIPAA. On top of that, clinical staff can easily verify a face match, and there’s no need for dedicated fingerprint-reading hardware at intake that must be frequently sanitized.

Pew’s expert panel said the optimal model for storing and accessing biometrics is to let patients use third-party smartphone apps. This match-on-device approach is preferred because it doesn’t require a single, nationwide standard for capturing data. That said, third-party apps should be required to use existing open standards, including OAuth/OpenID Connect and Unified Data Access Profiles. In addition, apps should share only alphanumeric keys to verify identity; biometric data should remain within the EHR and the patient’s smartphone.

At the same time, Pew said health systems that ultimately use biometrics for patient matching should still verify demographic data. This will further reduce errors while accommodating patients who prefer not to share biometric data or can’t share due to age, cognitive impairment, or lack of technology access.

Brian Eastwood is a Boston-based writer with more than 10 years of experience covering healthcare IT and healthcare delivery. He also writes about enterprise IT, consumer technology, and corporate leadership.

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