Biometrics for Positive Patient Identification: 3 Things to Know

By Dan Cidon, Chief Technology Officer, NextGate
Twitter: @NextGate
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As the need for accurate patient ID matching and improved data interoperability in healthcare continues, utilization of biometrics is an emerging trend that is giving organizations more flexibility, accuracy, and confidence when locating, aggregating, and sharing patient records.

Researchers project that the global healthcare biometrics market is expected to reach 5.6 billion by 2022, up from 1.34 billion in 2015. Currently, the U.S. and Canada comprise 70 percent of the biometrics market.

Today, more than 150 hospitals and healthcare organizations in the U.S., such as regional blood centers, already use biometrics for patient registration. Many are going beyond the initial registration applications to more complex approaches to improve access to and preserve the security of patients’ health information.

There are several technological advances driving increased adoption of biometrics across the board for positive patient identification, including facial, palm and voice recognition and ear, fingerprint and iris scanning. Healthcare has proven to be a fertile ground for biometric technology thanks to value based care and population health initiatives, which rely on better patient matching capabilities to reduce medical errors, combat fraud and medical identity theft, and decrease costs associated with billing inaccuracies and redundant medical testing.

Biometric technology holds great promise and potential to reduce these significant, costly and even deadly patient matching issues, however there are still significant challenges in moving to a biometrics-based patient-matching approach. Although biometrics can provide superior levels of accuracy, security and convenience, it is important to understand the extent to which the technology is applicable in healthcare.

  1. While the accuracy of biometric solutions has improved, it’s an ancillary solution to the problem of positive patient identification, where in fact, integration is the primary deterrent. Today, the architecture of most biometric solutions is unable to scale at an enterprise level since it can only manage a subset of patients.
  2. Use of biometrics in healthcare also faces large-scale deployment challenges given some biometric solutions, including palm vein and iris scanners, require highly specialized, stand-alone hardware. Biometric identification solutions, such as facial recognition however, use commodity technology already built into a patient’s smartphone. This simplifies deployment by offloading enrollment and registration to devices owned and maintained by the patient. Additionally, with the patient in control, user anxiety associated with invasive sensors are diminished.
  3. Maintaining biometrics solutions in-house, also requires significant server-side hardware and internal infrastructure. Even the largest healthcare organizations with the deepest pockets may find the cost of managing and integrating biometric data to be prohibitive. Therefore, a cloud-based approach to biometric patient matching is best to provide the necessary flexibility and affordability, especially when used with biometric solutions that leverage commodity hardware.

While biometrics holds great promise in patient identification, it will require organizations to invest heavily in integration technologies as well as leverage the use of commodity hardware like smartphones, to lower implementation and infrastructure constraints and broaden adoption and acceptance of the technology.