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Are Biometrics Tools Practical For Hospital Use?

Posted on February 21, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

In theory, using biometrics tools could solve some of the hospitals’ biggest data management problems.

For example, if the patient had to register for treatment when seeking care at a hospital emergency department (something I saw in place at my local hospital), it would presumably cut down medical identity fraud substantially. Also, doing patient matching using biometric data could make the process far more precise and far less error-ridden. When implemented correct it can achieve these goals.

In addition, requiring hospital employees to use biometric data to access patient records would lock down those records more tightly, and would certainly make credential sharing between employees far more difficult.

Unfortunately, hospitals that want to use biometric technology have to overcome some major obstacles. According to an article by Dan Cidon, CTO of NextGate, those obstacles include the following:

  • Biometric solutions need to be integrated with primary hospital systems, and that process can be difficult.
  • Most biometric solutions can only manage a subset of patients, which makes it difficult to scale biometrics at an enterprise level.
  • Standard biometric solutions like palm vein and iris scanners demand highly-specialized standalone hardware.
  • Bringing biometrics in-house demands significant server-side hardware and internal infrastructure, bringing the total cost to one that even major health systems might balk at.

On the other hand, Cidon notes, some of these issues can be minimized.

Take the problem of acquiring and maintaining specialized devices. To bypass this issue, Cidon recommends that hospitals try using lower-impact solutions like facial recognition, commodity technology built into patient smartphones. By relying on patient smartphones, hospitals can offload enrollment and registration to patient-owned devices, which not only simplifies deployment but also increases user comfort levels.

He also notes that by using a cloud-based approach, hospitals can avoid allocating a high level of server-side hardware and infrastructure to biometrics, as well as getting added flexibility and affordability, especially if they leverage commodity hardware to do the job.

Even if hospitals act on Cidon’s recommendations, going biometric for patient matching, security and medical identity theft protection will be a major project. After all, hospitals’ existing IT infrastructure almost certainly wasn’t designed to support these solutions and putting them in place effectively will probably take a few iterations.

Still, if putting biometric solutions in place can address critical safety and operational issues, especially dangerous patient record mismatches, it’s probably worth a try.

Breaking Bad: Why Poor Patient Identification is Rooted in Integration, Interoperability

Posted on December 20, 2017 I Written By

The following is a guest blog post by Dan Cidon, Chief Technology Officer, NextGate.

The difficulty surrounding accurate patient ID matching is sourced in interoperability and integration.

Coordinated, accountable, patient-centered care is reliant on access to quality patient data. Yet, healthcare continues to be daunted by software applications and IT systems that don’t communicate or share information effectively. Health data, spread across multiple source systems and settings, breeds encumbrances in the reconciliation and de-duplication of patient records, leading to suboptimal outcomes and avoidable costs of care. For organizations held prisoner by their legacy systems, isolation and silo inefficiencies worsen as IT environments become increasingly more complex, and the growth and speed to which health data is generated magnifies.

A panoramic view of individuals across the enterprise is a critical component for value-based care and population health initiatives. Accurately identifying patients, and consistently matching them with their data, is the foundation for informed clinical decision-making, collaborative care, and healthier, happier populations. As such, the industry has seen a number of high-profile initiatives in the last few years attempting to address the issue of poor patient identification.

The premature end of CHIME’s National Patient ID Challenge last month should be a sobering industry reminder that a universal solution may never be within reach. However, the important lesson emanating in the wake of the CHIME challenge is that technology alone will not solve the problem. Ultimately, the real challenge of identity management and piecing together a longitudinal health record has to do with integration and interoperability. More specifically, it revolves around the demographics and associated identifiers dispersed across multiple systems.

Because these systems often have little reason to communicate with one another, and because they store their data through fragmented architecture, an excessive proliferation of identifiers occurs. The result is unreliable demographic information, triggering further harm in data synchronization and integrity.

Clearly, keeping these identifiers and demographics as localized silos of data is an undesirable model for healthcare that will never function properly. While secondary information such as clinical data should remain local, the core identity of a patient and basic demographics including name, gender, date of birth, address and contact information shouldn’t be in the control of any single system. This information must be externalized from these insulated applications to maintain accuracy and consistency across all connected systems within the delivery network.

However, there are long-standing and relatively simple standards in place, such as HL7 PIX/PDQ, that allow systems to feed a central demographic repository and query that repository for data. Every year, for the past eight years, NextGate has participated in the annual IHE North American Connectathon – the healthcare industry’s largest interoperability testing event. Year after year, we see hundreds of other participating vendors demonstrating that with effective standards, it is indeed possible to externalize patient identity.

In the United Kingdom, for example, there has been slow but steady success of the Patient Demographic Service – a relatively similar concept of querying a central repository for demographics and maintaining a global identifier. While implementation of such a national scale service in the U.S. is unlikely in the near-term, the concept of smaller scale regional registries is clearly an achievable goal. And every deployment of our Enterprise Master Patient Index (EMPI) is a confirmation that such systems can work and do provide value.

What is disappointing, is that very few systems in actual practice today will query the EMPI as part of the patient intake process. Many, if not most, of the systems we integrate with will only fulfill half of the bargain, namely they will feed the EMPI with demographic data and identifiers. This is because many systems have already been designed to produce this outbound communication for purposes other than the management of demographic data. When it comes to querying the EMPI for patient identity, this requires a fundamental paradigm shift for many vendors and a modest investment to enhance their software. Rather than solely relying on their limited view of patient identity, they are expected to query an outside source and integrate that data into their local repository.

This isn’t rocket science, and yet there are so few systems in production today that initiate this simple step. Worse yet, we see many healthcare providers resorting to band aids to remedy the deficiency, such as resorting to ineffective screen scraping technology to manually transfer data from the EMPI to their local systems.

With years of health IT standards in place that yield a centralized and uniform way of managing demographic data, the meager pace and progress of vendors to adopt them is troubling. It is indefensible that a modern registration system, for instance, wouldn’t have this querying capability as a default module. Yet, that is what we see in the field time and time again.

In other verticals where banking and manufacturing are leveraging standards-based exchange at a much faster pace, it really begs the question: how can healthcare accelerate this type of adoption? As we prepare for the upcoming IHE Connectathon in January, we place our own challenge to the industry to engage in an open and frank dialogue to identify what the barriers are, and how can vendors be incentivized, so patients can benefit from the free flow of accurate, real-time data from provider to provider.

Ultimately, accurate patient identification is a fundamental component to leveraging IT for the best possible outcomes. Identification of each and every individual in the enterprise helps to ensure better care coordination, informed clinical decision making, and improved quality and safety.

Dan Cidon is CTO and co-founder NextGate, a leader in healthcare identity management, managing nearly 250 million lives for health systems and HIEs in the U.S. and around the globe.