One of the biggest challenges in patient identification is the ability to tie together all of a patient’s information and accurately associate it with that patient. Organizations struggle with this because they have patients’ information stored in many different IT systems, and this information enters those systems from many different avenues—at registration desks, from portals, from smart devices, or from other organizations during health information exchange.
Because of this, one patient may be represented with different demographic data in each IT system, making it hard to tie all of that patient’s information together. For example, someone who has moved recently and changed their name due to marriage will show up in one system with an out-of-date name and address, and in another system with an up-to-date name and address. And they may show up in a Lab system with only a name and birthdate. This makes it a challenge to associate all of this patient’s health data with that patient.
This challenge is becoming increasingly more difficult due to new IT systems, new “smart” medical devices, new telemedicine initiatives, and an increasing number of online portals that patients can log into. On top of this, organizations are rapidly being acquired, and electronic health record (EHR) systems are being consolidated, merged, and migrated.
At the same time, this patient matching and patient identity challenge is becoming exponentially more important to solve. True interoperability requires accurate patient identification each time information is exchanged between IT systems or between organizations—to prevent the wrong information from being associated with the wrong patient. Simultaneously, value-based care initiatives and population health analytics presume that providers are able to establish a comprehensive and complete view of each patient’s health history across the entire continuum of care.
However, the challenge of associating the right health information with the right patient is being addressed by patient matching technologies that are based on decades-old algorithms—and these patient matching technologies, found in EHRs and in enterprise master patient index technologies (EMPIs), haven’t seen true innovation in years. They’re fundamentally limited by the quality of the underlying demographic data they are matching, meaning they cannot make matches when patient records have out-of-date, incomplete, incorrect, or very different demographic data.
Because of this, EHRs and EMPIs suffer unacceptably high duplicate record rates which results in incomplete medical records, redundant testing, impaired clinical care, and unclaimable revenue. To compensate, organizations often must hire teams of health information management (HIM) staff to manually resolve duplicate records their algorithms cannot definitively make a decision on.
This is where Verato comes in.
Verato has pioneered a powerful new patient matching technology called “Referential Matching.” Rather than directly comparing the demographic data from two patient records to see if they match, Verato instead matches that demographic data to its comprehensive and continuously-updated reference database of identities. This proprietary database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete profile of demographic data spanning a 30-year history. It is essentially a pre-built answer key for all patient demographic data.
By matching records to this database instead of to each other, Verato can make matches that conventional patient matching technologies could never make—even patient records containing demographic data that is out-of-date, incomplete, incorrect, or different.
Referential Matching isn’t simply a better algorithm—it is a completely new approach that represents a quantum leap in patient matching technology and accuracy.
Referential Matching is the silver bullet for your patient matching challenges
Verato offers two cloud-based solutions powered by Referential Matching technology that can quickly and dramatically improve your patient matching—and solve your patient matching challenges.
Verato Auto-Steward™ is a simple cloud-based plug-in that automatically finds and resolves your EHR or EMPI technology’s missed matches and duplicate records using Referential Matching technology—and without disrupting any of your existing processes or IT systems. Verato Auto-Steward can even automatically resolve the “potential duplicate records” your EHR or EMPI has flagged for manual resolution by HIM staff.
Or, if you want a world-class EMPI technology that is the most accurate, most secure, easiest to implement, and most cost effective EMPI on the market, deploy the Verato Universal™ MPI in as little as six weeks and use its Referential Matching technology to instantly achieve the highest match accuracy rates in the industry.