Referential Matching: The Silver Bullet for Patient Identity and Patient Matching
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. 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 For example, someone who has moved recently and changed their name due to marriage will show up in one system with an out-of-date name and address, and in another system with an up-to-date name and address.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 or merged.
At the same time, this 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 technologies that are based on decades-old algorithms—and these technologies, found in EHRs and in enterprise master patient index technologies (EMPIs), haven’t seen true innovation in years. EHRs and EMPIs cannot see through bad or sparse data (like maiden/married names, old/new addresses, and missing data) to match and link health records to the right patients.
Because of this, EHRs and EMPIs suffer unacceptably high duplicate rates and require teams of health information management (HIM) staff to manually resolve duplicate patient identities missed by their algorithms. All of this results in incomplete medical records, redundant testing, impaired clinical care, and unclaimable revenue.
This is where Verato comes in. We’re a leading provider of cloud-based patient matching solutions including the Verato Universal™ MPI, which is a revolutionary master patient index that represents a groundbreaking approach to the problem of patient identity resolution. The Verato Universal MPI is a cloud-based solution that organizations can simply “plug into” to resolve, match, and link their patient identities—without the need for extensive algorithm tuning, data standardization, data governance, data cleansing, or data stewardship processes.
The big difference with the Verato Universal MPI is how it approaches patient matching. The Verato Universal MPI leverages a powerful new approach to patient matching called “Referential Matching.” Rather than directly comparing the demographic data of two patient records to see if they match, Verato instead compares the demographic data from those patient records to Verato’s comprehensive and continuously-updated reference database of identities. This database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete profile of demographic data—including nicknames, aliases, maiden names, common typos, past phone numbers, and old addresses. By matching patient records to identities in this reference database, Verato can make matches that conventional EHR and EMPI technologies can never make—even if patient records contain errored, out-of-date, incomplete, or inconsistent demographic data.
Ultimately, because of its greater accuracy and ease of implementation, the Verato Universal MPI can support the rapidly emerging patient matching needs that conventional EMPIs cannot.