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 For example, someone who has moved recently and changed their name due to marriage will show up in one system with an out-of-date name and address, and in another system with an up-to-date name and address.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 all makes it hard 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 being exchanged between IT systems or between organizations—to prevent the wrong information from being associated with the wrong patient. And an increasing number of accountable care organizations and an industry-wide push for large-scale health analytics exercises means that the right health information has to be tied to the right patient.
But this 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, called master patient indexes (MPIs) haven’t seen true innovation in years. Conventional versions of these MPIs 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, conventional MPIs suffer unacceptably high duplicate rates and require teams of data stewards to manually resolve duplicate patient identities missed by their algorithms. All of this results in providers being unable to find 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 (MPI) that represents a groundbreaking approach to the problem of patient identity resolution. The Verato Universal MPI is a pre-built, cloud-based, nationwide master patient index that enterprises 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 matching patient identities. Rather than just using algorithms that compare demographic data, the Verato Universal MPI is itself pre-populated with pre-mastered and continuously-updated demographic data spanning the entire U.S. population. It leverages this pre-mastered database as an “answer key” to match and link patient identities that other patient matching solutions can never match. This approach is called “referential matching” and the Verato Universal MPI is the only one of its kind to use it.
Ultimately, because of its greater accuracy and ease of implementation, the Verato Universal MPI can support the rapidly emerging patient matching needs that conventional MPIs cannot.