Laurel, Yanny, and Patient Matching
In the midst of one of the most politically and socially divisive eras in American history, who would have thought a simple 2-second audio recording would be what drove the nation to the brink of civil war.
Laurel or Yanny. Yanny or Laurel. Two very different sounding names somehow simultaneously embedded in one audio recording—and now forever embedded in our nation’s consciousness.
I have never felt so doubtful of my sanity as when I sat next to two coworkers, played the recording, and was told by both that they heard “Yanny.”
I also have never been so doubtful of my coworkers’ sanity.
But after extensively reconfirming at least my own sanity (if not my coworkers’), the “Laurel-Yanny” illusion got me thinking about the healthcare industry’s patient matching challenges.
Bear with me.
We already know that what I hear as Laurel, you hear as Yanny—yet both sounds represent the same audio recording. Now consider an EHR with two patient records—one for someone named Laurel, another for someone named Yanny. Let’s assume that in reality, both of these records represent the same person—perhaps Laurel changed her name since her last visit to her physician, or started going by her middle name instead of her first name.
An EHR’s patient matching technology would never match both records together, especially if the Yanny record had an old address as well as the old name. Instead, the health system would create a duplicate record for Laurel.
But Verato has created a powerful new patient matching technology—called Referential Matching—that can make matches despite radical differences in demographic data like name and address changes. Rather than directly comparing the demographic data from two patient records to see if the records match, Verato instead compares the demographic data from those records to its 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 spanning a 30-year history—including nicknames, aliases, maiden names, common typos, past phone numbers, and old addresses.
By matching patient records to identities in our reference database, Verato can make matches that conventional patient matching technologies could never make. For example, even if one patient record says “Laurel” and contains Laurel’s current address, and another says “Yanny” and contains her old address, both records will match to the same identity in Verato’s reference database—and therefore they will match to each other.
Organizations can instantly harness the power of Referential Matching with Verato Auto-Steward™, a simple cloud-based plug-in for your EHR or EMPI that automatically finds and resolves its missed matches and duplicate records using Referential Matching technology. Verato Auto-Steward can even automatically resolve the “potential duplicate records” your EHR or EMPI has flagged for manual resolution.
We may never agree on whether the audio recording says “Laurel” or “Yanny,” but we should all be able to agree that using Referential Matching technology to improve patient matching is a no-brainer.