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. Yet 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 EHR 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—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 can 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 the reference database—and therefore they will match to each other.
Organizations can instantly harness the power of Referential Matching by using Verato Auto-Steward™, a simple cloud-based plug-in that integrates with EHRs and enterprise master patient index (EMPI) technologies to improve their matching. In fact, Verato Auto-Steward can automatically resolve 50-75% of the potential duplicates that your EHR or EMPI has flagged as tasks for data stewards or HIM staff to manually resolve.
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.