One of the core challenges for Texas Health and other health systems as they tackle patient matching is making sure that the data going into the MPI system is of high quality. Poor quality data makes it that much more difficult to match patients to all of their records. Parris notes that throughout patients’ healthcare journeys, they are seen by a variety of providers over the course of their lives. So over a span of just a few years, a person can change his or her address, last name, and even gender. “That makes tying a certain person together difficult,” he acknowledges.

For example, Parris offers, there could be a situation in which patient Mary Smith lived in an apartment on the eastern side of Dallas-Fort Worth when she visited Texas Health last, and then a year later, comes visits the health system and is now Mary Joseph living in northern Dallas. “How do I make certain I connect those records, especially when you first come in and give your name, address, and phone number, and those don’t connect [with each other],” he asks. “With Verato, we were able to piece that record together, not just across our venture but across our joint ventures, too. That allowed us to get a better picture of somebody.”

In the past, explains Parris, that person might have existed—especially with the traditional MPI—as two separate people, because there’s very little identifying a person except for the first name, “and you don’t want me to merge every Mary together.” But unless a patient tells the healthcare entity that his or her name changed and the record should be updated to reflect that, there wouldn’t be a way to know it happened.

Of course, when a patient steps foot into a healthcare facility, the last thing he or she is worrying about is back-end data. “They’re worried about their health,” says Parris. “So from the perspective of an MPI, being able to reference a data set that’s outside of Texas Health that has someone’s name, previous names, and previous addresses all pulled into one data set, allows us to pull Mary Smith and Mary Joseph together into the same person. That enables us to connect that person’s identity, allowing us to merge the electronic health record [EHR], and merge it across other systems to continue that conversation,” he says.

Healthcare is littered with “disjointed places where information might be given, but it also coincides with other information we’re bringing in to understand you and your needs better. There is that same need to match information up,” says Parris. He believes that’s where the value of an MPI comes into play, in that it performs referential matching—explained by Verato as matching the demographic data from each record to a continuously-updated database of identities spanning the entire U.S. population, as opposed to directly comparing the demographic data from two patient records—to pull these identifies into the same version of the person.

For Texas Health, the result of these efforts has been the creation of a patient ID for each of its 7 million patients, allowing the organization to get a full, comprehensive view of every patient. “Now we can understand how customers are journeying through our system,” Parris says. For example, he offers, going down the path of needing a knee surgery, does a patient call Texas Health directly, use the system’s website, or book an initial appointment using the MyChart portal? Once the patient has multiple consultations, gets imaging done, and schedules the procedure, there is an ability to look at just that person or thousands of people who have that orthopedic surgeon, he notes.

“So you can look down that chain and say, here are the 1,000 that actually had the surgery, and the 10,000 who did not. Where did they fall off on that journey?” That would not be possible without initially linking that data from all of the health system’s separate information sources. “So we can now connect [data] to get folks back on the journey to improving their health,” Parris emphasizes.

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