How Did Patient Matching Become a National Imperative?

Nick Orser

Nick Orser, Product Marketing Manager

How Did Patient Matching Become a National Imperative?

A few years ago, patient matching was a challenge addressed by health information management professionals within the four walls of their hospitals and health systems—typically without any oversight from the executive suite. Today, accurate patient matching has become a national imperative. Organizations like the Office of the National Coordinator for Health Information Technology (ONC) and the College of Healthcare Information Management Executives (CHIME) have sponsored patient matching competitions with prizes as high as $1,000,000. And in early October, five US senators wrote a letter to the Government Accountability Office (GAO) urging it “to consider how ONC could improve patient matching by considering the application of a national patient matching strategy.”

What happened? How did patient matching escalate from being an organization-wide challenge in the domain of health information managers to a national imperative in the domain of US senators?

The answer is that there has been a veritable perfect storm—a confluence of factors that has created an inflection point both in the ability of healthcare organizations to match patients to their records, as well as in the consequences of an inability to do so.

Let’s examine these factors.

First, federal health policy has driven the executive suites of healthcare organizations to focus on strategic initiatives that all fundamentally rely on accurate patient matching. Patient engagement, population health, interoperability, health information exchange, accountable care, precision medicine—foundational to all of these initiatives is the ability to identify and link a patient to all of his or her health records. For example, two organizations cannot exchange health records if they cannot verify that they are exchanging records for the correct person. And a hospital cannot analyze its patient population if it doesn’t know which health records belong to which member of that population.

Related Content: Watch our webinar to learn 7 critical reasons why a National Patient Identifier is not the answer

Second, an explosion of patient data has made patient matching much more challenging for organizations. They are receiving more patient data from more sources, including patient portals, patient engagement applications, telemedicine, personal health records, and Internet of Things (IoT) medical devices—and of course none of this patient data subscribes to the same standards, shares the same formats, or has the same caliber of quality or completeness. Add to this the fact that organizations are frequently merging with each other and are increasingly expected to share more patient data with payers, providers, health information exchanges (HIEs), and state and federal health agencies, and there is just too much data to be matched.

Third, conventional patient matching technologies have reached a mathematical limit to their matching abilities. These conventional technologies (called master patient indexes, or MPIs) rely on “probabilistic” algorithms that were formalized in the 1960s and that—no matter how sophisticated—can never see through errored, incomplete, and out-of-date patient demographic data to determine whether two patient records match. Essentially, these algorithms’ matching accuracy is entirely dependent on the quality of the demographic data they are comparing—and patient demographic data is of notoriously low quality. To compensate, conventional MPIs require manual human intervention for any records they cannot automatically match using their algorithms. This means for an organization with 5,000,000 patients, typically 250,000 to 1,000,000 potential matches must be manually reviewed and resolved by a human. Add to this the fact that conventional MPIs take months to implement and that their algorithms must be re-tuned with each new data source, and they suddenly become a very burdensome “solution” for a problem they are mathematically incapable of solving.

To summarize: Patient matching has become much more important for the executive suite due to new strategic initiatives. And it has become much more difficult due to new data sources and business requirements. But the very technologies we rely on to match patient records have become overwhelmed by these increasing demands and have rapidly begun to fail. And this is simultaneously happening at every healthcare organization in the country.

Where does that leave us?

With decreased quality of care. With drastic implications for patient safety and privacy. With millions of dollars of lost revenue each year to denied claims. And with increased costs to our healthcare system due to systemic inefficiencies, redundant tests and procedures, and unnecessary IT and labor expenditures.

Related Content: Watch our webinar to learn 7 critical reasons why a National Patient Identifier is not the answer

With these consequences in mind, we at Verato set out to solve the patient matching challenge at a national scale. And we built the Verato Universal™ MPI, a cloud-based, nationwide master patient index that any organization—from the smallest clinic to the largest HIE—can simply “plug into” to match all of its patient records.

The Verato Universal MPI is pre-populated with continuously-updated demographic data spanning the entire US population. And it incorporates a powerful new matching technology called Referential Matching that leverages this database as an “answer key” for demographic data to help it match patient records that even world-class probabilistic algorithms can never match—even if those records contain errored, incomplete, and out-of-date patient demographic data.

We created the Verato Universal MPI over four years, investing over 50,000 man-hours of data science and engineering efforts, spending millions of dollars to curate over a billion commercially-available demographic records sourced from three different industries, and inventing a totally new “referential matching” approach to harness this big-data technology.

In essence, the Verato Universal MPI is the most comprehensive and sophisticated MPI in the country. And because it is cloud-based, and because it leverages a “referential matching” architecture, and because it contains demographic data spanning the entire US, it can instantly be deployed as a nationwide patient matching solution.

In fact, the Verato Universal MPI is already being used to match patient records by HIEs that cover over an eighth of the US population. It is such an accurate and game-changing solution that many of these HIEs—which face some of the toughest possible patient matching challenges due to the size of their patient populations and the disparity of their participants—are even using the Verato Universal MPI to automatically match the ambiguous potential matches that their conventional MPIs can never match on their own.

The Verato Universal MPI has already been built to be a nationwide patient matching solution. Because of this, any conversation regarding a nationwide patient matching strategy must seriously discuss whether the Verato Universal MPI is the answer to a problem that is suddenly of critical significance to our national healthcare system.

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