Traditional Patient Matching is History: How to Link Records Despite Out-of-Date Data

Federal institutions like ONC and private collaboratives like The Sequoia Project have made it a priority to solve the challenge of consolidating and harmonizing patient identities within health systems. ONC has even set a milestone for healthcare organizations to achieve a 0.5% patient record duplicate rate by 2020.¹ But organizations have been addressing this challenge for over a decade, and the average duplicate rate is still 10-20%.² Clearly, current state-of-the-art technologies and approaches are not adequate to hit this milestone.

Verato approaches patient matching in an innovative new way—one that can get you to a 2% patient record duplicate rate in just 2 months. To learn more about our Referential Matching technology, reach out to us.

The Problem: Out-of-Date Data Makes Patient Matching a Challenge

Patient matching engines—like those found in enterprise master patient index (EMPI) technologies—are the foundational technology responsible for linking and deduplicating patient records. These matching engines use patient demographic data as the key to making a match.

But matching engines are only as accurate as the data they are comparing, and patient demographic data is notoriously inaccurate. In fact, 30% of patient demographic data in any given EHR or EMPI is mistyped, misspelled, incomplete, or incorrect. Most importantly, 12% of patient demographic data becomes out-of-date each year³. For example, if Rebecca Jones changes her last name and moves (e.g. after a marriage), her existing patient records suddenly contain out-of-date data, and next time she goes to the hospital, a duplicate record will be created because and the matching engine won’t match her new record to her old record.

The solution to this challenge? Referential Matching technology.

Verato has pioneered a powerful new patient matching technology called “Referential Matching.” Rather than directly comparing the demographic data from two patient records to see if they match, Verato instead matches that demographic data to its comprehensive and continuously-updated reference database of identities. This proprietary 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. It is essentially a pre-built answer key for all patient demographic data.

By matching records to this database instead of to each other, Verato can make matches that conventional patient matching technologies could never make—even patient records containing demographic data that is out-of-date, incomplete, incorrect, or different.

Conclusion

Because of the amount of out-of-date, incorrect, and incomplete patient data in organizations’ databases, typical matching technologies only achieve a 70% match accuracy rate. This leads to an excess of duplicate patient records—and therefore to an incomplete view of patients’ medical histories. Referential Matching technology is a powerful, new, totally different approach that is a quantum leap more accurate than existing technologies.

To learn more about Verato, read why Gartner named us a Cool Vendor for 2016. Then, reach out to us to learn how we can get you to a 2% duplicate record rate in just 2 months.

Sources:

¹Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap. Office of the National Coordinator for Health Information Technology. 2015.
²CHIME National Patient ID Challenge Background. The College of Healthcare Information Management Executives. 2016.
³A Framework for Cross-Organizational Patient Identity Management. The Sequoia Project. November 2015.