The Age of Data Governance for Patient Matching is Over

Nick Orser

Nick Orser, Product Marketing Manager

The Age of Data Governance for Patient Matching is Over

A recent study published in the AHIMA journal Perspectives in Health Information Management analyzed nearly 400,000 duplicate patient record pairs that came from a range of geographies and organization types. The study, titled “Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields,” examined the differences between duplicate pairs in data fields such as name, birthdate, and SSN to determine what data errors had caused the duplicates to be created in the first place.

The value of the study cannot be understated: it is broad in its scope and comprehensive in its analysis. However, the results of the study could hardly be called surprising. The study found that duplicate records were caused by four problems: (1) a lack of data standardization, (2) frequently changing demographic data, (3) a lack of enough demographic data points in a record, and (4) the entry of default and null values in key identifying fields.

Related Content: Read our whitepaper to learn about the growing demands for patient matching and how they’re redefining the healthcare landscape

The study then went on to suggest that improving data governance would lessen the occurrence of those four problems—and therefore would greatly decrease the creation of duplicate records. However, the age of data governance for the purposes of patient matching is over.

Verato solves the patient matching problem in an innovative way that eliminates the need for providers to clean and govern their data just to find and prevent duplicates. We use the industry’s most accurate “referential matching” engine to match patient records with 98% accuracy by first matching them to the same identity in our reference database, CARBON. CARBON contains a vast array of commercially available identity data for every adult in the US. This data spans over 30 years and includes correct data as well as incorrect data. This means that, as demonstrated in the illustration below, even if Rebecca Jones has gotten married, moved, and now goes by Becky, Verato can still match two of her patient records together.


In fact, using our referential matching engine, we address each of the four problems identified by the report as issues that lead to duplicate records.

Problem: A lack of data standardization
Solution: Our matching algorithms can see through different data formats to match patient records together. This is especially useful when Verato is used to match patient records together between different providers.

Problem: Frequently changing demographic data
Solution: CARBON keeps a historical record of demographic data and receives 60 million monthly updates. This means that past names and current names, past addresses and current addresses, past genders and current genders, etc., are all stored in CARBON and are all used to match patient records together.

Related Content: Read our whitepaper to learn about the growing demands for patient matching and how they’re redefining the healthcare landscape

Problem: A lack of enough demographic data points in a record
Solution: One of the most powerful aspects of the Verato referential matching engine is the ability to match records together even if they have very sparse data. This is because CARBON contains data about every adult in the US—therefore, Verato can know from very limited data that a patient record could only possibly refer to one person. For example, even if a patient record only has a last name, a birthdate, and a street address, Verato could examine every identity in CARBON to know that those three attributes could only refer to one person in the US. This also allows Verato to match a patient record with just those three attributes to a patient record with three entirely different identifying attributes (such as first name, SSN, and zip code).

Problem: The entry of default and null values in key identifying fields
Solution: For the same reasons described in the previous paragraph, Verato can match patient records together even if they have default or null values in them.

We believe we have brought an end to the era of needing to enforce strict data quality and data governance standards just for the sake of matching patient records. We encourage you to download our success story with San Diego Health Connect (a large HIE) to see how we more than doubled the number of matches in their MPI. We also encourage you to give us a call to find out how we can end your data governance headaches and match your patient records together with 98% accuracy.

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