A recent study published in the AHIMA journal Perspectives in Health Information Management analyzed nearly 400,000 duplicate patient records 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.
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 has pioneered a powerful new patient matching technology called “Referential Matching” that eliminates the need for providers to clean and govern their data just to find and prevent duplicates.
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conventional patient matching technologies could never make—even patient records containing demographic data that is out-of-date, incomplete, incorrect, or different.
In fact, using our Referential Matching technology, we can overcome each of the four problems identified by the report as issues that lead to duplicate records.
Problem: A lack of data standardization
Solution: Referential Matching technology can see through inconsistent data formats to match patient records together.
Problem: Frequently changing demographic data
Solution: Our reference database contains a 30-year history of demographic data for each identity and receives 60 million monthly data updates. This means that past names and current names, past addresses and current addresses, past genders and current genders, etc., are all stored in our reference database, and are all used to help match patient records together.
Problem: A lack of enough demographic data points in a record
Solution: One of the most powerful aspects of the our Referential Matching technology is the ability to match records together even if they have very sparse data. Referential Matching technology can know from very limited data that a patient record could only possibly refer to one person in the US. For example, even if a patient record only has a name and a birthdate, Verato’s Referential Matching technology can compare this data to Verato’s reference database to ensure that only one person in the US has ever had this name and birthdate combination—meaning the patient record must belong to that person.
Problem: The entry of default and null values in key identifying fields
Solution: For the same reasons described in the previous paragraph about matching records with sparse demographic data, 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.