Problem Solved for Patient Matching Data

AHIMA  recently published a study  in the journal Perspectives in Health Information Management analyzing  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 focusing on what data errors had caused the duplicates to be created in the first place.

The study is broad-based and comprehensive in its analysis. The results, however, are hardly 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 conclusion was to suggest that improving data governance would lessen the occurrence of those four problems—and therefore would greatly decrease the creation of duplicate records.  Although this statement is certainly true, it is far easier said than done.

Privasent 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. The Privasent solution uses a patented process combining biometric technologies and smart cards to register and authenticate patient identities, displacing the error-ridden and privacy-compromising demographics-based approaches currently deployed for patient identification.

Using our absolute patient identity methodology, 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: Once a patient is enrolled in our system, they are authenticated biometrically using a hand scan. Using biometrics eliminates most of the problems caused by lack of data standardization between systems. Privasent can identify medical record numbers from multiple systems and verify the patient identify between them.

Problem: Frequently changing demographic data
Solution: Using a biometric identifier and the medical record number from the healthcare provider also eliminates the need to depend solely on changing demographic data.

Problem: A lack of enough demographic data points in a record
Solution: The biometric identifier adds an additional level of identification and supplements any missing demographics.

Problem: The entry of default and null values in key identifying fields
Solution: For the same reasons described in the previous paragraphs, Privasent 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. Privasent eliminates these errors by capturing a unique biometric identity using palm-vein scanning technology coupled with smart cards for multi-factor identification.  An encrypted identity is associate with each of the patient EHRs each time the patient authenticates, with Privasent providing a foundation for linking disparate records among providers information systems as well as among providers in and HIE.

To learn more about preventing duplicate records, check out our webpage at


About the Author:

Debra Fryar is a blogger for Privasent and advocate for proper patient identification in a new age of healthcare tech.

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