Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields

Abstract

Patient identification matching problems are a major contributor to data integrity issues within electronic health records. These issues impede the improvement of healthcare quality through health information exchange and care coordination, and contribute to deaths resulting from medical errors. Despite best practices in the area of patient access and medical record management to avoid duplicating patient records, duplicate records continue to be a significant problem in healthcare. This study examined the underlying causes of duplicate records using a multisite data set of 398,939 patient records with confirmed duplicates and analyzed multiple reasons for data discrepancies between those record matches. The field that had the greatest proportion of mismatches (nondefault values) was the middle name, accounting for 58.30 percent of mismatches. The Social Security number was the second most frequent mismatch, occurring in 53.54 percent of the duplicate pairs. The majority of the mismatches in the name fields were the result of misspellings (53.14 percent in first name and 33.62 percent in last name) or swapped last name/first name, first name/middle name, or last name/middle name pairs. The use of more sophisticated technologies is critical to improving patient matching. However, no amount of advanced technology or increased data capture will completely eliminate human errors. Thus, the establishment of policies and procedures (such as standard naming conventions or search routines) for front-end and back-end staff to follow is foundational for the overall data integrity process. Training staff on standard policies and procedures will result in fewer duplicates created on the front end and more accurate duplicate record matching and merging on the back end. Furthermore, monitoring, analyzing trends, and identifying errors that occur are proactive ways to identify data integrity issues.

Keywords: data discrepancy; data integrity; data quality; enterprise master patient index (EMPI); health information exchange (HIE); master patient index (MPI); patient identity; patient matching.

Figure 1

Example of Character Matching Method Used to Compare Name Fields of Duplicate Records Note: Green arrows indicate position matches between the characters. Red arrows indicate discrepancies.

Figure 2

Percentage of Duplicate Pairs with a Mismatch in the SSN, MN, LN, FN, DOB, or Gender Field (n = 398,939) Abbreviations: DOB = date of birth, FN = first name, MN = middle name, LN = last name, SSN = Social Security number.