Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching.

Background: Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient's records may be improperly split across multiple profiles in the MPI.

Objectives: We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE.

Methods: We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE.

Results: We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value < 0.001).

Conclusions: This method may assist HIEs in detecting split patient records.