Research article

Medical record linkage in health information systems by approximate string matching and clustering

Erik A Sauleau^* , Jean-Philippe Paumier^* and Antoine Buemi^*

Service des études et applications de l'information médicale (SEAIM), Hospital, 87 Ave d'Altkirch, F68051 Mulhouse, France

author email corresponding author email* Contributed equally

BMC Medical Informatics and Decision Making 2005, 5:32doi:10.1186/1472-6947-5-32

Published:	11 October 2005

Abstract

Background

Multiplication of data sources within heterogeneous healthcare information systems always results in redundant information, split among multiple databases. Our objective is to detect exact and approximate duplicates within identity records, in order to attain a better quality of information and to permit cross-linkage among stand-alone and clustered databases. Furthermore, we need to assist human decision making, by computing a value reflecting identity proximity.

Methods

The proposed method is in three steps. The first step is to standardise and to index elementary identity fields, using blocking variables, in order to speed up information analysis. The second is to match similar pair records, relying on a global similarity value taken from the Porter-Jaro-Winkler algorithm. And the third is to create clusters of coherent related records, using graph drawing, agglomerative clustering methods and partitioning methods.

Results

The batch analysis of 300,000 "supposedly" distinct identities isolates 240,000 true unique records, 24,000 duplicates (clusters composed of 2 records) and 3,000 clusters whose size is greater than or equal to 3 records.

Conclusion

Duplicate-free databases, used in conjunction with relevant indexes and similarity values, allow immediate (i.e.: real-time) proximity detection when inserting a new identity.