Grouping methods for ongoing record linkage

The grouping of record-pairs to determine which records belong to the same individual is an important part of the record linkage process. While a merge grouping approach is commonly used, other methods may be more appropriate when linking to a repository of previously linked data. In this paper, we applied a number of grouping strategies to three large scale hospital datasets (comprising around 27 million records), each with a known truth set. These datasets were linked against a created ‘repository’ whose quality was varied. Experimental results show that alternate grouping methods can yield very large benefits in linkage quality, especially when the quality of the underlying repository is high. Best link methods can remove between 25-90% of matching errors, depending on the characteristics of the underlying datasets.