title: An efficient record linkage scheme using graphical analysis for identifier error detection.
abstract: BACKGROUND
  Integration of information on individuals (record linkage) is a key problem in healthcare delivery, epidemiology, and "business intelligence" applications. It is now common to be required to link very large numbers of records, often containing various combinations of theoretically unique identifiers, such as NHS numbers, which are both incomplete and error-prone.
  
  
  METHODS
  We describe a two-step record linkage algorithm in which identifiers with high cardinality are identified or generated, and used to perform an initial exact match based linkage. Subsequently, the resulting clusters are studied and, if appropriate, partitioned using a graph based algorithm detecting erroneous identifiers.
  
  
  RESULTS
  The system was used to cluster over 250 million health records from five data sources within a large UK hospital group. Linkage, which was completed in about 30 minutes, yielded 3.6 million clusters of which about 99.8% contain, with high likelihood, records from one patient. Although computationally efficient, the algorithm's requirement for exact matching of at least one identifier of each record to another for cluster formation may be a limitation in some databases containing records of low identifier quality.
  
  
  CONCLUSIONS
  The technique described offers a simple, fast and highly efficient two-step method for large scale initial linkage for records commonly found in the UK's National Health Service.
authors.affiliation: NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK.
authors.forename: John M
authors.initials: JM
authors.lastname: Finney
authors.forename: A Sarah
authors.initials: AS
authors.lastname: Walker
authors.forename: Tim E A
authors.initials: TE
authors.lastname: Peto
authors.forename: David H
authors.initials: DH
authors.lastname: Wyllie
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057286:Electronic Health Records; D017143:Fuzzy Logic; D006801:Humans; D016015:Logistic Models; D008498:Medical Record Linkage / Q000379:methods*; D015999:Multivariate Analysis; D006113:United Kingdom