An efficient record linkage scheme using graphical analysis for identifier error detection.
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...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 11; no. 1; pp. 7 - 8 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104834175&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104834175 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2011 vid: 11 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 104834175 NLM21284874 2010966395 10.1186/1472-6947-11-7 NLM21284874 PMC3039555 104834175 ppf: 7 ppct: 1 formats: tig: atl: An efficient record linkage scheme using graphical analysis for identifier error detection. aug: au: Finney, John M Walker, A Sarah Peto, Tim Ea Wyllie, David H Peto, Tim E A affil: NIHR Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK. david.wyllie@ndm.ox.ac.uk. sug: subj: Algorithms Electronic Health Records Medical Record Linkage Methods Cluster Analysis Logic Great Britain Human Logistic Regression Multivariate Analysis ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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