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Identifying Possible False Matches in Anonymized Hospital Administrative Data without Patient Identifiers
Authors:Gareth Hagger-Johnson  Katie Harron  Arturo Gonzalez-Izquierdo  Mario Cortina-Borja  Nirupa Dattani  Berit Muller-Pebody  Roger Parslow  Ruth Gilbert  Harvey Goldstein
Affiliation:1. Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK;2. Institute of Health Informatics, Faculty of Pop Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK;3. Institute of Child Health, Faculty of Pop Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK;4. Centre for Maternal and Child Health Research, School of Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK;5. City University London, UK;6. Public Health England, London, UK;7. University of Leeds, Leeds, UK;8. University of Bristol, Bristol, UK
Abstract:
ObjectiveTo identify data linkage errors in the form of possible false matches, where two patients appear to share the same unique identification number.ConclusionsResearchers can identify clinically implausible scenarios and patients affected, at the data cleaning stage, to mitigate the impact of possible linkage errors.
Keywords:Computerized patient medical records   data linkage   data quality   medical errors
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