Identifying Possible False Matches in Anonymized Hospital Administrative Data without Patient Identifiers

Abstract

OBJECTIVE: To identify data linkage errors in the form of possible false matches, where two patients appear to share the same unique identification number. DATA SOURCE: Hospital Episode Statistics (HES) in England, United Kingdom. STUDY DESIGN: Data on births and re-admissions for infants (April 1, 2011 to March 31, 2012; age 0-1 year) and adolescents (April 1, 2004 to March 31, 2011; age 10-19 years). DATA COLLECTION/EXTRACTION METHODS: Hospital records pseudo-anonymized using an algorithm designed to link multiple records belonging to the same person. Six implausible clinical scenarios were considered possible false matches: multiple births sharing HESID, re-admission after death, two birth episodes sharing HESID, simultaneous admission at different hospitals, infant episodes coded as deliveries, and adolescent episodes coded as births. PRINCIPAL FINDINGS: Among 507,778 infants, possible false matches were relatively rare (n = 433, 0.1 percent). The most common scenario (simultaneous admission at two hospitals, n = 324) was more likely for infants with missing data, those born preterm, and for Asian infants. Among adolescents, this scenario (n = 320) was more common for males, younger patients, the Mixed ethnic group, and those re-admitted more frequently. CONCLUSIONS: Researchers can identify clinically implausible scenarios and patients affected, at the data cleaning stage, to mitigate the impact of possible linkage errors.

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