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, in

SUBMITTER: Hagger-Johnson G 

PROVIDER: S-EPMC4545352 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature