Hagger-Johnson, G., Harron, K., Goldstein, H., Aldridge, R. & Gilbert, R. (2017). Probabilistic linkage to enhance deterministic algorithms and reduce data linkage errors in hospital administrative data. Journal of Innovation in Health Informatics,24(2), 234-246. United Kingdom: BCS Learning and Development Limited. Retrieved from https://doi.org/10.14236/jhi.v24i2.891
Background The pseudonymisation algorithm used to link together episodes of care belonging to the same patient in England [Hospital Episode Statistics ID (HESID)] has never undergone any formal evaluation to determine the extent of data linkage error. Objective To quantify improvements in linkage accuracy from adding probabilistic linkage to existing deterministic HESID algorithms. Methods Inpatient admissions to National Health Service (NHS) hospitals in England (HES) over 17 years (1998 to 2015) for a sample of patients (born 13th or 28th of months in 1992/1998/2005/2012). We compared the existing deterministic algorithm with one that included an additional probabilistic step, in relation to a reference standard created using enhanced probabilistic matching with additional clinical and demographic information. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year was determined. Results HESID produced a high missed match rate, improving over time (8.6% in 1998 to 0.4% in 2015). Missed matches were more common for ethnic minorities, those living in areas of high socio-economic deprivation, foreign patients and those with ‘no fixed abode’. Estimates of the readmission rate were biased for several patient groups owing to missed matches, which were reduced for nearly all groups. Conclusion Probabilistic linkage of HES reduced missed matches and bias in estimated readmission rates, with clear implications for commissioning, service evaluation and performance monitoring of hospitals. The existing algorithm should be modified to address data linkage error, and a retrospective update of the existing data would address existing linkage errors and their implications.
Institute for Learning Sciences and Teacher Education
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