Abstract
BackgroundThe 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...
Highlights
MethodsData linkage algorithms are widely used to combine records that belong to the same individual
We found that false matches occurred primarily because of disagreement on National Health Service (NHS) number and local ID, or because the record pairs may belong to multiple births
To evaluate the impact of data linkage error on results, we modelled the risk of hospital readmission for patients within one year
Summary
MethodsData linkage algorithms are widely used to combine records that belong to the same individual. 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. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year was determined. 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.
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