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Probabilistic linkage to enhance deterministic algorithms and reduce data linkage errors in hospital administrative

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Area of Science:

  • Health Informatics
  • Data Linkage
  • Patient Record Systems

Background:

  • The pseudonymisation algorithm for linking patient care episodes in England (HESID) lacked formal evaluation for data linkage errors.
  • Existing deterministic algorithms may not accurately link all patient records.

Purpose of the Study:

  • To evaluate improvements in data linkage accuracy by incorporating probabilistic linkage into the existing deterministic HESID algorithms.
  • To quantify the reduction in data linkage errors and their impact on patient data.

Main Methods:

  • Utilized 17 years of inpatient hospital admission data (1998-2015) from England's Hospital Episode Statistics (HES).
  • Compared the existing deterministic HESID algorithm with a hybrid approach including an additional probabilistic step.
  • Established a reference standard using enhanced probabilistic matching with additional clinical and demographic data.

Main Results:

  • The HESID algorithm demonstrated a high missed match rate, decreasing from 8.6% in 1998 to 0.4% in 2015.
  • Missed matches disproportionately affected ethnic minorities, deprived populations, foreign patients, and those without a fixed abode.
  • Probabilistic linkage reduced bias in hospital readmission rate estimates for most patient groups.

Conclusions:

  • Probabilistic linkage enhances HES data accuracy by reducing missed matches and correcting bias in readmission rate estimates.
  • Modifying the current HESID algorithm is recommended to address data linkage errors.
  • A retrospective update of existing data is necessary to rectify past linkage errors and their consequences.