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Documentation of Nursing Diagnosis01:10

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Data linkage errors in hospital administrative data when applying a pseudonymisation algorithm to paediatric

Gareth Hagger-Johnson1, Katie Harron2, Tom Fleming3

  • 1Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK Department of Epidemiology and Public Health, UCL, London, UK.

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PubMed
Summary

The Hospital Episode Statistics (HES) ID algorithm has a high rate of missed matches, underestimating patient readmissions. This data linkage error impacts analyses, highlighting the need for algorithm validation against robust standards.

Keywords:
EPIDEMIOLOGYSTATISTICS & RESEARCH METHODSdata linkage

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Area of Science:

  • Health Informatics
  • Data Linkage
  • Patient Record Management

Background:

  • Accurate patient record linkage is crucial for health research and clinical decision-making.
  • The Hospital Episode Statistics (HES) ID algorithm is used for linking patient records in England.
  • Estimating the accuracy of this algorithm is essential for understanding potential biases in health data.

Purpose of the Study:

  • To evaluate the data linkage error rate of the HES ID pseudoanonymisation algorithm.
  • To compare the HES ID algorithm's performance against a trusted reference standard in paediatric intensive care.
  • To assess the impact of linkage errors on readmission rate calculations.

Main Methods:

  • Utilized the Paediatric Intensive Care Audit Network (PICANet) database as a reference standard.
  • Applied the HES ID algorithm to PICANet records for infants and young people (0-19 years).
  • Calculated false-match and missed-match rates, comparing HES ID results to PICANet IDs.

Main Results:

  • The HES ID algorithm demonstrated a low false-match rate (0.2%) but a significant missed-match rate (4.1%).
  • Data linkage errors led to an underestimation of the true readmission rate by 3.8%.
  • Younger patients, males, and those from ethnic minority groups were more prone to false matches and missed matches.

Conclusions:

  • The HES ID algorithm exhibits a high missed-match rate, potentially biasing analyses of patient readmissions.
  • Validation of pseudoanonymisation algorithms against high-quality reference data is critical for reducing linkage errors.
  • Pseudonymisation at the source does not inherently resolve patient identifier errors impacting data linkage.