Quantifying the Impact of Anonymization-Induced Clinical Data Quality Loss: Methodological Quantitative Case Study Using Primary Diagnosis Codes and Hospital Length of Stay

The useful part

The secondary use of electronic health record data requires robust privacy protection. A further gap is that anonymization tools report internal information-loss values but do not signal the downstream distributional and inferential distortions these transformations introduce. This study evaluated the analytical footprint of k-anonymity at k=5, 10, and 15 on 2 core data elements in retrospective hospital research: primary (ICD-10-GM) diagnosis codes, and hospital length of stay (LOS).

How it works

• It aimed to determine and quantify whether anonymization introduces meaningful distortions not captured by the anonymization tool itself, and whether diagnosis-specific LOS patterns remain reproducible...
• Distributional distortion was assessed with the Kolmogorov-Smirnov statistic, quantile shifts, IQR changes, and tail changes.
• Inferential reproducibility was assessed with a 3-level linear mixed model.
• We compared the intraclass correlation coefficient and diagnosis-level effect concordance.
• Concordance was quantified using Spearman ρ and Lin concordance correlation coefficient, both with 95% CIs.

Details worth keeping

Anonymization was performed with the ARX tool. It used record suppression and microaggregation. Categorical fidelity was assessed with the Jaccard coefficient and Cramer.