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

Background: The secondary use of electronic health record data requires robust privacy protection. k-Anonymity is widely used to enable data sharing by ensuring that each quasi-identifier combination occurs in at least k records; yet, its analytical impact on clinically meaningful structures remains insufficiently characterized, particularly for the combination of record suppression and microaggregation that arises when a numeric attribute lacks a natural generalization hierarchy. 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. Objective: 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 Methods: We analyzed 719,387 inpatient encounters from University Hospital Mannheim from 2010 to 2024. Anonymization was performed with the ARX tool. It used record suppression and microaggregation. Distributional distortion was assessed with the Kolmogorov-Smirnov Results: ARX masked quasi-identifier cells rather than deleting rows; the proportion of encounters with a masked cell rose from 0.77% (k=5) to 2.62% (k=15), distributed almost uniformly across admission years. Kolmogorov-Smirnov Conclusions: k-Anonymity preserved the ranking of diagnosis-specific LOS effects but altered distributional shape, individual effect magnitudes, and diagnostic vocabulary, none of which was flagged by the internal loss metric. Anonymized data of this type may support ordinal analyses but can mislead analyses requiring faithful variance structure, accurate absolute effects, or complete rare-diagnosis representation. A reporting checklist is provided to document these effects.