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Evaluating Prompt Strategies for LLM-Based De-Identification of German Discharge Letters: A Feasibility Study Using
Florin Dominik Teschner1, Hung Manh Nguyen1, Martin Sedlmayr1
1Institute for Medical Informatics and Biometry, Faculty of Medicine Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Introduction:
Clinical text de-identification is essential for secondary healthcare data use but remains challenging due to heterogeneous documentation.
Methods:
We evaluated LLM-based de-identification of synthetic German discharge letters (GraSCCo) using four prompting strategies with GPT-4o and an additional GPT-OSS comparison. Performance was assessed via precision, recall, F1-score, false positives, and relative text reduction.
Results:
The baseline setup was not suitable due to excessive text loss. F1-scores reached 0.81 (session-isolated), 0.79 (structured-input), 0.93 (optimized prompt, GPT-4o), and 0.90 (GPT-OSS).
Discussion:
Results indicate that prompt refinement has a stronger impact on de-identification quality than structural preprocessing. LLM-based de-identification is feasible but requires careful prompt design and validation on real-world data.
