Evaluating GPT models for clinical note de-identification

Abstract

The rapid digitalization of healthcare has created a pressing need for solutions that manage clinical data securely while ensuring patient privacy. This study evaluates the capabilities of GPT-3.5 and GPT-4 models in de-identifying clinical notes and generating synthetic data, using API access and zero-shot prompt engineering to optimize computational efficiency. Results show that GPT-4 significantly outperformed GPT-3.5, achieving a precision of 0.9925, a recall of 0.8318, an F1 score of 0.8973, and an accuracy of 0.9911. These results demonstrate GPT-4's potential as a powerful tool for safeguarding patient privacy while increasing the availability of clinical data for research. This work sets a benchmark for balancing data utility and privacy in healthcare data management.

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