Privacy Protection for Chinese Electronic Medical Records Using Large Language Models: Effectiveness Evaluation and Application of LLM Models in Medical Data Tasks
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Background
The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional approaches have predominantly relied on rule-based protocols and data encryption systems, which typically require substantial involvement of IT professionals for implementation. Recent advancements in Large Language Models (LLMs) have introduced novel approaches for electronic medical records (EMRs) privacy protection, simultaneously enabling clinical practitioners to utilize these tools for specific data tasks.
Objectives
This study aims to leverage LLMs through a no-code framework to achieve structured processing of patient privacy data in Chinese EMRs and formulate privacy policies, while evaluating the practical efficacy of LLMs.
Methods
This study employs a disease-specific data subset from Peking Union Medical College Hospital (PUMCH), comprising data from approximately 160,000 patients, using a prompt engineering approach to enable LLMs to perform sensitive information annotation in lengthy EMR narratives. Simultaneously, it automates the classification of privacy-level for identified sensitive data and develops targeted protection strategies based on risk tiers, thereby mitigating non-essential exposure of patient privacy during data sharing. The research utilizes the Qwen model, with its entire workflow being exclusively driven by medical natural language prompts and self-evolving knowledge bases, requiring no supplementary programming or code development. These strategies were validated using the hospital’s test text dataset, with primary evaluation metrics focusing on precision rates (including accuracy of information extraction and privacy-level classification) and recall rate assessments for critical sensitive data categories.
Results
Utilizing 4 million text entries from PUMCH, we conducted sampled data observation and performed privacy annotation via LLM prompts across seven categories: names, addresses, contact details, national ID numbers, hospital names, sexually transmitted disease (STD) information, and pregnancy-related patient data. Through iterative prompt refinement via error analysis, optimal performance was achieved on the test set, demonstrating an average precision of 97% and recall of 95% across these seven entity types. Furthermore, sensitivity tier classification was implemented for three high-risk categories: addresses, STD information, and pregnancy-related data, attaining average precision of 95% and recall of 90% in sensitivity-level determination.
Discussion
We propose a novel codeless privacy protection framework leveraging LLMs, enabling intelligent anonymization of medical data through natural language interaction. This solution employs a three-tiered hierarchical protection mechanism that dynamically adapts privacy strategies to clinical scenario requirements, ensuring data security while maximizing data utility.
