Abstract
Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and research. Under HIPAA Safe Harbor, these notes are de-identified to protect patient privacy. However, Safe Harbor was designed for an era of categorical tabular data, focusing on the removal of explicit identifiers while ignoring the latent information found in correlations between identity and quasi-identifiers, which can be captured by modern LLMs. We first formalize these correlations using a causal graph, then validate it empirically through individual re-identification of patients from scrubbed notes. The paradox of de-identification is further shown through a diagnosis ablation: even when all other information is removed, the model can predict the patient's neighborhood based on diagnosis alone. This position paper raises the question of how we can act as a community to uphold patient-provider trust when de-identification is inherently imperfect. We aim to raise awareness and discuss actionable recommendations.
Abstract (translated)
隐私是一项人权，它维系了患者与提供者之间的信任。临床笔记记录了患者的私人脆弱性和个体性，这些信息用于护理协调和研究。根据《健康保险流通与责任法案》（HIPAA）的安全港规定，这些笔记会被去标识化以保护患者隐私。然而，安全港规则是为处理类别化的表格数据而设计的，在这种情况下，主要关注的是去除显式识别符，却忽视了身份识别符和准识别符之间相关性所蕴含的信息，后者可以通过现代大型语言模型（LLM）捕捉到。 我们首先通过因果图形式化这些关联，并通过从清理后的笔记中单独重新识别患者来实证验证。去标识化的悖论还通过诊断消除进一步展现出来：即使移除了所有其他信息，仅凭诊断也能让模型预测患者的居住区域。这篇立场论文提出了一个问题——当去标识化本质上是不完美的时候，我们作为社区应该如何行动以维护患者与提供者之间的信任。我们的目标是提高意识，并讨论可操作的建议。
URL
https://arxiv.org/abs/2602.08997
