用非识别的临床自由文本数据用于健康研究的患者重新识别风险是什么?
Elizabeth Ford1, Simon Pillinger2, Robert Stewart3,4
1Brighton and Sussex Medical School, Brighton, UK.
概括
通过使用大型语言模型,可以安全地将非识别的临床自由文本用于健康研究. 在安全的环境中存储和访问这些被删除的数据可以最大限度地降低患者重新识别的风险,从而使更多的研究成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 自然语言处理自然语言处理.
背景情况:
- 临床信息往往是患者记录中的非结构化的自由文本,限制了其在健康研究中的使用.
- 大型语言模型 (LLM) 的进步使自然语言能够在规模上进行处理.
- 对患者隐私和重新识别风险的担忧阻碍了在研究中使用临床自由文本.
研究的目的:
- 从自由文本中审查和讨论评估患者重新识别风险的要素.
- 确定自动化去识别流程的最佳实践.
- 提出一个模型来概念化和评估健康数据中的重新识别风险.
主要方法:
- 关于重新识别方法和数据保护策略的综合文献审查.
- 分析自动化取消身份识别的准确性和有效性.
- 检查公众对自由文本数据使用的看法和伦理考虑.
- 在英国的"五个保险箱"框架中,成功管理自由文本数据的案例研究.
主要成果:
- 自动化去识别流程正在得到改进,现有最佳实践.
- 以前的重新识别方法对受保护的健康数据取得了有限的成功.
- "五个安全"框架为安全环境中的健康数据提供了强大的保护.
- 公众的观点表明,人们愿意在适当的保障下共享研究数据.
结论:
- 当被非识别并存储在安全数据环境中时,从临床自由文本中重新识别患者的风险非常低.
- 实施强大的非识别和安全数据环境,使得临床自由文本在健康研究中的安全使用成为可能.
- 通过日常存储和提供对非识别临床文本数据的访问,可以促进更多的健康研究.
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