Scalable and Robust Clinical Text De-Identification Tools

描述（由申请人提供）：由于缺乏可扩展和健壮的去识别工具，开发信息丰富和快速增长的患者临床文本存储库的全部潜力受到阻碍。临床文本包含受保护的健康信息（PHI），健康保险流通与责任法案（HIPAA）将包含PHI的患者信息的研究使用限制在特定的，有限的，irb批准的项目中。因此，大量的临床文本库仍未被内部研究人员充分利用，甚至更少用于外部合作者的外部传输或由最先进的自然语言处理（NLP）技术进行集中处理。去识别，即从临床文本中移除PHI，是具有挑战性的。尽管它们的可用性已经超过十年，但商业上可用的自动化系统价格昂贵，需要本地定制，并且没有获得广泛的市场渗透。手工方法成本高且不可扩展，尽管它们留下了少量的残余PHI，但仍继续使用。基于最先进的机器学习技术的开源去识别工具可以达到或超过手动方法的水平，但也会受到残余PHI问题的影响。因此，当前的去识别方法也严重限制了临床文本的使用和移动性

DESCRIPTION (provided by applicant): Exploiting the full potential of information rich and rapidly growing repositories of patient clinical text is hampered by the absence of scalable and robust de-identification tools. Clinical text contains protected health information (PHI), and the Health Insurance Portability and Accountability Act (HIPAA) restricts research use of patient information containing PHI to specific, limited, IRB-approved projects. As a result, vast repositories of clinical text remain under-used by internal researchers, and are even less available for external transmission to outside collaborators or for centralized processing by state-of-the-art natural language processing (NLP) technologies. De-identification, which is the removal of PHI from clinical text, is challenging. Despite their availability for over a decade, commercially available automated systems are expensive, require local tailoring, and have not gained widespread market penetration. Manual methods are costly and do not scale, yet continue to be used despite the small amount of residual PHI they leave behind. Open source de-identification tools based on state-of-the-art machine learning technologies can perform at or above the level of manual approaches but also suffer from the residual PHI problem. Current de-identification approaches, then, also severely limit the use and mobility of clinical text while exposing patients to privacy risks. These approaches redact PHI, blacking it out or replacing it with symbols (e.g., "Here for cardiac eval is Mr. **PT_NAME<AA>, a **AGE<60s> yo male with his son Doug ..."). Traditional approaches leave residual PHI ("Doug" in this example) to be easily noticed by readers of the text, as it remains plainly visible among the prominent redactions. We developed and pilot tested an alternative approach we believe addresses the residual PHI problem. Our approach uses the strategy of concealing, rather than trying to eliminate, residual PHI. We call it the "Hiding In Plain Sight" (HIPS) approach. HIPS replaces all known PHI with "surrogate" PHI- fictional names, ages, etc.-that look real but do not refer to any actual patient. A HIPS version of the above text is: "Here for cardiac eval is Mr. Jones, a 64 yo male with his son Doug ..." where the name "Jones" and age "64" are fictional surrogates, but the name "Doug" is residual PHI. To a reader, the surrogates and the residual PHI are indistinguishable. This prevents the reader from detecting the latter, avoiding disclosure. Our preliminary studies suggest that HIPS can reduce the risk of disclosure of residual PHI by a factor of 10. This yields overall performance that far surpasses the performance attainable by manual methods, and is unlikely to be matched, we believe, by additional incremental improvements in PHI tagging models (i.e., efforts to reduce residual PHI). Our pilot studies indicate IRBs would welcome the HIPS approach if it were shown to be effective through rigorous evaluation. To expand usage of clinical text and enhance patient privacy, we propose to formalize rules of effective surrogate generation (Aim 1), extend related de-identification confidence scoring methods (Aim 2), and conduct rigorous efficacy testing of HIPS in diverse institutional settings (Aim 3).