Clinical Text Automatic De-Identification to Support Large Scale Data Reuse and Sharing

在美国，电子健康记录（EHR）系统的采用正在快速增长，而这 增长导致非常大量的患者临床数据以电子格式可用， 巨大的潜力，但同样大的问题是病人的保密性违反。二次利用 临床数据对于实现高质量医疗保健，改善医疗保健管理， 有效的临床研究。NIH希望更大的研究项目以某种方式共享他们的研究数据， 保护研究对象的机密性患者数据的去识别化已被提议作为 解决方案既便于临床数据二次使用又保护患者数据机密性。大多数 在EHR中发现的临床数据表示为叙述性文本临床笔记， 文本是一项乏味且昂贵的手工奋进。基于自然语言的自动化方法 处理已经实施和评估，允许更高的准确性和更快的去 识别比手动方法。 Clinacuity，Inc.提出将文本去识别系统从原型推进到准确的， 适应性强，功能强大的系统，在我们的实施和测试中集成到研究基础设施中 地点（南卡罗来纳州医学大学，查尔斯顿，SC），并准备进行商业化工作。到 为了完成这项任务，我们将着重于以下具体目标和相关目标， 继续准备综合系统的商业化，并进行详细的市场分析， 商业路线图的制定，以及现代媒体传播：1）加强文本的去识别化 系统性能、可扩展性和质量，以生成可随时部署的企业级解决方案; 2） 支持使用结构化数据进行增强的文本去识别（当结构化PII可用时）， 完整的患者记录去识别（即，结合结构化和非结构化数据的记录）。这一目标 还包括实现“单向”伪标识符密码散列以实现安全链接 已经去识别的患者记录; 3）将文本去识别系统与研究数据集成 捕获和管理系统。这包括将去识别系统作为安全的 Web服务，具有基于标准的访问和集成。 这种去识别系统在临床研究和医疗保健方面具有潜在的商业应用前景 设置.它将改善对更丰富，更详细，更准确的临床数据（临床文本）的访问， 临床研究者它将简化研究数据共享（正如NIH资助的大型研究项目所预期的那样） 帮助医疗机构保护患者数据的机密性。还将节省大量时间 该系统提供了一个比手动去识别至少快200-1000倍的过程。

The adoption of Electronic Health Record (EHR) systems is growing at a fast pace in the U.S., and this growth results in very large quantities of patient clinical data becoming available in electronic format with tremendous potential but an equally large concern for patient confidentiality breaches. Secondary use of clinical data is essential to fulfill the potential for high quality healthcare, improved healthcare management, and effective clinical research. NIH expects that larger research projects share their research data in a way that protects the confidentiality of research subjects. De-identification of patient data has been proposed as a solution to both facilitate secondary use of clinical data and protect patient data confidentiality. The majority of clinical data found in the EHR is represented as narrative text clinical notes, and de-identification of clinical text is a tedious and costly manual endeavor. Automated approaches based on Natural Language Processing have been implemented and evaluated, allowing for higher accuracy and much faster de- identification than manual approaches. Clinacuity, Inc. proposes to advance a text de-identification system from a prototype to an accurate, adaptable, and robust system, integrated into the research infrastructure at our implementation and testing site (Medical University of South Carolina, Charleston, SC), and ready for commercialization efforts. To accomplish this undertaking, we will focus on the following specific aims and related objectives, while continuing to prepare the commercialization of the integrated system, with detailed market analysis, commercial roadmap development, and modern media communication: 1) Enhance the text de-identification system performance, scalability, and quality to produce an enterprise-grade solution ready for deployment; 2) Enable use of structured data for enhanced text de-identification (when structured PII is available) and for complete patient records de-identification (i.e., records combining structured and unstructured data). This aim also includes implementing “one-way” pseudo-identifier cryptographic hashing to enable securely linking already de-identified patient records; 3) Integrate the text de-identification system with a research data capture and management system. This includes implementation of the de-identification system as a secure web service, with standards-based access and integration. This de-identification system has potential commercial applications in clinical research and in healthcare settings. It will improve access to richer, more detailed, and more accurate clinical data (in clinical text) for clinical researchers. It will ease research data sharing (as expected for larger NIH-funded research projects) and help healthcare organizations protect patient data confidentiality. Significant time-savings will also be offered, with a process at least 200-1000 times faster than manual de-identification.