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

临床文本自动去识别化，支持大规模数据重用和共享

• 批准号: 9908962
• 负责人: STEPHANE MEYSTRE
• 金额: $ 75.93万
• 依托单位: CLINACUITY,INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9908962
• 关键词: Adoption; Clinical; Clinical Data; Clinical Research; Code; Communications Media; Confidentiality of Patient Information; Data; Development; Electronic Health Record; Enrollment; Environment; Fast Healthcare Interoperability Resources; Funding; Growth; Health Insurance Portability and Accountability Act; Improve Access; Link; Manuals; Medical; Modernization; National Institute of General Medical Sciences; Natural Language Processing; Patient Care; Patient Data Privacy; Patients; Performance; Personally Identifiable Information; Phase; Privacy; Process; Records; Reference Standards; Research; Research Infrastructure; Research Personnel; Research Project Grants; Research Subjects; Risk; Savings; Secure; Site; South Carolina; Speed; Structure; System; Test Result; Testing; Text; Time; Training; Trust; United States National Institutes of Health; Universities; Visualization; base; commercial application; commercialization; cost; cryptography; data reuse; data sharing; health care quality; health care service organization; health care settings; health management; improved; large scale data; prototype; software development; standard measure; structured data; systems research; unstructured data; web services

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.

电子健康记录（EHR）系统的采用在美国快速增长，这 增长导致大量患者临床数据以电子格式获得 巨大的潜力，但对患者置信度的关注也同样重要。次要使用 临床数据对于满足高质量医疗保健，改善医疗保健管理的潜力至关重要， 和有效的临床研究。 NIH期望较大的研究项目以某种方式共享他们的研究数据 这保护了研究主题的机密性。已经提出了将患者数据的识别作为一个 解决临床数据的紧急次要使用并保护患者数据机密性。大多数 EHR中发现的临床数据表示为叙事文本临床注释，并取消识别临床 文字是一项繁琐且昂贵的手动努力。基于自然语言的自动方法 处理和评估已经实施和评估，可以提高准确性和更快的速度 识别比手动方法。 Clinacuity，Inc。提议将文本去识别系统从原型推进到准确的， 在我们的实施和测试中，适应性和健壮的系统集成到研究基础架构中 站点（南卡罗来纳州医科大学，南卡罗来纳州查尔斯顿），并准备进行商业化工作。到 完成这项工作，我们将专注于以下特定目标和相关对象，而 继续通过详细的市场分析来准备集成系统的商业化， 商业路线图开发和现代媒体通讯：1）增强文本识别 系统性能，可伸缩性和质量生产企业级解决方案准备部署； 2） 启用结构化数据以增强文本去识别（可用结构化PII时），并且用于 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）。这个目标 还包括实施“单向”伪识别仪加密散列以启用安全链接 已经被识别的患者记录； 3）将文本去识别系统与研究数据集成 捕获和管理系统。这包括实现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中具有潜在的商业应用 设置。它将改善获得更丰富，更详细和更准确的临床数据（在临床文本中）的获取 临床研究人员。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） 并帮助医疗组织保护患者数据机密性。大量的时空也将是 提供的过程至少要比手动去识别快200-1000倍。