Automated Problem and Allergy Lists Enrichment Based on High Accuracy Information Extraction from the Electronic Health Record

 描述(申请人提供)：医疗差错被认为是导致大量死亡的原因，即使有些很难避免，但许多是可以预防的。具有决策支持的计算机化医生医嘱录入系统已被提出以降低这种用药错误的风险，但这些系统依赖于电子健康记录(EHR)中的结构化和编码信息。不幸的是，EHR中可用信息的很大一部分只在叙述性临床文件中提到。大多数EHR都有问题和过敏的电子清单，但它们需要用户手动管理，以添加新问题，修改现有问题，并删除不相关的问题。因此，这些电子清单往往是不完整、不准确和过时的。Clinacity，Inc.建议 提出了一种从癌症患者电子病历的临床叙述文本中自动提取结构化和编码的医疗问题和过敏的新系统，并建立了其可行性。为了将这一新系统从原型推进到准确、适应性和健壮的系统，并集成到我们的实施和测试现场(洪博培癌症研究所和犹他州盐湖城犹他大学医院)使用的商业EHR系统中，并为商业化努力做好准备，我们将致力于以下目标：1)提高NLP系统的性能、可扩展性和质量，2)开发用于NLP系统本地适应的高级可视化界面，以及3)将NLP系统与商业EHR系统集成。还将开发用于培训和测试信息提取应用程序的大型和多样化的参考标准，该参考标准包括亨斯迈癌症研究所和犹他大学医院(犹他州盐湖城)治疗的患者的未识别临床叙述的随机样本，以及领域专家注释的问题和过敏。商业应用：Clinacity提出的系统不仅将帮助医疗保健提供者维护完整和及时的问题和过敏列表，为他们提供对患者的有效概述，而且还将帮助医疗保健组织满足有意义的使用要求。拟议的系统在住院和门诊设置中具有潜在的商业应用，通过节省时间提高忙碌的医疗保健提供者的效率，并帮助医疗保健组织演示“有意义的使用”并获得医疗保险和医疗补助服务奖励付款中心。Clinacity将进一步扩大该系统及其输出的商业潜力，采用模块化设计原则，允许独立使用每个模块，并增强其本地适应性，使部署更容易。

 DESCRIPTION (provided by applicant): Medical errors are recognized as the cause of numerous deaths, and even if some are difficult to avoid, many are preventable. Computerized physician order-entry systems with decision support have been proposed to reduce this risk of medication errors, but these systems rely on structured and coded information in the electronic health record (EHR). Unfortunately, a substantial proportion of the information available in the EHR is only mentioned in narrative clinical documents. Electronic lists of problems and allergies are available in most EHRs, but they require manual management by their users, to add new problems, modify existing ones, and the removal of the ones that are irrelevant. Consequently, these electronic lists are often incomplete, inaccurate, and out of date. Clinacuity, Inc. proposed a new system to automatically extract structured and coded medical problems and allergies from clinical narrative text in the EHR of patients suffering from cancer, and established its feasibility. To advance this new system from a prototype to an accurate, adaptable, and robust system, integrated into the commercial EHR system used in our implementation and testing site (Huntsman Cancer Institute and University of Utah Hospital, Salt Lake City, Utah), and ready for commercialization efforts, we will work on the following aims: 1) enhance the NLP system performance, scalability, and quality, 2) develop an advanced visualization interface for local adaptation of the NLP system, and 3) integrate the NLP system with a commercial EHR system. A large and varied reference standard for training and testing the information extraction application will also be developed, a reference standard including a random sample of de-identified clinical narratives from patients treated at the Huntsman Cancer Institute and at the University of Utah Hospital (Salt Lake City, Utah), with problems and allergies annotated by domain experts. Commercial application: The system Clinacuity proposes will not only help healthcare providers maintain complete and timely lists of problems and allergies, providing them with an efficient overview of a patient, but also help healthcare organizations attain meaningful use requirements. The proposed system has potential commercial applications in inpatient and outpatient settings, increasing the efficiency of busy healthcare providers by saving time, and aiding healthcare organizations in demonstrating "meaningful use" and obtaining Centers for Medicare & Medicaid Services incentive payments. Clinacuity will further extend the commercial potential of the system and its output, using modular design principles allowing utilization of each module independently, and enhancing its local adaptability for easier deployment.