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

基于电子健康记录中高精度信息提取的自动化问题和过敏列表丰富

• 批准号: 9357564
• 负责人: STEPHANE MEYSTRE
• 金额: $ 76.75万
• 依托单位: CLINACUITY,INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357564
• 关键词: Adverse drug event; Cessation of life; Cities; Clinical; Code; Communications Media; Complex; Development; Disease; Electronic Health Record; Ensure; Environment; Excision; Goals; Health Insurance Portability and Accountability Act; Health Personnel; Healthcare; Hospitals; Huntsman Cancer Institute at the University of Utah; Hybrids; Hypersensitivity; Imagery; Incentives; Injury; Inpatients; Institutes; Laboratories; Malignant Neoplasms; Manuals; Medical; Medical Errors; Medication Errors; Methods; Modernization; Natural Language Processing; Outpatients; Output; Patient Care; Patients; Performance; Pharmaceutical Preparations; Phase; Process; Reference Standards; Reporting; Risk; Sampling; Savings; Secure; Site; Sodium Chloride; Speed; Structure; System; Test Result; Testing; Text; Time; Training; United States Centers for Medicare and Medicaid Services; Universities; Utah; Work; base; cancer care; commercial application; commercialization; computerized physician order entry; cost; design; improved; payment; prevent; processing speed; prototype; public health relevance; software development; standard measure; usability; web services

 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 Consequently, these electronic lists are often incomplete, inaccurate, and out of date.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.

 描述（由申请人提供）：医疗错误被认为是许多死亡的原因，即使有些是难以避免的，许多是可以预防的。已经提出了具有决策支持的计算机化医嘱输入系统来降低这种用药错误的风险，但是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中的大部分信息仅在叙述性临床文件中提及。大多数EHR都提供了问题和过敏的电子列表，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，这些电子清单往往不完整、不准确和过时。Clinacuity，Inc.提出 一个新的系统，自动提取结构化和编码的医疗问题和过敏症的临床叙述文本中的电子病历的癌症患者，并建立其可行性。为了将这个新系统从原型发展成为一个准确、适应性强、功能强大的系统，并将其集成到我们的实施和测试站点中使用的商业EHR系统中，（亨斯迈癌症研究所和犹他州大学医院，湖城，犹他州），并准备商业化的努力，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）开发一个先进的可视化界面，用于NLP系统的本地适应，以及3）将NLP系统与商业EHR系统集成。还将开发用于训练和测试信息提取应用程序的大量不同的参考标准，参考标准包括在亨斯迈癌症研究所和犹他州大学医院（犹他州湖城）接受治疗的患者的去识别临床叙述的随机样本，由领域专家注释问题和过敏。商业应用：Clinacuity提出的系统不仅可以帮助医疗保健提供者维护完整和及时的问题和过敏列表，为他们提供患者的有效概述，还可以帮助医疗保健组织达到有意义的使用要求。所提出的系统在住院和门诊设置中具有潜在的商业应用，通过节省时间来提高忙碌的医疗保健提供者的效率，并帮助医疗保健组织展示“有意义的使用”并获得医疗保险和医疗补助服务中心的奖励支付。Clinacuity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，以便于部署。

项目成果

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry.

从中央癌症登记处的非结构化文本字段中自动提取和分类癌症分期。

• 发表时间: 2018
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: AAlAbdulsalam,AbdulrahmanK;Garvin,JenniferH;Redd,Andrew;Carter,MarjorieE;Sweeny,Carol;Meystre,StephaneM
• 通讯作者: Meystre,StephaneM