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

基本信息

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
项目类别：
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

项目摘要

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 Consequently, these electronic lists are often incomplete, inaccurate, and out of date.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（适用提供）：医疗错误被认为是许多死亡的原因，即使有些人难以避免，许多人也可以预防。已经提出了具有决策支持的计算机化物理订单进入系统，以减少这种药物错误的风险，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，仅在叙事临床文件中提到了EHR中可用的很大一部分信息。大多数EHR中都可以使用问题和过敏的电子列表，但是它们需要用户手动管理，以添加新问题，修改现有问题，并删除无关紧要的问题。因此，这些电子列表通常不完整，不准确且过时。 Clinacuity，Inc。提出一种新的系统，可自动从患有癌症患者的EHR中自动从临床叙事文本中提取结构化和编码的医学问题以及过敏，并确定其可行性。将这个新系统从原型推广到准确，适应性和健壮的系统，并集成到我们的实施和测试网站中使用的商业EHR系统（Huntsman Cancer Institute and Utah University of Hospital，犹他州盐湖城），并准备好进行商业化工作，我们将在以下目的上进行以下目的：1）增强NLP系统性能，范围范围，并提高NLP系统的范围，并进行稳定性，2）范围，2） 3）将NLP系统与商业EHR系统集成在一起。还将开发出大量且多样化的培训和测试参考标准，这是一个参考标准，其中包括从亨斯曼癌症研究所和犹他大学医院（犹他州盐湖城）接受治疗的患者的随机取消识别临床叙事样本，以及域专家注释的问题和过敏。商业应用：系统倾向提案不仅将帮助医疗保健提供者保持完整，及时的问题和过敏，从而有效地概述了患者的概述，还可以帮助医疗机构达到有意义的使用要求。拟议的系统在住院和门诊环境中具有潜在的商业应用，通过节省时间来提高繁忙的医疗保健提供者的效率，并帮助医疗保健组织证明“有意义的使用”并为医疗保险和医疗补助服务激励付款获得中心。 Clinacuity将使用模块化设计原理进一步扩展系统及其输出的商业潜力，允许独立利用每个模块，并增强其本地适应性，以更轻松地部署。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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。

DOI：
发表时间：
2018
期刊：
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
影响因子：
0
作者：
AAlAbdulsalam,AbdulrahmanK;Garvin,JenniferH;Redd,Andrew;Carter,MarjorieE;Sweeny,Carol;Meystre,StephaneM
通讯作者：
Meystre,StephaneM