Automated Dynamic Lists for Efficient Electronic Health Record Management

用于高效电子健康记录管理的自动化动态列表

• 批准号: 8830154
• 负责人: STEPHANE MEYSTRE
• 金额: $ 11.97万
• 依托单位: CLINACUITY,INC.
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-05 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8830154
• 关键词: Cancer Patient; Caring; Cessation of life; Cities; Clinic; Clinical; Code; Complex; Disease; Electronic Health Record; Electronics; Ensure; Event; Excision; Goals; Health Personnel; Healthcare; Hybrids; Hypersensitivity; Incentives; Inpatients; Institutes; Laboratories; Malignant Neoplasms; Manuals; Measurement; Measures; Medical; Medical Errors; Medication Errors; Methods; Metric; Natural Language Processing; Outpatients; Output; Patients; Pharmaceutical Preparations; Predictive Value; Reference Standards; Reporting; Risk; Sampling; Sodium Chloride; Structure; System; Test Result; Testing; Text; Time; Training; United States Centers for Medicare and Medicaid Services; Utah; Validation; Work; base; commercial application; computerized physician order entry; design; improved; novel; payment; prevent; prototype; public health relevance

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 Consequently, these electronic lists are often incomplete, inaccurate, and out of date.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. proposes to develop 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. To establish the merit and feasibility of such a system, we will work on the following objectives: 1) create a reference standard for training and testing the information extraction application, a reference standard including a random sample of de-identified clinical narratives from patients treated at the Huntsman Cancer Institute Cancer Clinics (Salt Lake City, Utah), with problems and allergies annotated by domain experts; 2) develop a prototype to automatically extract medical problems and allergies, implementing a novel stepwise hybrid approach to maximize sensitivity first, and also enhance positive predictive value; and 3) test the prototype with the aforementioned reference standard, using a cross-validation approach for training and testing. 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 and Medicaid Services incentive payments. Clinacuity will further extend the commercial potential of the system and it is 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医师订购系统，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 为了开发一种新系统，以自动从EHR中自动提取结构化和编码的医学问题以及患有癌症患者的EHR中的过敏。为了建立此类系统的绩效和可行性，我们将致力于以下目标：1）创建培训和测试信息提取应用的参考标准，该标准是参考标准，其中包括从亨斯曼癌症研究所癌症诊所（盐湖城）（犹他州盐湖城）（犹他州盐湖城）的患者进行识别的临床叙事样本，并由Dimain Perferss提供了问题和过敏; 2）开发一个原型，以自动提取医学问题和过敏，实施一种新型的逐步混合方法，以最大程度地提高灵敏度，并增强阳性预测价值； 3）使用上述参考标准测试原型，使用交叉验证方法进行训练和测试。商业应用：该系统倾向提出的不仅将帮助医疗保健提供者保持完整，及时的问题和过敏列表，从而为他们提供了对患者的有效概述，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支付获得中心。 Clinacuity将进一步扩展系统的商业潜力，并使用模块化设计原理，允许独立利用每个模块，并增强其局部适应性以更轻松地部署。