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: 8590856
• 负责人: STEPHANE MEYSTRE
• 金额: $ 27.7万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8590856
• 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 Consequently, these electronic lists are often incomplete, inaccurate, and out of date.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）：医疗错误被认为是造成大量死亡的原因，即使有些错误难以避免，但许多错误是可以预防的。具有决策支持功能的计算机化医生医嘱输入系统已被提出来降低用药错误的风险，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，电子病历中可用的大部分信息仅在叙述性临床文件中提及。大多数电子病历中都提供问题和过敏的电子列表，但它们需要用户手动管理，添加新问题、修改现有问题以及删除不相关的问题。因此，这些电子列表通常不完整、不准确且过时。 Clinacuity, Inc. 提议 开发一种新系统，从癌症患者 EHR 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。为了确定这样一个系统的优点和可行性，我们将致力于以下目标：1）创建一个用于培训和测试信息提取应用程序的参考标准，该参考标准包括来自亨斯迈癌症研究所癌症诊所（犹他州盐湖城）治疗的患者的去识别化临床叙述的随机样本，并由领域专家注释问题和过敏； 2）开发一个自动提取医疗问题和过敏的原型，实施一种新颖的逐步混合方法，首先最大限度地提高灵敏度，同时提高阳性预测值； 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：Clinacuity 提出的系统不仅可以帮助医疗保健提供者维护完整、及时的问题和过敏清单，为他们提供对患者的有效概述，而且还可以帮助医疗保健组织实现有意义的使用要求。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付款。 Clinacuity将进一步扩展该系统的商业潜力并输出，采用模块化设计原则，允许独立利用每个模块，并增强其本地适应性以方便部署。