Meaningful Drug Interaction Alerts

有意义的药物相互作用警报

• 批准号: 9883780
• 负责人: DANIEL C MALONE
• 金额: $ 37.75万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883780
• 关键词: Meaningful; Drug; Interaction; Alerts

Meaningful Drug Interaction Alerts Project Summary: Clinical decision support (CDS) for electronic health records (EHR) and prescribing systems has been promoted to improve patient outcomes. One type of CDS are drug-drug interaction (DDI) alerts. The Office of the National Coordinator for Health IT meaningful use criteria includes the implementation of DDI detection and warnings to physicians and other healthcare professionals. Nearly all healthcare organizations rely on DDI alerts generated from commercial drug knowledge databases. Warnings are currently generated using simple drug combination rules, ignoring drug attributes and the wealth of information available in the EHR that could make the warnings specific to the patient. As a result, providers are bombarded with useless warnings and often miss important ones. Our approach is to change the framework for DDI alerting from basic look-up tables to a more complex, but meaningful, clinical algorithms. Our plan is innovative because it will: 1) eliminate alerts for DDIs that are not clinically important given the patient and drug context; 2) develop implementable and tested algorithms using existing and new evidence; and 3) support the dissemination, implementation, and evaluation of these algorithms across the spectrum of healthcare facilities and organizations. The central hypothesis of this project is that individualizing DDI alerts to specific patient circumstances will result in a much greater proportion of alerts that physicians, pharmacists, and other healthcare providers will be more likely to heed. We will accomplish our objectives and test our hypothesis by pursuing the following aims: Specific Aim 1: Design sharable evidence-based individualized DDI algorithms that capitalize on the wealth of patient data located within electronic health records; Specific Aim 2: Validate the function of newly designed DDI algorithms using electronic health record data; and Specific Aim 3: Conduct a prospective evaluation of DDI algorithms in a variety of healthcare environments including ambulatory and institutional settings. This project will greatly improve CDS for DDIs by incorporating contextual factors into evidence-based and validated alert algorithms, which will reduce alert fatigue and result in more meaningful CDS. Our approach, involving partners across multiple organizations and environments and experts in drug interaction and biomedical informatics, will result in safer healthcare with respect to the use of medications.

有意义的药物相互作用警报 项目概要： 电子健康记录 (EHR) 和处方系统的临床决策支持 (CDS) 促进改善患者治疗效果。 CDS 的一种类型是药物相互作用 (DDI) 警报。办公室 国家健康信息技术协调员有意义的使用标准包括实施 DDI 检测和 对医生和其他医疗保健专业人员的警告。几乎所有医疗保健组织都依赖 DDI 从商业药物知识数据库生成的警报。当前使用简单的方法生成警告 药物组合规则，忽略药物属性和 EHR 中可用的丰富信息 针对患者提出具体的警告。结果，提供商受到无用警告的轰炸， 经常错过重要的事情。 我们的方法是将 DDI 警报的框架从基本的查找表更改为更复杂的，但 有意义的临床算法。我们的计划具有创新性，因为它将：1) 消除对非 DDI 的警报 考虑到患者和药物背景，具有临床重要性； 2）使用开发可实现和经过测试的算法 现有的和新的证据； 3) 支持这些内容的传播、实施和评估 跨医疗机构和组织的算法。这个假设的中心假设 项目的目的是针对特定患者情况个性化 DDI 警报将导致更大比例 医生、药剂师和其他医疗保健提供者更有可能注意到的警报。我们将 通过追求以下目标来实现我们的目标并检验我们的假设： 具体目标 1：设计可共享的基于证据的个性化 DDI 算法，利用丰富的 电子健康记录中的患者数据； 具体目标2：利用电子健康记录数据验证新设计的DDI算法的功能； 和 具体目标 3：在各种医疗保健环境中对 DDI 算法进行前瞻性评估 包括门诊和机构环境。 该项目将通过将背景因素纳入基于证据的和 经过验证的警报算法，这将减少警报疲劳并产生更有意义的 CDS。我们的方法， 让跨多个组织和环境的合作伙伴以及药物相互作用方面的专家参与进来 生物医学信息学将在药物使用方面带来更安全的医疗保健。