Meaningful Drug Interaction Alerts

有意义的药物相互作用警报 项目摘要： 用于电子健康记录（EHR）和处方系统的临床决策支持（CDS）已被 以改善患者的治疗效果。一种类型的CDS是药物相互作用（DDI）警报。办公室 国家卫生信息技术有意义使用标准协调员包括DDI检测的实施， 警告医生和其他医疗保健专业人员。几乎所有的医疗机构都依赖DDI 商业药物知识数据库生成的警报。目前，使用简单的 药物组合规则，忽略药物属性和EHR中可用的丰富信息， 针对患者提出警告。结果，提供商被无用的警告轰炸， 经常错过重要的。 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是创新的，因为它将：1）消除不符合 考虑到患者和药物背景，具有临床重要性; 2）使用 现有的和新的证据; 3）支持传播，实施和评估这些 算法在医疗机构和组织的范围内。这个问题的核心假设是 项目的另一个特点是，针对特定患者情况的个性化DDI警报将导致更大比例的 医生、药剂师和其他医疗保健提供者将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， 电子健康记录中的患者数据; 具体目标2：使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算法进行前瞻性评价 包括流动和机构环境。 该项目将通过将背景因素纳入循证和 经验证的警报算法，这将减少警报疲劳并产生更有意义的CDS。我们的方法， 涉及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， 生物医学信息学将导致在使用药物方面更安全的医疗保健。

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.