Meaningful Drug Interaction Alerts

有意义的药物相互作用警报 项目总结： 电子健康记录(EHR)和处方系统的临床决策支持(CDS)已经 推广以改善患者的预后。CDS的一种类型是药物相互作用(DDI)警报。的办公室 国家卫生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。几乎所有的医疗组织都依赖DDI 从商业药物知识数据库生成的警报。当前使用Simple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，这些信息可能 针对患者发出特定的警告。其结果是，供应商被无用的警告和 往往会错过重要的事情。 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。我们的计划是创新的，因为它将：1)消除对DDIS的警报 在考虑到患者和药物环境的情况下具有重要的临床意义；2)开发可实现和经过测试的算法 现有和新的证据；以及3)支持这些证据的传播、实施和评估 各种医疗机构和组织的算法。这一点的中心假设是 项目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更有可能注意到的警报。我们会 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 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算法进行前瞻性评估 包括非卧床和机构环境。 该项目将通过将上下文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和专家参与药物相互作用和 生物医学信息学，将导致更安全的医疗保健方面的药物使用。

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.