Duke Jon D, Bolchini Davide
Regenstrief Institute, Indianapolis, IN, USA.
AMIA Annu Symp Proc. 2011;2011:339-48. Epub 2011 Oct 22.
Evaluating the potential harm of a drug-drug interaction (DDI) requires knowledge of a patient's relevant co-morbidities and risk factors. Current DDI alerts lack such patient-specific contextual data. In this paper, we present an efficient model for integrating pertinent patient data into DDI alerts. This framework is designed to be interoperable across multiple drug knowledge bases and clinical information systems. To evaluate the model, we generated a set of contextual DDI data using our local drug knowledge base then conducted an evaluation study of a prototype contextual alert design. The alert received favorable ratings from study subjects, who agreed it was an improvement over traditional alerts and was likely to support clinical management and save physician time. This framework may ultimately help reduce alert fatigue through the dynamic display of DDI alerts based on patient risk.
评估药物相互作用(DDI)的潜在危害需要了解患者的相关合并症和风险因素。当前的DDI警报缺乏此类特定于患者的背景数据。在本文中,我们提出了一种将相关患者数据整合到DDI警报中的有效模型。该框架旨在跨多个药物知识库和临床信息系统实现互操作性。为了评估该模型,我们使用本地药物知识库生成了一组背景DDI数据,然后对一个原型背景警报设计进行了评估研究。该警报获得了研究对象的好评,他们一致认为它比传统警报有所改进,并且可能有助于临床管理并节省医生时间。该框架最终可能通过基于患者风险动态显示DDI警报来帮助减轻警报疲劳。