Improving Medication Safety through Human Factors and Informatics Research

描述（由申请人提供）： 根据医学研究所的数据，美国每年发生超过100万起可预防的药物不良事件（ADE）。为了降低患者伤害的风险，VA的计算机化供应商订单输入（CPOE）系统包括自动药物警报，例如药物相互作用警报，以警告处方者潜在的问题。不幸的是，令人信服的证据表明，VA警报没有提供足够的信息来帮助处方医生在订购药物时做出决定。警报设计可以通过人为因素研究获得信息;人为因素工程师专注于如何设计技术，流程和工作系统，以匹配固有的人类能力和局限性。 目的本职业发展奖（CDA-2）的研究目标是评估VA警报设计与其他系统相比的优势和劣势，并确定增强VA药物警报以供临床决策的策略。具体目标如下：目标1：确定处方者用于药物决策的认知策略 当面临药物警报时，提供有关如何改进警报设计的证据。 目标2：开发和测试警报内容的新策略，并显示辅助决策- 在订购过程的最早阶段，为了促进更安全的 处方决定。 目的3：评估有前途的新警报设计是否改善了药物管理。 每一个目标都包含了人为因素技术。有几个人的因素研究人员在VA;这CDA-2的经验将准备候选人成为一个独立的VA人的因素研究人员，在信息技术处方，药物管理和患者安全的专业知识。 方法针对目标1，采用认知任务分析法，包括关键决策法和模拟访谈法，对处方医师进行药物过敏警报、药物相互作用警报和药物疾病警报的决策过程。对于目标2，将开发和评价两种新的警报设计，以评估哪种设计最有效地支持处方者理解，因为这对于支持决策至关重要。最后，对于目标3，将对处方医生进行模拟研究，以将新的警报设计与VA CPOE系统进行比较，以评估重新设计是否改善了临床决策结果。 作为目标3的一部分，我们还将测试在一个VAMC中实施设计变更和收集临床结局的可行性。 意义本CDA-2研究将确定VA警报设计策略，以更好地支持临床决策。这项研究对于减少可预防的伤害和推进未来的VA健康信息技术至关重要。

DESCRIPTION (provided by applicant): Background/Rationale: According to the Institute of Medicine, over 1 million preventable adverse drug events (ADEs) occur in the U.S. each year. To reduce the risk of patient harm, the VA's computerized provider order entry (CPOE) system includes automated medication alerts, such as drug-drug interaction alerts, to warn prescribers about potential problems. Unfortunately, compelling evidence indicates that VA alerts do not provide sufficient information to assist prescribers as they make decisions while ordering medications. Alert design can be informed by human factors research; human factors engineers focus on how to design technologies, processes, and work systems to match inherent human capabilities and limitations. Objectives The research objective of this career development award (CDA-2) is to assess the strengths and weaknesses of VA alert designs compared to other systems and identify strategies to enhance VA medication alerts for clinical decision-making. The specific aims are as follows: Aim 1: Identify the cognitive strategies that prescribers use for medication decision-making when faced with medication alerts, to provide evidence on how to improve alert design. Aim 2: Develop and test new strategies for alert content and display that aid decision- making at the earliest possible stage of the order process, in order to promote safer prescribing decisions. Aim 3: Evaluate whether the promising new alert design improves medication management. Each of these aims incorporate human factors techniques. There are few human factors researchers in VA; this CDA-2 experience will prepare the candidate to become an independent VA human factors researcher, with specialized expertise in information technologies for prescribing, medication management, and patient safety. Methods For Aim 1, cognitive task analysis, including critical decision method and simulation interviews, will be conducted with prescribers to uncover their decision-making process for drug-allergy alerts, drug-drug interaction alerts, and drug-disease alerts. For Aim 2, two new alert designs will be developed and evaluated to assess which design most effectively supports prescriber comprehension, since this is essential to support decision-making. Finally, for Aim 3, a simulation study will be conducted with prescribers to compare the new alert design against the VA CPOE system to assess whether the redesign improves clinical decision-making outcomes. As part of Aim 3, we will also test the feasibility of implementing design changes and collecting clinical outcomes at one VAMC. Significance This CDA-2 research will identify VA alert design strategies to better support clinical decision- making. This research is essential to reduce preventable harm and advance VA health information technology for the future.