Cognitive Support for Shared Decision Making Using Veterans Like Me

描述（由申请人提供）： 该项目的目标是加强退伍军人和医疗保健提供者之间的共享决策（SDM）。我们假设，我们可以通过提供关于感兴趣的结果的图形信息沿着关于像他们这样的个人的具体故事，使患者能够参与SDM。 目前，医疗保健系统正在努力让患者成为合作伙伴，以使医疗保健更加以患者为中心。有强有力的证据表明，共同决策（SDM）可以提高患者的满意度和健康结果。为了让患者积极参与SDM，他们需要被告知选项的利弊。尽管可以从多个来源获得信息，但许多患者没有此类信息。请考虑以下示例：Jim是一名56岁的退伍军人，有高血压病史，向他的初级保健提供者提出心悸和疲劳的投诉。ECG显示房颤。他开始使用美托洛尔控制心率，症状消退。在一次关于降低血栓栓塞风险治疗方案的简短讨论中，他的医生说其他药物“不能充分降低中风风险”，而且“太新”，所以吉姆开始服用华法林。经过几周反复的INR监测实验室抽血和饮食调整的努力，Jim希望他能获得更多关于像他这样的患者在不同抗凝治疗方面的结果和经验的信息。 我们相信，一种新型的决策辅助工具，可以安全地将大型临床数据存储库的功能交到患者手中，可以帮助满足上述示例中SDM的信息需求。患者决策辅助工具提高了人们对选择的了解，创造了对利益和危害的准确感知，降低了决策难度，并增加了决策过程的参与度。目前的患者决策辅助工具具有局限性，包括：1）需要专家来总结医学证据; 2）产生频繁更新所需的劳动; 3）医学证据的局限性和矛盾。此外，很少有决策辅助工具提供类似情况下患者的故事。 为了解决这些局限性，我们设想了一种可扩展的、引人入胜的患者决策辅助工具，称为VeteransLikeMe（VLMe）。VLMe将从临床资料库中检索相关病例，并使用交互式图形显示来传达汇总统计数据，并自动撰写患者故事，以促进患者参与。我们的愿景是利用“大临床数据”帮助告知患者。由于这类辅助决策工具以往并没有向病人提供，而且需要大量资源来开发，我们建议首先进行一项试验研究。在这项试点研究中，我们将进行需求分析，告知VLMe的设计，开发一个模型界面，并测试的可接受性，可行性和安全性相结合的结果信息的图形表示沿着与类似患者的故事，在SDM的上下文中。试点将重点关注房颤（AF）的药物管理决策，这是VA患者人群中非常普遍的疾病。我们将解决以下问题：如何让退伍军人了解不同药物治疗策略的相对益处和潜在副作用，使其对个人有意义并支持参与治疗决策？具体目标1：分析房颤退伍军人的SDM需求，以做出抗凝治疗决策。具体目标2：创建一个实体模型的视觉显示和小插图，以传达感兴趣的结果。具体目标3：对VLMe模型进行试验，以评估可接受性、可行性和安全性。

DESCRIPTION (provided by applicant): The goal of this project is to enhance shared decision-making (SDM) between Veterans and healthcare providers. We hypothesize that we can empower patients to participate in SDM by providing graphical information regarding outcomes of interest along with concrete stories about individuals like themselves. Currently, healthcare systems are striving to engage patients as partners in order to make healthcare more patient-centered. There is strong evidence that shared decision-making (SDM) improves both patient satisfaction and health outcomes. In order for patients to actively participate in SDM, they need to be informed of options' pros and cons. Despite the availability of information from multiple sources, many patients do not have this type of information available. Consider the following example: Jim, a 56 year-old Veteran with a history of hypertension, presents to his primary care provider with a complaint of palpitations and fatigue. An ECG demonstrates atrial fibrillation. He is started on metoprolol for rate control with resolution of his symptoms. During a brief discussion about options for thromboembolic risk reduction therapy, his doctor says other medications "don't reduce your risk of stroke enough" and are "too new," so Jim is started on warfarin. After several weeks of repeated lab draws for INR monitoring and struggling with dietary modification, Jim wishes that he had been provided with more information about the outcomes and experiences of patients like him on the different therapies for anticoagulation. We believe a new type of decision aid that safely puts the power of a large clinical data repository into the hands of patients could help meet the information needs for SDM in the example above. Patient decision aids improve people's knowledge of options, create accurate perceptions of benefits and harms, reduce difficulty in decision-making, and increase participation in the decision-making process. Current patient decision aids have limitations including: 1) the need for experts to summarize the medical evidence; 2) the labor required to produce frequent updates; 3) the limitations and contradictions of the medical evidence. In addition, few decision aids provide stories about patients in similar situations. To address these limitations, we envision an extendable and engaging patient decision aid called VeteransLikeMe (VLMe). VLMe will retrieve relevant cases from a clinical repository and use interactive graphic displays to communicate summary statistics and automatically compose patient stories to facilitate patient engagement. Our vision is to use "big clinical data" to help inform patients. Since this type of decision aid has not been previously made available to patients and will take considerable resources to develop, we propose to first carry out a pilot study. In this pilot study, we will conduct a needs analysis to inform the design of VLMe, develop a mockup interface, and test the acceptability, feasibility and safety of combining a graphical representation of outcome information along with stories about similar patients in the context of SDM. The pilot will focus on medication management decisions for atrial fibrillation (AF), a highly prevalent condition in the VA patient population. We will address the following question: How can Veterans be informed of the relative benefits and potential side effects of different medication strategies in a way that is personally meaningful and supports involvement in therapeutic decision-making? Specific Aim 1: Analyze the SDM needs of Veterans with AF for anticoagulation therapy decisions. Specific Aim 2: Create a mockup visual display and vignettes to communicate outcomes of interest . Specific Aim 3: Conduct a trial of the mockup VLMe to assess acceptability, feasibility and safety.