Prioritized Clinical Decision Support to Reduce Cardiovascular Risk

描述（由申请人提供）：本项目的目标是开发和实施复杂的基于床旁EHR的临床决策支持，（a）识别和（B）优先考虑所有可用的循证治疗方案，以降低给定患者的心血管风险（CVR）。在开发基于EHR的决策支持干预后，我们将在一项包括18个初级保健诊所、60名初级保健医生和18，000名中度或高度CVR的成年人的分组随机试验中测试其对CVR的影响。如果成功，这种方法将（a）改善慢性疾病的结果，并减少约35%的美国成年人口的CVR，（B）最大限度地提高对复杂的门诊EHR系统进行的大量投资的临床回报，以及（c）为如何使用EHR技术支持在初级保健环境中提供“个性化医疗”提供了一个模型。 公共卫生相关性：本项目的目标是开发和实施复杂的基于床旁电子病历的临床决策支持，（a）识别和（B）优先考虑所有可用的循证治疗方案，以降低特定患者的心血管风险（CVR）。治疗选项的优先列表以不同的格式提供给初级保健医生（PCP）和患者，每次就诊时，CVR为中度至高度、控制欠佳且可能可逆的CVR因素的患者都会进行就诊。根据每种治疗方案的潜在CVR降低幅度，对现有循证治疗方案进行优先排序。这种干预策略被称为优先临床决策支持（PCS），专为在初级保健环境中广泛使用而设计，并有可能大幅增加目前在35%的10年FRA CVR为10%或更高的美国成年人中控制CVR的努力。为了评估PCS干预降低成人CVR的能力，我们将随机分配18家初级保健诊所，其中包括60名初级保健医生（PCP）和约18，000名符合条件的成年人，这些成年人具有基线10年内发生重大CV事件的风险（心脏病发作或中风）的10%或更多的两个实验条件之一：第1组包括9家诊所（30名PCP和9，000名患者），这些诊所将接受优先临床决策支持（PCS），以减少合格成人每次临床就诊时的CVR。第2组包括9家诊所（30名PCP和9，000名患者），未接受研究干预，构成常规护理对照组。本研究将正式检验以下假设：在基线CVR控制后，干预开始后12个月和24个月时，第1组的干预后10年Frachial CVR将优于第2组。此外，将评估干预对CVR特定组分（BP、血脂、血糖、阿司匹林使用和吸烟）的影响，并量化干预的成本效益。这个创新项目建立在我们的研究团队10年的前期工作基础上，通过引入优先级，通过在办公室访问时为患者和PCP提供决策支持，以及通过在广泛和至关重要的CVR减少临床领域扩展决策支持，扩展了之前成功的EHR临床决策支持干预措施。这个项目的结果，无论是积极的还是消极的，都将扩大我们对如何最大限度地提高目前正在复杂的门诊EHR系统中进行的大规模公共和私营部门投资的临床回报的理解。如果成功的话，这个决策支持工具可以广泛地用于标准化和个性化的护理提供的情况下，经理，药剂师和其他提供者在广泛的医疗服务配置。

DESCRIPTION (provided by applicant): The objective of this project is to develop and implement sophisticated point-of-care EHR-based clinical decision support that (a) identifies and (b) prioritizes all available evidence-based treatment options to reduce a given patient's cardiovascular risk (CVR). After developing the EHR-based decision support intervention, we will test its impact on CVR, the components of CVR, in a group randomized trial that includes 18 primary care clinics, 60 primary care physicians, and 18,000 adults with moderate or high CVR. This approach, if successful, will (a) improve chronic disease outcomes and reduce CVR for about 35% of the U.S. adult population, (b) maximize the clinical return on the massive investments that are increasingly being made in sophisticated outpatient EHR systems, and (c) provide a model for how to use EHR technology support to deliver "personalized medicine" in primary care settings. PUBLIC HEALTH RELEVANCE: The objective of this project is to develop and implement sophisticated point-of-care EHR-based clinical decision support that (a) identifies and (b) prioritizes all available evidence-based treatment options to reduce a given patient's cardiovascular risk (CVR). The prioritized list of treatment options is provided in different formats to both the primary care physician (PCP) and patient at the time of each office visit made by a patient with moderate to high CVR and sub-optimally controlled and potentially reversible CVR factors. Available evidence-based treatment options are prioritized based on the magnitude of potential CVR reduction of each treatment option. This intervention strategy, referred to as Prioritized Clinical Decision Support (PCS), is specifically designed for widespread use in primary care settings and has the potential to substantially augment current efforts to control CVR in the 35% of American adults with 10-year Framingham CVR of 10% or higher. To assess the ability of the PCS intervention to reduce CVR in adults, we will randomize 18 primary care clinics with 60 primary care physicians (PCPs) and approximately 18,000 eligible adults with baseline Framingham 10-year risk of a major CV event (either heart attack or stroke) of 10% or more into one of two experimental conditions: Group 1 includes 9 clinics (with 30 PCPs and 9,000 patients) that will receive prioritized clinical decision support (PCS) to reduce CVR at the time of each clinical encounter made by an eligible adult. Group 2 includes 9 clinics (with 30 PCPs and 9,000 patients) that receive no study intervention and constitute a usual care control group. The study will formally test the hypothesis that after control for baseline CVR, post- intervention 10-year Framingham CVR will be better in Group 1 than Group 2 at 12 and 24 months after start of the intervention. In addition, impact of the intervention on specific components of CVR (BP, lipids, glucose, aspirin use, and smoking) will be assessed, and the cost-effectiveness of the intervention will be quantified. This innovative project builds upon 10 years of prior work by our research team, and extends prior successful EHR clinical decision support interventions by introducing prioritization, by providing decision support to both patients and PCPs at the time of the office visit, and by extending the decision support across the broad and critically important clinical terrain of CVR reduction. The results of this project, whether positive or negative, will extend our understanding of how to maximize the clinical return on massive public and private sector investments now being made in sophisticated outpatient EHR systems. If successful, this decision support tool could be broadly used to both standardize and personalize care delivered by case managers, pharmacists, and other providers in a wide range of care delivery configurations.