Electronic Health Record-linked Decision Support for Communicating Genomic Data t

描述（由申请人提供）：使用临床决策支持向临床医生有效传达基因组数据存在挑战。我们的长期目标是了解使用临床决策支持系统（CDSS）的效果，以促进有效地沟通基因组数据对医生的信心，使明智的健康决策。本提案的具体目标是开发和评估CDSS。我们的方法将吸引大约10名健康IT冠军，他们是马里兰州医疗系统临床决策支持委员会的成员，以及50名利益相关者，包括临床研究协调员，内分泌学家，介入心脏病学家，糖尿病教育工作者，执业护士，医疗助理，行政人员，和其他参与马里兰州大学的两个示范性个性化医学（PM）项目的临床专业人员。典型项目是个性化糖尿病医学项目，旨在识别和诊断患有高度渗透性糖尿病遗传形式的患者并提供定制治疗，以及抗血小板干预研究药物基因组学的实施部门，旨在将CYP 2C 19基因型结果转化为可操作的抗血小板处方决策。核心假设是，参与这些PM计划的利益相关者在部署CDSS后，对我们向医生有效传达基因组数据的能力的态度将比以前更好。鉴于我们在开发基因组CDS方面的记录，我们有资格开发CDSS，以有效地交流基因组数据。鉴于我们与医生一起研究基因组CDS的经验以及我们在两个正在进行的示范PM实施计划的背景下探索CDSS的能力，我们也处于进行这项研究的独特位置。我们将完成以下目标来测试我们的假设：目标1，我们将采用以用户为中心的方法来设计一个原型CDSS;目标2，我们将正式整合到电子健康记录（EHR）的基因组知识;目标3，我们将整合最终的CDSS与EHR。我们将使用一个前/后研究设计，以评估健康IT冠军和利益相关者参与示范PM计划的态度。在项目的开始和结束时，我们将使用由规范化过程理论提供信息的调查工具来衡量态度，该理论侧重于个人和团体为新技术或实践所做的工作，以便在日常实践中嵌入和持续。这项工作的主要贡献将是：（a）一个原型CDSS，旨在有效地沟通基因组数据的医生;（B）可执行的基因组CDS的知识和配置的资源内使用我们的原型CDSS;和（c）我们的中心假设，提供洞察利益相关者的态度，我们的能力，有效地沟通基因组数据的医生后，部署CDSS的答案。这项工作是AHRQ感兴趣的一个关键研究领域，特别是通过使用集成数据和知识管理，利用卫生信息技术来改善医疗决策。

DESCRIPTION (provided by applicant): There are challenges to using clinical decision support to effectively communicate genomic data to clinicians. Our long-term goal is to understand the effect of using a clinical decision support system (CDSS) to facilitate effectively communicating genomic data on physician confidence to make informed health decisions. The specific objective of this proposal is to develop and evaluate the CDSS. Our approach will engage approximately 10 health IT champions who are members of the University of Maryland Medical System Clinical Decision Support Committee and 50 stakeholders including clinical research coordinators, endocrinologists, interventional cardiologists, diabetes educators, nurse practitioners, medical assistants, administrative staff, and other clinical professionals participating in two exemplar personalized medicine (PM) programs at the University of Maryland. Exemplar programs are the Personalized Diabetes Medicine Program that aims to identify and diagnose patients with highly penetrant genetic forms of diabetes and provide customized treatment, and the implementation arm of the Pharmacogenomics of Anti-Platelet Intervention Study, that aims to translate CYP2C19 genotype results into actionable antiplatelet prescribing decisions. The central hypothesis is that stakeholders involved in these PM programs will have better attitudes about our ability to effectively communicate genomic data to physicians after deploying the CDSS, than before. We are qualified to develop a CDSS for effectively communicating genomic data given our track record in developing genomic CDS. We are also uniquely situated to conduct this research given our experience studying genomic CDS with physicians and our ability to explore the CDSS in the context of two ongoing exemplar PM implementation programs. We will complete the following aims to test our hypothesis: Aim 1 we will employ user-centered approaches to design a prototype CDSS; Aim 2 we will formalize genomic knowledge for integration into the electronic health record (EHR); and Aim 3 we will integrate the finalized CDSS with the EHR. We will use a pre-/post- study design to assess the attitudes of health IT champions and stakeholders involved in exemplar PM programs. At the start and conclusion of the project, we will measure attitudes using a survey instrument informed by Normalization Process Theory that focuses on the work individuals and groups have to do for a new technology or practice to become embedded and sustained in routine practice. Major contributions of this work will be: (a) a prototype CDSS designed to effectively communicate genomic data to physicians; (b) executable genomic CDS knowledge and configured resources for use within our prototype CDSS; and (c) an answer for our central hypothesis that provides insight into stakeholder attitudes about our ability to effectively communicate genomic data to physicians after deploying the CDSS. This work is a key research area of interest of AHRQ, specifically, using health IT to improve healthcare decision-making through use of integrated data and knowledge management.