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

描述(由申请人提供)：使用临床决策支持来有效地将基因组数据传达给临床医生存在挑战。我们的长期目标是了解使用临床决策支持系统(CDSS)的效果，以促进关于医生做出知情健康决策的信心的基因组数据的有效交流。这项提案的具体目标是制定和评估CDSS。我们的方法将使马里兰大学医疗系统临床决策支持委员会成员中的大约10名医疗IT冠军和50名利益相关者参与马里兰大学的两个示范个性化医学(PM)项目，其中包括临床研究协调员、内分泌学家、介入性心脏病学家、糖尿病教育工作者、护士、医务助理、行政人员和其他临床专业人员。EMPLAAR计划是个性化糖尿病医学计划，旨在识别和诊断具有高渗透性糖尿病遗传形式的患者并提供定制治疗，以及抗血小板干预药物基因组学研究的实施部门，旨在将CYP2C19基因结果转化为可操作的抗血小板处方决策。中心假设是，在部署CDSS后，参与这些PM项目的利益相关者将比以前对我们有效地将基因组数据传达给医生的能力有更好的态度。鉴于我们在开发基因组CDS方面的记录，我们有资格开发一个有效地传递基因组数据的CDSS。考虑到我们与医生研究基因组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验证我们的假设：目标1我们将采用以用户为中心的方法设计一个原型CDSS；目标2我们将基因组知识形式化以集成到电子健康记录(EHR)中；目标3我们将最终的CDSS与EHR集成。我们将使用研究前/研究后设计来评估参与示范PM计划的卫生IT冠军和利益相关者的态度。在项目开始和结束时，我们将使用由正常化过程理论提供信息的调查工具来测量态度，该工具侧重于个人和团队为使新技术或实践嵌入和维持在常规实践中所必须做的工作。这项工作的主要贡献将是：(A)旨在有效地向医生传递基因组数据的原型CDSS；(B)可执行的基因组CDS知识和配置的资源，用于我们的原型CDSS；以及(C)对我们的中心假设的答案，该中心假设提供了利益相关者对我们在部署CDSS后有效地向医生传达基因组数据的能力的态度的洞察。这项工作是AHRQ感兴趣的一个关键研究领域，具体地说，利用卫生信息技术通过使用集成数据和知识管理来改进医疗决策。

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.