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与临床医生沟通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，以促进有效地交流基因组数据，提高医生的信心，从而做出明智的健康决策。本建议的具体目标是发展和评价CDSS。我们的方法将与马里兰大学医疗系统临床决策支持委员会的大约10名医疗IT倡导者和50名利益相关者合作，包括临床研究协调员、内分泌学家、介入性心脏病学家、糖尿病教育者、执业护士、医疗助理、行政人员和其他参与马里兰大学两个典型个性化医疗（PM）项目的临床专业人员。典型的项目是个性化糖尿病医学项目，旨在识别和诊断具有高渗透性遗传形式的糖尿病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基因型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对我们有效地与医生沟通基因组数据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交流基因组数据的CDSS。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范例PM实施项目中探索CDSS的能力，我们在开展这项研究方面也处于独特的地位。我们将完成以下目标来检验我们的假设：目标1我们将采用以用户为中心的方法来设计原型CDSS；目标2：我们将使基因组知识正规化，以便整合到电子健康记录（EHR）中；目标3，我们将把最终的CDSS与电子病历整合在一起。我们将使用前/后研究设计来评估参与示范项目管理的健康IT冠军和利益相关者的态度。在项目开始和结束时，我们将使用标准化过程理论（Normalization Process Theory）提供的调查工具来测量态度，该理论侧重于个人和团体必须做的工作，以便将新技术或实践嵌入并维持在日常实践中。这项工作的主要贡献将是：(a)设计一个原型CDSS，用于有效地向医生传达基因组数据；(b)可执行的基因组CDS知识和配置资源，用于我们的原型CDSS；(c)对我们的中心假设的回答，该假设提供了洞察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。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的数据和知识管理，使用医疗IT来改进医疗保健决策。

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.