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Clinical Decision Support for Unsolicited Genomic Results

主动提供的基因组结果的临床决策支持

基本信息

批准号：
10436990
负责人：
CASEY OVERBY TAYLOR
金额：
$ 48.14万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10436990
关键词：
Address Adopted Adoption Area Award Clinical Committee Members Custom Data Diagnosis Disease Ensure Feedback Focus Groups Future Genomic medicine Genomics Goals Health Health Personnel Health system Healthcare Individual Infrastructure Institution Institutional Policy Learning Modeling Motion National Human Genome Research Institute Outcome Patients Persons Pharmacy and Therapeutics Committee Prevention Process Recommendation Research Research Methodology Risk Role Science Structure Surveys System Test Result Testing Time Time and Motion Studies Vision Work adverse drug reaction base clinical care clinical decision support computerized tools dashboard design evidence base genetic makeup genomic data health care settings implementation science improved innovation interest meetings preference programs provider adoption research study social usability

项目摘要

Project Summary As healthy individuals increasingly can receive genomic testing results that indicate their risk for poor outcomes (e.g. diseases or adverse drug reactions), healthcare providers will need to ensure that the results are handled prudently, by addressing the receipt of the results, the workflow challenges, and liability issues. Given that clinical genomic tests can be initiated outside of the clinical setting (e.g., in a research study), from the clinician’s perspective, they can be characterized as unsolicited genomic results (UGR). Clinical decision support (CDS) has great potential to ease the adoption of UGR by providing clinicians with recommendations and patient-related information presented at particular times to enhance clinical care. Deploying CDS for UGR in a healthcare setting in a scalable way, however, will depend on our capacity to leverage local institutional policy and oversight structures to approve of CDS guidance and strategies for UGR. The specific objective of this research program is to develop and evaluate the Evidence-based Decision support Implementation over Time (EDIT) model for prioritizing and revising deployed CDS for UGR. The EDIT model will empower local oversight committees such as Pharmacy & Therapeutics committees to have a role in the CDS review and deployment processes within existing institutional social systems using accepted organizational processes. The direct benefits of this work will be an EDIT dashboard that can be used by oversight committees to prioritize new and to revise deployed CDS, and infrastructure to close the loop of the learning health system by transferring CDS revisions approved by oversight committee members into deployed CDS for UGR. EDIT model implementation will be informed by mixed methods research strategies: Strategy 1, we will conduct focus groups with oversight committee members in order to understand current roles, tasks and goals of the committee, as well as to capture opinions about the best processes to prioritize, review and approve of new and revised CDS for UGR as part of committee meeting activities. Research Strategy 2, we will conduct a survey study with patients to assess preferences for the return of UGR with CDS and usability studies with oversight committee members to gather feedback on the EDIT dashboard design. Strategy 3, we will conduct time-motion observations of local oversight committee meetings prior to and after deploying the EDIT model in order to plan a future, multi-institution, time-motion study with statistical power to detect differences between oversight committees that use the EDIT dashboard and those that do not. The hypothesis is that time spent prioritizing new and revised CDS will be shorter with use of the EDIT dashboard. Overall, the EDIT model establishes processes that lower barriers to implementing robust genomic medicine programs that can be followed by others. The Genomic Innovator Award will enable me to study, in team-science projects, how the EDIT model can accelerate the institutional review and approval process of CDS for UGR. The broader impact of this work is being able to study rate of UGR adoption by healthcare providers for deployed CDS for UGR.

项目总结