Adapting Clinical Systems to Enable Adolescents' Genomic Choices.
Adapting Clinical Systems to Enable Adolescents' Genomic Choices.
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DOI：
10.1055/s-0040-1718747
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Hall, Eric S
中科院分区：
文献类型：
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作者：
Prows, Cynthia A;Marsolo, Keith;Myers, Melanie F;Nix, Jeremy;Hall, Eric S
我们为青少年提供了个性化的选择，让他们在研究期间了解他们想要的遗传结果类型，并创建了一个工作流程来过滤结果并将其传输到电子健康记录（EHR）。我们描述了所需的调整，以确保青少年的结果记录在EHR和返回到青少年/父母二人组匹配他们的选择。一个网络应用程序可以根据青少年的选择手动修改基本的实验室报告数据。实验室报告的最终PDF格式无法通过EHR患者门户查看，因此创建了EHR表单，以支持手动输入可在门户中查看的离散结果。使青少年能够选择遗传结果是一个劳动密集型的过程。开发应用程序和EHR表格需要超过350个小时，研究专业人员需要超过50个小时的时间将选择输入应用程序和EHR。通过患者门户网站了解遗传结果的青少年及其父母表示，他们对返回的方法感到满意，如果有选择，他们会再次做出选择。虽然未来的EHR升级预计将使患者门户网站访问PDF，但需要进行额外的改进，以允许根据患者的偏好对结果进行分区和过滤。此外，将这些结果分离为更离散的组件将允许它们单独存储在EHR中，支持在临床决策支持或人工智能应用中使用这些数据。
We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR). We describe adaptations needed to ensure that adolescents’ results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents’ choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal. Enabling adolescents’ choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional’s time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option. Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications.