Real-time genetic diagnosis at the point of care

总结 当出现复杂疾病时，患者和护理人员的负担会给家庭和患者带来沉重的负担。 医疗保健系统。多次住院治疗和各种检测程序往往会带来更多 未知和悲伤给本已困难的局势带来了影响。去医院就诊会打乱患者的日程安排， 给医疗保健系统带来不必要的负担，其目的是最大限度地提高人们的健康结果 患者。这些复杂的疾病需要额外的就诊和不必要的检测。我们想开发一个系统 这将确定哪些患者可以从获取其现有遗传信息中受益。医生可能会 努力了解订购基因检测的正确时间，并且随着内部变化的快速步伐 在遗传学领域，许多医生没有在适当的时候利用可用的基因检测。 基因检测还需要来自有限的工作人员的医院资源，因此每一位出现的患者 由于病例复杂，可能不适合进行基因检测。确定哪些患者应该接受治疗 获取他们的遗传信息需要创新的方法。在盖辛格，我们有一群 150,000 名患者已被测序，我们目前拥有他们的基因数据。我们建议开始 患者的临床表现目前已绘制成电子健康记录，以识别 将触发遗传资源可用的表型术语。这些遗传资源将包括 显示患者改善医疗结果的最佳影响点的工作流程。为了实现这一点 愿景中，我们确定了我们想要解决的三个领域。识别患者与候选者 实时条件，然后对基因组序列数据进行并发生物信息分析。最后， 我们希望解决将基因检测结果返回给提供者和患者的问题，以便双方都有 指导特定情况护理的适当信息。为了满足这三个需求，我们 与 Geisinger 的专家一起制定了三个具体目标以供实施。目标 1. 发展 高影响表型识别系统（HIPIS）。目标 2. 开发动态虚拟遗传面板 （DVGP）用于实时基因诊断。目标 3. 分析临床工作流程以实现最佳护理点 整合实时基因诊断。与 Geisinger 专家以及人类专家合作 表型分析（Peter Robinson）将增进对将遗传信息整合到患者体内的理解 关心。这种转变将使各领域许多专家的工作成果触手可及。 初级保健医生同时研究复杂疾病的最佳方向。

Summary The burden on patients and caregivers when complex diseases arise creates a taxing toll for both families and healthcare systems. Multiple inpatient hospitalizations and various testing procedures often bring more unknowns and grief to an already difficult situation. Hospital visits disrupt patient schedules, and also place unnecessary burdens on a healthcare system whose purpose is to maximize the health outcomes of the patients. These complex diseases utilize extra visits and unnecessary testing. We want to develop a system that would identify patients who could benefit from accessing their existing genetic information. Physicians may struggle to understand the correct time to order genetic testing, and with the rapid pace of change within the genetics field, many physicians are not utilizing the genetic testing that is available at an appropriate time. Genetic testing also requires hospital resources from a limited pool of workers, thus every patient that presents as a complex case may not be a suitable candidate for genetic testing. Identifying which patients should be accessing their genetic information requires an innovative approach. At Geisinger, we have a cohort of 150,000 patients who have been sequenced and we currently have their genetic data. We propose starting with the patients clinical presentations that are currently charted into an electronic health record to identify phenotype terms that would trigger genetic resources to be available. These genetic resources would include workflows that show optimal points of impact for the patients to improve healthcare outcomes. To realize this vision, we have identified three areas that we would like to address. Identification of patients with a candidate condition in real time, followed by a concurrent bioinformatic analysis of the genomic sequence data. Finally, we want to address returning the genetic test result to the provider and patient so that both parties have the appropriate information to guide condition-specific care. In order to address these three needs, we have developed three specific aims with the experts at Geisinger in mind for implementation. Aim 1. Development of a High Impact Phenotype Identification System (HIPIS). Aim 2. Develop Dynamic Virtual Genetic Panels (DVGP) for real-time genetic diagnosis. Aim 3. Analysis of clinical workflows for optimal point of care integration of real time genetic diagnosis. Collaboration with Geisinger experts as well as experts in human phenotyping (Peter Robinson) will increase understanding about integrating genetic information into patient care. This transformation will allow the work of many experts in various fields to be sitting at the fingertips of primary care physicians while researching the best direction for complex diseases.