Real-time genetic diagnosis at the point of care

总结 当复杂疾病出现时，患者和护理人员的负担对家庭和家庭都造成了沉重的负担， 医疗保健系统。多次住院治疗和各种检测程序往往带来更多的 未知和悲伤的情绪，已经很困难了医院访问打乱了病人的时间表， 对医疗保健系统造成不必要的负担，其目的是最大限度地提高患者的健康结果。 患者这些复杂的疾病需要额外的访问和不必要的测试。我们想开发一个系统 这将确定哪些患者可以从访问他们现有的遗传信息中受益。医生可能 努力了解订购基因检测的正确时间，随着国内快速变化的步伐， 在遗传学领域，许多医生没有在适当的时候利用基因检测。 基因检测还需要医院从有限的工作人员中获得资源，因此， 因为复杂的病例可能不适合进行基因检测。确定哪些患者应该 获取他们的遗传信息需要一种创新的方法。在盖辛格，我们有一群 150，000名患者已接受测序，我们目前拥有他们的遗传数据。我们建议从 与当前绘制到电子健康记录中的患者临床表现相结合， 表型术语将触发遗传资源的可用性。这些遗传资源包括 这些工作流程显示了患者改善医疗保健结果的最佳影响点。实现这一 在展望未来的时候，我们确定了我们希望解决的三个领域。识别具有候选人的患者 在真实的时间条件下，随后进行基因组序列数据的并行生物信息学分析。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， 适当的信息，以指导具体情况的护理。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，以便实施。目标1.发展 高影响表型鉴定系统（HIPIS）。目标二。开发动态虚拟基因组 （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 集成了真实的实时基因诊断。与Geisinger的专家以及人类 表型分析（Peter罗宾逊）将增加对将遗传信息整合到患者体内的理解。 在乎这一转变将使许多专家在各个领域的工作，坐在指尖， 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.