Real-time genetic diagnosis at the point of care

护理点实时基因诊断

• 批准号: 10640219
• 负责人: Marc S. Williams
• 金额: $ 92.11万
• 依托单位: GEISINGER CLINIC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-10 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640219
• 关键词: Address; Affect; Algorithms; Area; Bioinformatics; Caregivers; Caring; Clinical; Clinical Data; Clinical Engineering; Clinical Management; Clinical Research; Collaborations; Complex; Data; Databases; Development; Diagnosis; Diagnostic; Disease; Documentation; Electronic Health Record; Eligibility Determination; Emotional; Expenditure; Family; Frustration; Genes; Genetic; Genetic Diseases; Genomics; Grief reaction; Health; Health Personnel; Healthcare Systems; Heart; Hospitalization; Hospitals; Human; Individual; Inpatients; Life Style; Link; Manuals; Medical Records; Mind; Outcome; Outpatients; Pathogenicity; Pathway interactions; Patient Care; Patient Schedules; Patient-Focused Outcomes; Patients; Penetrance; Persons; Phenotype; Physicians; Population; Preventive; Primary Care Physician; Procedures; Process; Provider; Research; Resources; Role; Sensitivity and Specificity; Signs and Symptoms; System; Taxes; Test Result; Testing; Time; Uncertainty; Underserved Population; Variant; Vision; Visit; Work; care outcomes; causal variant; clinical care; clinically relevant; cohort; experience; genetic association; genetic disorder diagnosis; genetic information; genetic resource; genetic testing; genetic variant; improved; improved outcome; in silico; innovation; medical specialties; point of care; prevent; psychologic; rapid diagnosis; virtual

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.

摘要 当复杂疾病出现时，患者和照顾者的负担给家庭和照顾者都造成了沉重的代价 医疗保健系统。多次住院和各种检测程序通常会带来更多 未知和悲痛给本已困难的局面带来了影响。医院的来访打乱了病人的日程安排，还 给旨在最大化健康结果的医疗保健系统带来不必要的负担 病人。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他们现有的基因信息中受益。医生可以 难以理解订购基因检测的正确时间，以及内部快速变化的速度 在遗传学领域，许多医生没有利用在适当的时候可用的基因检测。 基因检测还需要来自有限工作人员的医院资源，因此每一位患者 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 获取他们的基因信息需要一种创新的方法。在盖辛格，我们有一群 15万名患者已经被测序，我们目前有他们的基因数据。我们建议开始 患者的临床表现目前被绘制到电子健康记录中，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 为患者显示最佳影响点以改善医疗保健结果的工作流程。要认识到这一点 展望，我们已经确定了我们想要解决的三个领域。有候选人的病人的识别 然后对基因组序列数据进行并行的生物信息学分析。最后， 我们希望解决将基因测试结果返回给提供者和患者的问题，以便双方都有 适当的信息，以指导具体情况的护理。为了解决这三个需求，我们有 制定了三个具体目标，并考虑到盖辛格专家的实施。目标1.发展 一种高效的表型鉴定系统(HIPIS)。目标2.开发动态虚拟遗传小组 (DVGP)用于实时基因诊断。目标3.最佳护理点的临床工作流程分析 集成实时基因诊断。与盖辛格专家以及人类方面的专家合作 表型鉴定(Peter Robinson)将增加对将遗传信息整合到患者中的理解 关心。这一转变将使各个领域的许多专家的工作变得轻而易举 初级保健医生正在研究治疗复杂疾病的最佳方向。