Diagnosing the Unknown for Care and Advancing Science (DUCAS)

诊断未知的护理和推进科学 (DUCAS)

• 批准号: 10682163
• 负责人: Euan A Ashley
• 金额: $ 470.51万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-19 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682163
• 关键词: Address; Amalgam; Automation; Bioinformatics; Biomedical Research; Caring; Clinic; Clinical; Clinical Research; Consent; Data; Data Analyses; Data Coordinating Center; Diagnosis; Diagnostic; Diagnostics Research; Disease; Ecosystem; Ensure; Environment; Etiology; Evaluation; Family; Genomics; Goals; Healthcare; Healthcare Systems; Industry Standard; Infrastructure; Institution; Knowledge; Lead; Machine Learning; Medical; Nomenclature; Patient Participation; Patient advocacy; Patient-Focused Outcomes; Patients; Persons; Process; Productivity; Research; Research Personnel; Research Support; Scheme; Science; Services; Therapeutic; Translational Research; United States National Institutes of Health; advocacy organizations; clinical care; data management; data science management; data sharing; design; experience; follow-up; genome-wide; medical schools; multidisciplinary; novel diagnostics; outreach; patient population; patient screening; programs; rare condition; remediation; response; scale up; timeline

PROJECT SUMMARY In our current healthcare system, it often takes years before patients with rare conditions and rare presentations of common conditions receive a diagnosis. In 2013, the National Institutes of Health supported the creation of the Undiagnosed Diseases Network (UDN) to address the needs of these patients, to facilitate the diagnostic process for those with undiagnosed conditions and generate new knowledge about underlying mechanisms of disease. The UDN was successful in solving medical mysteries, shortening diagnostic odysseys, and contributing to biomedical research discovery. In order to serve more patients, the UDN process must be scaled and integrated into broader healthcare and research ecosystems. As the Data Management Coordinating Center (DMCC) for a network of Diagnostic Centers of Excellence, Harvard Medical School will leverage experience in the UDN to create sustainable, nationally scaled infrastructure to support diagnosis, research, and care for those who are undiagnosed. This will be accomplished by bringing together experts in trans-institutional data sharing, data analysis, clinical care, bioinformatics, novel diagnostics, and translational research and creating three DMCC Cores - Administrative, Data Management, and Clinical Research Support - to address unmet needs of the undiagnosed. The Administrative Core will unite the DMCC and support activities of all three Cores. Together, the DMCC Cores will accomplish four aims: 1) Scale up UDN throughput by at least an order of magnitude to meet a pressing national need, 2) Leverage partnerships for sustainable coordination of diagnostic processes to increase patient autonomy while advancing opportunities for investigative science, 3) Maximize data mobility, interpretability, and shareability, and 4) Provide analytic service and data stewardship through the Data Management Core and Clinical Research Support Cores led by experts in genomics and AI teaming with clinicians and researchers.

项目摘要 在我们当前的医疗保健系统中，通常需要几年的时间才能遇到罕见的患者 共同条件的介绍得到诊断。 2013年，国立卫生研究院支持 创建未诊断的疾病网络（UDN）以满足这些患者的需求，以促进 对于患有未诊断状况的人的诊断过程，并产生有关基础的新知识 疾病机制。 UDN成功地解决了医疗谜团，缩短了诊断 奥德赛，并为生物医学研究发现做出贡献。为了为更多患者服务，UDN过程 必须缩放并整合到更广泛的医疗保健和研究生态系统中。作为数据管理 哈佛医学院的诊断中心网络协调中心（DMCC）将 利用UDN的经验来创建可持续的，全国规模的基础设施以支持诊断， 研究，并照顾那些未被诊断的人。这将通过将专家汇集在一起​​来实现 跨机构数据共享，数据分析，临床护理，生物信息学，新型诊断和转化 研究和创建三个DMCC核心 - 行政，数据管理和临床研究支持 - 满足未诊断的未满足的需求。行政核心将团结DMCC和支持 这三个核心的活动。 DMCC核心将共同完成四个目标：1）扩展UDN吞吐量 至少有一个数量级以满足迫切的国家需求，2）利用伙伴关系来实现可持续性 诊断过程的协调以提高患者自主权，同时促进机会 调查科学，3）最大化数据迁移率，可解释性和共享性，4）提供分析 通过数据管理核心和临床研究支持核心的服务和数据管理 基因组学和AI专家与临床医生和研究人员合作。