A National Center for Digital Health Informatics Innovation

国家数字健康信息学创新中心

• 批准号: 10464821
• 负责人: CHRISTOPHER G CHUTE
• 金额: $ 9.68万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-25 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10464821
• 关键词: Address; Adult; Algorithmic Software; Algorithms; Architecture; Area; Benchmarking; COVID-19; COVID-19 severity; Child; Childhood; Clinical; Clinical Data; Clinical Research; Collaborations; Common Data Element; Communities; Community of Practice; Computer software; Country; Cultural Evolution; Data; Data Analyses; Data Set; Decentralization; Development; Discipline; Ecosystem; Electronic Health Record; FAIR principles; Fast Healthcare Interoperability Resources; Foundations; Goals; Gold; Health; Health system; Individual; Informatics; Information Resources; Information Technology; Infrastructure; Institution; Knowledge; Learning; Licensing; Longevity; Metadata; Methodology; Methods; Modeling; Morphologic artifacts; Outcome; Participant; Patients; Pediatrics; Prediction of Response to Therapy; Process; Public Health Informatics; Quality Control; Rare Diseases; Reporting; Research; Scanning; Science; Severities; Severity of illness; Software Engineering; Software Tools; Synapses; System; Technology; Training; Translational Research; United States National Institutes of Health; Update; Variant; Vision; Work; base; blind; clinical care; cloud based; cohort; collaborative approach; collaborative environment; coronavirus disease; data ecosystem; data harmonization; data modeling; data quality; data resource; data sharing; data standards; data translator; design; digital health; experience; health care quality; implementation process; improved; innovation; interoperability; invention; open data; open source; predictive modeling; quality assurance; software development; success; support network; tool; vaccine effectiveness

Project Summary Local solutions rarely scale to multi-institutional settings, and don’t realize the vision of CTSAs catalyzing data- driven translational research. Data, Software, and People are Largely Decentralized and Unconnected in CTSA. CTSAs hold a wealth of data that is neither accessible nor interoperable; this hinders opportunities to innovate algorithms and tools. Tools in turn, suffer from myopic focus or blind duplication. It is extremely difficult to identify expert partners whether for functionally validating a candidate variant for a rare disease, finding clinical cohorts, or navigating the IP odyssey of starting a company. There are the challenges in building a truly learning health system where high quality clinical and research data can effectively and efficiently be reused in clinical care. The CD2H has developed the National COVID Cohort Collaborative (N3C) with COVID data from clinical institutions across the country. This publicly available data resource includes data on adults and pediatrics and can address the questions related to vaccine effectiveness, treatments and predictions of severity of illness. The Pediatric COVID-19 DREAM Challenge will ask community participants to predict severity status in children, using the National COVID Cohort Collaborative (N3C) Electronic Health Record (EHR) data. The desired outcome for this challenge is to produce trained and validated pediatric COVID-19 severity prediction models that can be implemented into a clinical workflow in an EHR to help facilitate appropriate treatment.

项目摘要 本地解决方案很少扩展到多机构环境，并且没有实现CTSA催化数据的愿景- 推动翻译研究。数据、软件和人员在很大程度上是分散和不连接的， CTSA CTSA拥有大量既不可访问也不可互操作的数据;这阻碍了 创新算法和工具。 反过来，工具也会受到近视焦点或盲目复制的影响。确定专家合作伙伴极其困难 无论是用于功能性验证罕见疾病的候选变体、寻找临床队列，还是导航 创业的知识产权之旅在建立一个真正的学习型卫生系统方面存在挑战， 高质量的临床和研究数据可以在临床护理中有效和高效地重复使用。CD 2 H具有 利用来自全国各地临床机构的COVID数据， 全国 这个公开的数据资源包括成人和儿科的数据，可以解决以下问题 与疫苗有效性、治疗和疾病严重程度的预测有关。儿科COVID-19 梦想挑战将要求社区参与者预测儿童的严重程度，使用国家 COVID队列协作（N3 C）电子健康记录（EHR）数据。这件事的预期结果 挑战是生成经过训练和验证的儿科COVID-19严重程度预测模型， 在EHR的临床工作流程中实施，以帮助促进适当的治疗。

项目成果

Personas for the translational workforce.

转化员工的角色。

• DOI: 10.1017/cts.2020.2
• 发表时间: 2020-01-10
• 期刊: Journal of clinical and translational science
• 影响因子: 2.6
• 作者: Gonzales S;O'Keefe L;Gutzman K;Viger G;Wescott AB;Farrow B;Heath AP;Kim MC;Taylor D;Champieux R;Yen PY;Holmes K
• 通讯作者: Holmes K

Post-recovery COVID-19 and incident heart failure in the National COVID Cohort Collaborative (N3C) study.

恢复后COVID-19和国家共同队列合作（N3C）研究中的事件心力衰竭。

• DOI: 10.1038/s41467-022-31834-y
• 发表时间: 2022-07-15
• 期刊: Nature communications
• 影响因子: 16.6

Trends in Use and Outcomes of Same-Day Discharge Following Elective Percutaneous Coronary Intervention.

选择性经皮冠状动脉介入治疗后当天出院的使用趋势和结果。

• DOI: 10.1016/j.jcin.2021.05.043
• 发表时间: 2021
• 期刊: JACC. Cardiovascular interventions
• 作者: Bradley,StevenM;Kaltenbach,LisaA;Xiang,Katelyn;Amin,AmitP;Hess,PaulL;Maddox,ThomasM;Poulose,Anil;Brilakis,EmmanouilS;Sorajja,Paul;Ho,PMichael;Rao,SunilV
• 通讯作者: Rao,SunilV

Temporal Events Detector for Pregnancy Care (TED-PC): A rule-based algorithm to infer gestational age and delivery date from electronic health records of pregnant women with and without COVID-19.

• DOI: 10.1371/journal.pone.0276923
• 发表时间: 2022
• 期刊: PloS one
• 影响因子: 3.7

The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Protocol for a Single-Center Prospective Trial.

• DOI: 10.2196/32873
• 发表时间: 2022-06-30
• 期刊: JMIR research protocols
• 影响因子: 1.7