ISCF HDRUK DIH Sprint Exemplar: GOSH–DRIVE Digital Research Environment for secure cloud-based health data management, FHIR-based interoperability and auditable analytics; a platform for research and advanced cognitive services development

ISCF HDRUK DIH Sprint 示例：GOSHâDRIVE 数字研究环境，用于基于云的安全健康数据管理、基于 FHIR 的互操作性和可审计分析；

• 批准号: MC_PC_18034
• 金额: $ 28.54万
• 依托单位: Great Ormond Street Hospital
• 依托单位国家: 英国
• 项目类别: Intramural
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MC_PC_18034
• 关键词: ISCF; HDRUK; DIH; Sprint; Exemplar

Great Ormond Street Hospital, a specialist children’s centre, has established a digital research unit, including NHS clinicians, UCL academics and industry partners. Our aim is to improve child health by optimising the use of clinical data for research.This project will demonstrate how routinely collected, non-identifiable patient data can be used and tracked through the complete research cycle. We will take advantage of cloud-platforms and new technology (‘Fast Healthcare Interoperability Resources’ ;FHIR) for the collection and safe handling of healthcare data. This approach will support the development of various healthcare ‘apps’, for patients, families and healthcare professionals, similar to existing phone apps used regularly by millions.Throughout this project, data is collected, accessed, and used in a safe and secure manner. The development of SMART apps does not require access to personal data while FHIR infrastructure can make health data available, irrespective of the IT system used by the organisation. The availability of health data will support research across NHS organisations that will directly benefit patients and clinical staff by improving knowledge, communication and healthcare management, and will support future developments such as artificial intelligence, without personal data being shared with technology companies that can provide these services.

大奥蒙德街医院是一家专业儿童中心，它建立了一个数字研究部门，包括NHS临床医生、UCL学者和行业合作伙伴。我们的目标是通过优化临床数据的使用来改善儿童健康。该项目将展示如何在整个研究周期中使用和跟踪常规收集的不可识别的患者数据。我们将利用云平台和新技术（“快速医疗互操作性资源”;FHIR）收集和安全处理医疗数据。这种方法将支持为患者、家庭和医疗保健专业人员开发各种医疗保健“应用程序”，类似于数百万人经常使用的现有手机应用程序。在整个项目中，数据以安全可靠的方式收集、访问和使用。SMART应用程序的开发不需要访问个人数据，而FHIR基础设施可以提供健康数据，无论组织使用的IT系统如何。健康数据的可用性将支持NHS组织的研究，通过改善知识，沟通和医疗保健管理直接使患者和临床工作人员受益，并将支持人工智能等未来发展，而无需与可以提供这些服务的技术公司共享个人数据。

项目成果

An informatics consult approach for generating clinical evidence for treatment decisions.

信息学咨询方法，用于为治疗决策生成临床证据。

• DOI: 10.1186/s12911-021-01638-z
• 发表时间: 2021-10-12
• 期刊: BMC medical informatics and decision making
• 影响因子: 3.5
• 作者: Lai AG;Chang WH;Parisinos CA;Katsoulis M;Blackburn RM;Shah AD;Nguyen V;Denaxas S;Davey Smith G;Gaunt TR;Nirantharakumar K;Cox MP;Forde D;Asselbergs FW;Harris S;Richardson S;Sofat R;Dobson RJB;Hingorani A;Patel R;Sterne J;Banerjee A;Denniston AK;Ball S;Sebire NJ;Shah NH;Foster GR;Williams B;Hemingway H
• 通讯作者: Hemingway H

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes.

• DOI: 10.1038/s41598-023-32469-9
• 发表时间: 2023-06-20
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Rangelov, Bojidar;Young, Alexandra;Lilaonitkul, Watjana;Aslani, Shahab;Taylor, Paul;Guomundsson, Eyjolfur;Yang, Qianye;Hu, Yipeng;Hurst, John R.;Hawkes, David J.;Jacob, Joseph
• 通讯作者: Jacob, Joseph

Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

• DOI: 10.1136/archdischild-2022-323822
• 发表时间: 2022-12
• 期刊: Archives of disease in childhood
• 影响因子: 5.2

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries.

• DOI: 10.1001/jamanetworkopen.2021.12596
• 发表时间: 2021-06-01
• 期刊: JAMA network open
• 影响因子: 13.8
• 作者: Bourgeois FT;Gutiérrez-Sacristán A;Keller MS;Liu M;Hong C;Bonzel CL;Tan ALM;Aronow BJ;Boeker M;Booth J;Cruz Rojo J;Devkota B;García Barrio N;Gehlenborg N;Geva A;Hanauer DA;Hutch MR;Issitt RW;Klann JG;Luo Y;Mandl KD;Mao C;Moal B;Moshal KL;Murphy SN;Neuraz A;Ngiam KY;Omenn GS;Patel LP;Jiménez MP;Sebire NJ;Balazote PS;Serret-Larmande A;South AM;Spiridou A;Taylor DM;Tippmann P;Visweswaran S;Weber GM;Kohane IS;Cai T;Avillach P;Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
• 通讯作者: Consortium for Clinical Characterization of COVID-19 by EHR (4CE)

Evolving phenotypes of non-hospitalized patients that indicate long COVID.

• DOI: 10.1186/s12916-021-02115-0
• 发表时间: 2021-09-27
• 期刊: BMC medicine
• 影响因子: 9.3
• 作者: Estiri H;Strasser ZH;Brat GA;Semenov YR;Consortium for Characterization of COVID-19 by EHR (4CE);Patel CJ;Murphy SN
• 通讯作者: Murphy SN