Venkatachalam Indumathi, Conceicao Edwin Philip, Sim Jean Xiang Ying, Whiteley Sean Douglas, Lee Esther Xing Wei, Lim Hui San, Cheong Joseph Kin Meng, Arora Shalvi, Fang Andrew Hao Sen, Chow Weien
Department of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore.
Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.
Mayo Clin Proc Digit Health. 2023 May 15;1(2):172-184. doi: 10.1016/j.mcpdig.2023.04.001. eCollection 2023 Jun.
To develop an electronic surveillance system that provides prompt in-depth situational infectious disease risk and linkage analysis for inpatients in a tertiary hospital.
All patients admitted to Singapore General Hospital (SGH), a 1900-bedded tertiary care hospital, are included in routine surveillance. The 3-Dimensional Disease Outbreak Surveillance System (3D-DOSS) was developed to spatiotemporally represent inpatient surveillance data on a "digital twin" of SGH and evaluated for performance in surveillance, contact tracing, and outbreak investigations. This study was conducted over a 12 month period (October 1, 2020 to September 30, 2021).
The 3D-DOSS surveillance module identified an influenza cluster of 10 inpatients in November 2018, mapping retrospective data between September 2018 and December 2018. Seventy-six clusters of 2 or more linked patients with health care-associated carbapenemase-type carbapenemase-producing were detected in SGH in 2 years (2018 and 2019). The 3D-DOSS contact tracing module promptly identified 44 primary and 162 secondary inpatient contacts, after exposure to a health care worker with coronavirus disease 2019 in April 2021. For outbreak mapping, 24 patients with OXA-48 were mapped on October 22, 2020, using 3D-DOSS to determine their spatiotemporal distribution.
The integration of health care data and representation on a virtual hospital digital twin is a useful tool in an outbreak alert and response framework. Infectious disease surveillance systems, which are syndrome-based, that can access real-time data, and can incorporate movement networks, can potentially enhance health care-associated infection prevention and preparedness for disease X.
开发一种电子监测系统，为三级医院的住院患者提供及时、深入的传染病风险态势及关联分析。
纳入新加坡总医院（SGH）所有住院患者，这是一家拥有1900张床位的三级医疗机构，进行常规监测。开发了三维疾病暴发监测系统（3D-DOSS），以便在SGH的“数字孪生”上对住院患者监测数据进行时空呈现，并对其在监测、接触者追踪和暴发调查方面的性能进行评估。本研究为期12个月（2020年10月1日至2021年9月30日）。
3D-DOSS监测模块在2018年11月识别出10例住院患者的流感聚集性病例，绘制了2018年9月至2018年12月的回顾性数据。在2年时间里（2018年和2019年），SGH检测到76个由2名或更多名与医疗保健相关的产碳青霉烯酶型碳青霉烯酶的关联患者组成的聚集性病例。2021年4月，一名医护人员感染2019冠状病毒病后，3D-DOSS接触者追踪模块迅速识别出44名主要住院接触者和162名次要住院接触者。对于暴发情况绘制，2020年10月22日使用3D-DOSS对24例携带OXA-48的患者进行了绘制，以确定其时空分布情况。
将医疗保健数据整合并呈现在虚拟医院数字孪生上是暴发预警和应对框架中的一个有用工具。基于综合征、能够获取实时数据并能纳入移动网络的传染病监测系统有可能加强医疗保健相关感染的预防以及对未知疾病X的应对准备。
