Covid-19: Early identification and monitoring of the 2019 novel Coronavirus (Covid-19) disease community spread

缺乏医疗保健信息技术来利用电子健康记录信息(EHR)进行有用的洞察，锁定了EHR系统的好处。例如，新冠肺炎是一种意外爆发，会导致鼻子、喉咙和肺部感染，并可能导致死亡。在加拿大，大约有55,000个新冠肺炎。新冠肺炎给加拿大的医疗体系带来了复杂的挑战和负担，因为它的突发性和潜在的生命损失概率很高。目前，由于缺乏能够实现对这种疾病的远程识别和监测的技术，在制定更大规模的新冠肺炎病早期患者识别策略方面存在知识缺口，而无需在医院对患者进行亲自检查。这项研究的目标是：1)开发创新的预测分析算法，利用鼻咽拭子数据分析患者的电子心率，以发现未诊断的有传播新冠肺炎风险的患者。为实现这一目标，加拿大电子病历系统将通过以下方式实现：1)从加拿大电子病历系统中存在的多个异质数据集中正确提取和集成新冠肺炎的语义信息；2)利用提取的信息开发新的预测算法；3)开发新的算法来估计患者传播疾病的概率；3)开发动态群集算法，将患者归入具有不同新冠肺炎传播概率(例如低、高)的同质组中；2)开发一个创新平台，将预测分析算法与不同的医疗数据库整合到一个共享决策机制中。医生和政策制定者可以使用共享的决策机制来共享信息，并就如何控制疾病的传播做出决定。

The lack of health care information technology to leverage Electronic Health Records information (EHR) into useful insights locks the benefits of EHR systems. For example, COVID-19 is an unexpected outbreak that causes infections of the nose, throat and lungs and may cause death. In Canada, there are approximately 55,000 COVID-19. COVID-19 presents a sophisticated challenge and burden to the Canadian health care system because of its unexpected nature and high probability of potential life-loss. Currently, there is a knowledge gap in the development of early patient identification strategies of COVID-19 disease on a larger scale without in-person examination of patients at hospitals, due to the lack of technology to enable the remote identification and monitoring of this disease. The objectives of this study are: 1) The development of innovative predictive analytics algorithms to analyze patient EHR with nasopharynx swabs data to find undiagnosed patients at risk of spreading COVID-19. This objective will be achieved by correctly extracting and integrating COVID-19 semantic information from the multiple heterogeneous datasets that exist in the Canadian EHR systems, develop new predictive algorithms utilizing the extracted information, develop a new algorithm to estimate the probability of a patient to spread the disease, and develop a dynamic clustering algorithm to cluster patients into homogenous groups of different COVID-19 spreading probability (e.g., Low, High), 2)The development of an innovative platform to integrate predictive analytics algorithms with different medical databases into a shared decision-making mechanism. The shared decision-making mechanism can be used by physicians and policymakers to share information and work toward decisions about how to control the spread of the disease.