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