Coronavirus Disease 2019 (COVID-19) is the greatest public health challenge in over a century. Basic questions about its prevention, diagnosis, treatment and prognosis remain unanswered, hindering evidence-based clinical decision-making. This gap is particularly noticeable in Emergency Departments (ED) where the sickest patients present for early assessment and care.

EDs have experienced a surge of patients presenting with flu-like symptoms, most of whom ultimately test negative for COVID-19. In the absence of validated clinical decision rules allowing emergency physicians and nurses to reliably predict who is at high risk, all patients require isolation, and all healthcare personnel must use personal protective equipment, exacerbating national shortages.

Based on US experience, most patients can ultimately be discharged from the ED with fewer than 30% requiring hospitalization. Unfortunately, physicians and nurses lack evidence-based criteria to guide ED discharge decisions. This decision is particularly difficult for vulnerable patients such as the homeless, or those suffering from mental health or substance use disorders who may not be able to self-monitor or self-isolate.

This challenge was highlighted in an outbreak in a BC correctional facility in which Indigenous inmates were disproportionately affected. Without evidence-based criteria, we risk making discharge decisions that put patients and their surrounding communities at risk.

There are also no evidence-based tools to identify patients who would not benefit from intubation and mechanical ventilation. This decision has a profound impact on resource utilization, as patients with COVID-19 often require prolonged ventilator support, and impact critical care capacity.

To address these knowledge gaps, we have developed a 51-site, pan-Canadian network that will allow us to rapidly accrue a sufficiently large dataset to develop, test and implement clinical decision rules for COVID-19 ED patients within one year.

There are currently no ED-based observational studies or registries listed on or the WHO COVID-19 research database that have the aims or capacity to generate risk prediction tools to address these evidence gaps.

This project is also unique in its ability to to link to large national administrative and public health databases, and collect follow-up data on patient-oriented outcomes including self-isolation and quality of life. We are ideally poised to ensure rapid integration of our findings into clinical practice through social media and medical education podcasts.