Utilizing the Electronic Health Records to Create a Syndromic Staff Surveillance System During the COVID-19 outbreak

Jean, Sim Xiang Ying; Conceicao, Edwin Philip; Wee, Liang En; Aung, May Kyawt; Wei, Sylvia Seow Yi; Yang, Raymond Teo Chee; Qing, Goh Jia; Ting, Dennis Yeo Wu; Jyhhan, Kuo Benjamin; Lim, John Wah; Gan, Wee Hoe; Ling, Moi Lin; Venkatachalam, Indumathi

OBJECTIVES: Since December 2019, COVID-19 has caused a worldwide pandemic and Singapore has seen escalating cases with community spread. Aggressive contact tracing and identification of suspects has helped to identify local community clusters, surveillance being the key to early intervention. Healthcare workers have contracted COVID-19 infection both at the workplace and community. We aimed to create a prototype staff surveillance system for the detection of acute respiratory infection (ARI) clusters amongst our healthcare workers (HCWs) and describe its effectiveness. METHODS/RESULTS: A prototypical surveillance system was built on existing electronic health record infrastructure. Over a 10-week period, we investigated 10 ARI clusters amongst 7 departments. One of the ARI clusters was later determined to be related to COVID-19 infection. We demonstrate the feasibility of syndromic surveillance to detect ARI clusters during the COVID-19 outbreak. CONCLUSION: The use of syndromic surveillance to detect ARI clusters amongst HCWs in the COVID-19 pandemic may enable early case detection and prevent onward transmission. It could be an important tool in infection prevention within healthcare institutions.