Utilizing the electronic health records to create a syndromic staff surveillance system during the COVID-19 outbreak

Highlights

• It is feasible to use EHR to create a surveillance system to identify ARI clusters.

• This requires combination of clinical and geospatial variables mapped over time.

• This may aid surveillance efforts during the COVID-19 pandemic.

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 (HCWs) 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 HCWs and describe its effectiveness.

Methods

A prototypical surveillance system was built on existing electronic health record infrastructure.

Results

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.