RTLS helps enhance contact tracing of high-risk COVID-19 cases, South Korean study finds


A study in a South Korean university hospital has pointed to the potential of using a real-time location system in preventing the spread of infectious diseases such as COVID-19.

A research team from the Department of Infectious Diseases at Yongin Severance Hospital, a university hospital in the city of Yongin, conducted a study to compare the effectiveness and validity of RTLS with a conventional contact tracing method for identifying high-risk contacts of COVID-19-positive patients at the hospital.


The study identified over 1,000 contact cases of confirmed COVID-19 patients in the first quarter of the year.

The findings, which have been published in the Journal of Medical Internet Research, revealed that the RTLS has higher sensitivity in detecting high-risk contact cases (60%) compared to a conventional contact tracing method (46.8%), which involves an in-person interview and a review of EMR and surveillance camera feeds. 

Moreover, the RLTS has shown an 8.1% secondary transmission rate among the identified cases while the conventional method has shown 5.3%. 


There has been a growing demand for technologies that assist already overstretched hospitals in reducing the burden of infection control during the pandemic.  

While the study has shown that the sensitivity value of RTLS in identifying secondary transmissions is “not acceptably high” for a single contact tracing method, it can still benefit hospitals as a complementary tool to conventional methods of contact tracing, “especially when individuals share rooms with each other and under the influence of highly transmissible diseases,” the study’s authors said in their conclusion.


In the early months of the pandemic, the Tan Tock Seng Hospital in Singapore also incorporated RTLS into its COVID-19 response to better handle the surge of patients. It provided RTLS tags to patients, visitors and staff for contact tracing, and used the same technology to track equipment in real time.


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