Use of a Real-Time Locating System for Contact Tracing of Health Care Workers During the COVID-19 Pandemic at an Infectious Disease Center in Singapore: Validation Study

Ho, Hanley J.; Zhang, Xiaozhu; Huang, Zhilian; Aung, Aung Hein; Lim, Wei‐Yen; Chow, Angela

doi:10.2196/19437

Cited by 73 publications

(63 citation statements)

References 22 publications

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“…Concurrently, RTLS tags are enabled with tag-tag radio-frequency identification technology to determine close contacts of <=2 meters between staff and patients. Although the location-based radio-frequency identification system in the NCID has been validated by Ho et al [ 12 ], the performance of tag-based technology has yet to be assessed.…”

Section: Methodsmentioning

confidence: 99%

“…Hellmich et al [ 11 ] were able to identify twice as many potential contacts of confirmed pertussis cases in an emergency department with RTLS compared with EMR review. Ho et al [ 12 ] also found that the use of location-based RTLSs to complement EMR review increased the sensitivity of detecting health care workers’ contact with COVID-19 inpatients from 47.2% to 77.8%. However, despite the positive outcomes of using RTLSs for contact tracing [ 11 - 13 ], such technologies have largely been confined to health care institutions due to the high costs of infrastructure setup [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study

Huang¹,

Guo²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

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Background Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app—TraceTogether—in March 2020 to augment Singapore’s contact tracing capabilities. Objective This study aims to compare the performance of the contact tracing app—TraceTogether—with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. Methods All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians’ 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. Results RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. Conclusions TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study

Huang¹,

Guo²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

show abstract

“…4 Within local institutions, use of real time location tracking devices and institutional temperature recording systems have been utilized as a method of identifying at risk individuals. 5 , 6 Surveillance is one of the key pillars of infection prevention, enabling early detection and institution of downstream practices that allow reduction in pathogen spread and containment of infection. Syndromic surveillance may allow for an even earlier mobilization of response before the causative pathogen is identified and enhance disease containment.…”

Section: Introductionmentioning

confidence: 99%

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

Sim

Wee

Seow

et al. 2021

American Journal of Infection Control

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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.

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“…Given the scale of the response required, technological advances can complement traditional contact tracing methods to introduce efficiencies needed to successfully avert ongoing transmission [ 15 ]. Digital contact tracing using real-time locator systems, including downloadable apps, has gained traction as a direct-to-consumer approach to efficiently identify individuals who may have come into close contact with persons diagnosed with COVID-19 [ 16 , 17 ]. These systems require broad community acceptance and usage to provide sufficient population-wide coverage, as mathematical modelling estimates suggest that high population coverage is needed to effectively reduce transmission [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…These systems require broad community acceptance and usage to provide sufficient population-wide coverage, as mathematical modelling estimates suggest that high population coverage is needed to effectively reduce transmission [ 18 , 19 ]. These data further suggest that a combination of direct contact tracing with a digital approach has the highest yield in identifying cases that warrant isolation or quarantine to successfully mitigate ongoing transmission [ 16 ]. However, digital privacy remains a concern, and limited voluntary use of contact tracing approaches hampers the utility of these systems [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Deployment of a Free, Privacy-Assured COVID-19 Symptom Tracker for Public Safety During Reopening: System Development and Feasibility Study

Kassaye¹,

Spence²,

Lau³

et al. 2020

JMIR Public Health Surveill

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Background Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of cases of coronavirus disease (COVID-19) in the United States has exponentially increased. Identifying and monitoring individuals with COVID-19 and individuals who have been exposed to the disease is critical to prevent transmission. Traditional contact tracing mechanisms are not structured on the scale needed to address this pandemic. As businesses reopen, institutions and agencies not traditionally engaged in disease prevention are being tasked with ensuring public safety. Systems to support organizations facing these new challenges are critically needed. Most currently available symptom trackers use a direct-to-consumer approach and use personal identifiers, which raises privacy concerns. Objective Our aim was to develop a monitoring and reporting system for COVID-19 to support institutions conducting monitoring activities without compromising privacy. Methods Our multidisciplinary team designed a symptom tracking system after consultation with experts. The system was designed in the Georgetown University AvesTerra knowledge management environment, which supports data integration and synthesis to identify actionable events and maintain privacy. We conducted a beta test for functionality among consenting Georgetown University medical students. Results The symptom tracker system was designed based on guiding principles developed during peer consultations. Institutions are provided access to the system through an efficient onboarding process that uses clickwrap technology to document agreement to limited terms of use to rapidly enable free access. Institutions provide their constituents with a unique identifier to enter data through a web-based user interface to collect vetted symptoms as well as clinical and epidemiologic data. The website also provides individuals with educational information through links to the COVID-19 prevention recommendations from the US Centers for Disease Control and Prevention. Safety features include instructions for people with new or worsening symptoms to seek care. No personal identifiers are collected in the system. The reporter mechanism safeguards data access so that institutions can only access their own data, and it provides institutions with on-demand access to the data entered by their constituents, organized in summary reports that highlight actionable data. Development of the system began on March 15, 2020, and it was launched on March 20, 2020. In the beta test, 48 Georgetown University School of Medicine students or their social contacts entered data into the system from March 31 to April 5, 2020. One of the 48 users (2%) reported active COVID-19 infection and had no symptoms by the end of the monitoring period. No other participants reported symptoms. Only data with the unique entity identifier for our beta test were generated in our summary reports. Conclusions This system harnesses insights into privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. Our pilot study demonstrated feasibility and ease of use. Refinements based on feedback from early adapters included release of a Spanish language version. These systems provide technological advances to complement the traditional contact tracing and digital tracing applications being implemented to limit SARS-CoV-2 transmission during reopening.

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Use of a Real-Time Locating System for Contact Tracing of Health Care Workers During the COVID-19 Pandemic at an Infectious Disease Center in Singapore: Validation Study

Cited by 73 publications

References 22 publications

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study

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

Rapid Deployment of a Free, Privacy-Assured COVID-19 Symptom Tracker for Public Safety During Reopening: System Development and Feasibility Study

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