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EMPLOYEE CONTACT TRACING HELPS MOUNT SINAI CONTROL COVID-19 SPREAD

Analysis  |  By Scott Mace  
|   November 08, 2022


TOPICS

COVID-19
digital health
infection
innovation
population health
strategy
technology
workplace


OFFICIALS AT THE NEW YORK HEALTH SYSTEM SAY FUTURE PANDEMIC TRACKING WILL
BENEFIT FROM THIS DIGITAL FRAMEWORK.

Mount Sinai has shared details of a new employee contact tracing database
developed to control the spread of COVID-19.



Writing in the November issue of The Lancet Digital Health, researchers form the
New York-based health system describe the creation of the Mount Sinai Employee
Health COVID-19 REDCap Registry, a cloud-based digital framework using a web
application known as Research Electronic Data Capture.



The tool is intended to track and reduce the spread of the virus across the
Mount Sinai Health System, which includes eight hospitals and more than 400
outpatient clinics.







The database powering the tool assigns unique identification codes for each
exposure without intentionally linking each exposure to previous events for that
same person or department.





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In this way, Mount Sinai can associate events to assist investigations in
identifying patterns of the disease's spread. This design also adjusts and
responds to changes in the COVID-19 disease as variants such as delta and
omicron emerge.



The Employee Health COVID-19 REDCap Registry provides secure, easy to use forms
for employee health collection and workflow-monitored contact-tracing
information for employees. It also provides qualitative analysis of employee
interviews and integrated genomic sequencing.



So far, the initiative has yielded 50,000 employee interviews and more than 500
framework revisions, according to researchers.



The registry is available through mobile and desktop devices connected to the
internet, and remote access allows integration at all Mount Sinai Health System
clinics and hospitals. The web forms enable swift follow-up from employee health
services.







The contact-tracing function captures employee demographics, length of
quarantine, which personal protective equipment the employee used, and a recent
history of testing for COVID-19. An exposure matrix provides risk scores based
on the type of exposure. Supervised machine learning predicts exposure outcomes,
according to the researchers.



The registry allowed Mount Sinai employee health services to trim case follow-up
times from days to hours.







Scott Mace is a contributing writer for HealthLeaders.







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TAGGED UNDER:

COVID-19
digital health
infection
innovation
population health
strategy
technology
workplace

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