Employee contact tracing helps Mount Sinai control the spread of COVID-19

New York City health system officials 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 from 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 name of Research Electronic Data. Capture.

The tool is intended to track and reduce the spread of the virus in Mount Sinai’s health system, which includes eight hospitals and more than 400 outpatient clinics.

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

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

The REDCap Employee Health COVID-19 Registry provides secure, easy-to-use forms for collecting employee health data 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 resulted in 50,000 employee interviews and more than 500 framework reviews, according to the researchers.

The registry is available through internet-connected mobile and desktop devices, and remote access enables integration with all Mount Sinai Health System clinics and hospitals. Web forms enable quick tracking of employee health services.

The contact tracing feature captures employee demographics, length of quarantine, personal protective equipment used by the employee, and recent COVID-19 testing history. An exposure matrix provides risk scores based on the type of exposure. According to the researchers, supervised machine learning predicts exposure outcomes.

The registry has allowed Mount Sinai Employee Health Services to reduce case follow-up times from days to hours.

Scott Mace is a contributing writer for HealthLeaders.