NEWS RELEASE
WELLINGTON-DUFFERIN-GUELPH PUBLIC HEALTH
*************************
The MaRS Discovery District, the world's largest urban innovation hub supporting the health and tech sector, has awarded Wellington-Dufferin-Guelph Public Health $10,000 for being a finalist in phase 1 of the MaRS Health’s Opioid Data Challenge.
This national challenge, in partnership with the Public Health Agency of Canada, asks Canadians to identify innovative ways of using data to better understand and respond to the opioid crisis plaguing Canadian communities.
WDG Public Health submitted its Opioid Overdose Alert System that was developed in collaboration with the Wellington Guelph Drug Strategy and other local partners. The system collects real-time information on opioid-related overdoses and incidents from 10 local organizations and service providers to build a detailed understanding of local patterns.
When abnormal overdose or incident patterns emerge, an alert is triggered to inform the community and allow agencies, substance users, EMS and other stakeholders to respond quickly and appropriately. The system is being piloted in Guelph where six alerts have been issued since July 2018. Using social media, the alerts reach thousands of residents quickly.
“Gathering local data to inform local decisions helps to create a unified response that addresses our community’s unique needs,” said Dr. Nicola Mercer, Medical Officer of Health and CEO for Wellington-Dufferin-Guelph Public Health. “Our hope is to see the proven success of this model help communities across Canada reduce substance use harms and save more lives.”
WDG Public Health will use the $10,000 award to fund phase 2 of the challenge, which will involve extending and testing the methodology and alert system, appropriately named the FAST (fast, accessible, scalable, and timely) Opioid Alert Platform, in other communities across Canada.
The Challenge winners will be announced in late June 2019 with up to two $50,000 awards available. Winners will also have the opportunity to integrate their model into existing public health surveillance systems.
*************************
