The state run hotline call center for Covid-19 is setup in one venue, with more than 30 persons present in one room, all risking to be infected/isolated/quarantined if just one person is found as a suspect/positive. Further, the caller is not tracked from service to service, nobody checks if the caller followed the instructions to call other downstream services - Dept of Health, Ambulance Service
What it does
We created an end-to-end service,
- taking hotline calls, providing information and carrying out the triage for suspects/contacts and forwarding them to the appropriate services
- mobile app for self monitoring and self-declaration of symptoms/direct contacts for access to public places / work places - especially medical staff, police, firefighters etc...
Through the volunteer run call center we gather information about people facing difficulty in procuring food and basic goods, as well as people requesting emotional support services.
The needs are relayed to local NGOs and trained psychologists for followup and resolution.
How we built it
We customized the information flow of an existing Telemedicine CRM - EvoMed and embeded it in a VoIP server for distributed call center operations. Further, we've developed a iOS and Android App for self-monitoring and risk triage, providing positive proof that the individual did not have contact with a covid pozitive or suspected individual, nor have any Covid symptoms.
The support network has a hub and spoke model, with distributed communication stations to mitigate the infection risk for volunteers and beneficiaries.
Challenges we ran into
Adoption of solution by authorities. Swift validation of medical process and followup steps for suspects and covid symptomatic individuals.
Accomplishments that we are proud of
Service implementation and user traction at a County Level Health Department. ~8000 monitored beneficiaries ~400 Covid-19 triage and monitoring sessions in the past 10 days.
What's next for 9SOS
App Upload on Apple and Google Stores Worker Cohort triage monitoring with anonymized data for early warning signs and alerts.