• My mom is a public health nurse
  • Only 50% of her time is spent giving Covid-19 vaccines to people
  • The rest of her time is spent calling and collecting information from Covid-19 patients
  • What if she spent 90% of her time giving vaccines instead?
  • What if she could focus on just the patients that really need her help?
  • What if all of her co-workers spent 90% of their time giving vaccines?
  • 100,000+ more people would be vaccinated right now
  • The Covid-19 crisis would be resolved much sooner
  • Less people would be hurt by Covid-19
  • How can we use machine learning, automation and the instant communication the mobile phones in everyone's pockets provide to help front-line medical staff keep their patients safe?
  • This is the inspiration behind VirusValet

What it does

  • VirusValet automates Covid-19 check-ins so public health nurses can focus on giving vaccines and helping the patients that need them the most
  • Nurses add Covid-19 patient profiles to VirusValet
  • VirusValet automates collecting information from patients by texting them using the Twilio messaging API and asking them questions about their health
    • What symptoms do they have?
    • Are they self-isolating? etc.
  • Using the information it collects, VirusValet highlights patients that need the most help using machine learning
  • Nurses can also review the questions the bot has asked the patients and their responses
  • Nurses can also ask further questions to clarify the patients' status and give suggestions

How we built it

  • We used the Twilio messaging API to send text messages from a Python web application
  • We used Google Cloud to speed up the training of our machine learning model and data processing scripts
  • Our web application interfaces with the Twilio messaging API and uses the machine learning model to automate communication with patients and nurses

Challenges we ran into

  • We had great difficulty finding enough data for our machine learning model
  • There were tons of problems that our code ran into when interfacing the machine learning scripts, Twilio messaging API scripts and web application

Accomplishments that we're proud of

  • Learning how to use the Twilio messaging API and interfacing it with our machine learning model and web application
  • Interfacing three very complex applications together and building a product in a weekend

What we learned

  • It is very important to write clean code so it can be easily extended later
  • Code should be broken up into different packages, each of which are built properly
  • This makes it easier to use them and debug them when problems occur
  • A machine learning model is only as good as the data you give it

What's next for VirusValet

  • Gathering more data and making our machine learning model more accurate
  • Improving our web application to allow nurses to use it easier

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