Vera
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Vera

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Vera Vera Presentation Transcript

  • vera An SMS/text message health-tracker for underserved demographics. APIs/Technology Used health.data.ny.gov DTII Prevention Tips node.js, coffeescript Firebase Twilio Datasets Used Effect of BMI on Lifetime Risk for Diabetes in the U.S. Health Indicators Warehouse Pew Internet & American Life + unlimited extensibility Built for NYS Health Data Code-a-thon By Ethan Bond, Femi King, and Raymond Jacobson
  • vera Health-tech relies on high-tech. But low-tech communities need it the most. • • • Accessible personal health monitoring for underserved communities Highly extensible platform Utilize and contribute to open datasets me: Vera? ! vera: Hey there, I’m Vera. It’s a pleasure to meet you. What’s your name? For example: ‘name: Robert’
  • vera vera: Ethan, I have to ask you some questions! What’s your age in years? For example: ‘age: 20’ vera: Good evening Ethan! Just checking in. What did you eat today? For example ‘foods: pizza, steak’ me: age: 19 me: foods: salad, steak, green beans ! ! vera: Great! How tall are you in inches? Don’t be shy! For example: ‘height: 60’ ! me: height: 69 ! vera: How much do you weigh in pounds? For example: ‘weight: 215’ ! me: weight: 202 ! vera: Nice! Your BMI is 30 which is obese. Together we can fix that up. To see how, type ‘info’ and learn what I can do. I’ll check back with you tomorrow to get started, so start eating healthy! Type ‘tips’ to learn how. ! ! vera: Great! That’s a definite step up. Keep it up and I’ll be asking for progress in a few days! ! ! ! ! me: get data ! vera: It’s important to see progress over time. For the next day, vera.io/419583 will be available for you to view.
  • vera Health dashboard www.vera.io/419583 Food group intake Your BMI vs NY 9% 34 11% 31.5 38% 29 12% 26.5 24 April June August October 31% You Meat Vegetables Starches Fruits Dairy NY Average
  • vera Extensible, contributory, powerful. • • • • • As natural language processing gets more powerful, so do Vera’s capabilities User-input can be more complex than text. Possibilities including eyetracking, speech records, and photographs This can bring high-tech diagnostic power to the low-tech battlegrounds of healthcare Data can be anonymous to construct a massive database of metrics Freely accessible database allows researchers to test hypotheses against millions of self-reported anonymous data points We’re excited! Built for NYS Health Data Code-a-thon By Ethan Bond, Femi King, and Raymond Jacobson