Scaling Performance Graphs and Machine Learning Techniques to 100,000 Frontline Nutrition Workers across India

Organization: 
Dimagi Inc
Organization Location: 
India

A rigorous randomized controlled study conducted by Harvard Medical School, the University of Washington and Dimagi proved the effectiveness of mobile-based performance improvement graphing to increase motivation of frontline workers, translating to a 27 percent bump in the rate of home visits. Performance graphs on the mobile phone create a feedback loop for the frontline worker to track success against key responsibilities; the critical tasks she has yet to complete; and progress against peers. As the critical point of care for their community, nutrition frontline workers require visibility into their performance, both in conducting regular visits with pregnant women and young mothers, and in convincing them to adopt health practices that improve health outcomes. The past year has seen the deployment of the largest mobile-health project to date, with 90,000 nutrition frontline workers across India equipped with a behavioral change counselling and patient tracking application to improve their care of nearly 10 million people. For most pregnant women in this beneficiary population, the nutrition frontline worker is the source of information on healthy living during pregnancy, including proper diet, iron supplements, attention to critical danger signs and readiness for delivery. With Saving Lives at Birth funding, Dimagi proposes to scale up proven performance-improvement techniques to India’s 90,000 nutrition frontline workers. The funding would be used for the developmentand deployment of performance graphs and machine-learning techniques to frontline workers who are currently using a CommCare mobile application.

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