AUTHOR=Ganju Aakash , Satyan Srini , Tanna Vatsal , Menezes Sonia Rebecca TITLE=AI for Improving Children’s Health: A Community Case Study JOURNAL=Frontiers in Artificial Intelligence VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.544972 DOI=10.3389/frai.2020.544972 ISSN=2624-8212 ABSTRACT=

The Indian health care system lacks the infrastructure to meet the health care demands of the country. Physician and nurse availability is 30 and 50% below WHO recommendations, respectively, and has led to a steep imbalance between the demand for health care and the infrastructure available to support it. Among other concerns, India still struggles with challenges like undernutrition, with 38% of children under the age of five being underweight. Despite these challenges, technological advancements, mobile phone ubiquity and rising patient awareness offers a huge opportunity for artificial intelligence to enable efficient healthcare delivery, by improved targeting of constrained resources. The Saathealth mobile app provides low-middle income parents of young children nflwith interactive children’s health, nutrition and development content in the form of an entertaining video series, a gamified quiz journey and targeted notifications. The app iteratively evolves the user journey based on dynamic data and predictive algorithms, empowering a shift from reactive to proactive care. Saathealth users have registered over 500,000 sessions and over 200 million seconds on-app engagement over a year, comparing favorably with engagement on other digital health interventions in underserved communities. We have used valuable app analytics data and insights from our 45,000 users to build scalable, predictive models that were validated for specific use cases. Using the Random Forest model with heterogeneous data allowed us to predict user churn with a 93% accuracy. Predicting user lifetimes on the mobile app for preliminary insights gave us an RMSE of 25.09 days and an R2 value of 0.91, reflecting closely correlated predictions. These predictive algorithms allow us to incentivize users with optimized offers and omni-channel nudges, to increase engagement with content as well as other targeted online and offline behaviors. The algorithms also optimize the effectiveness of our intervention by augmenting personalized experiences and directing limited health resources toward populations that are most resistant to digital first interventions. These and similar AI powered algorithms will allow us to lengthen and deepen the lifetime relationship with our health consumers, making more of them effective, proactive participants in improving children’s health, nutrition and early cognitive development.