Overview

Health equity has been studied extensively but has not been solved because it involves a series of complex social, structural, and political conditions. These conditions influence the environments in which people are born, grow, live, work and age (WHO). Individually, these conditions are measurable, and to varying degrees manageable, but understanding the complex relationship and interdependencies between these influencers of health equity has been historically challenging.

Increasingly, however, we have data and analytical techniques that allows us to demystify the issue of health equity, and most importantly, to generate actionable insights that allow us to make a difference. Artificial Intelligence, in particular, offers a way to capitalize on data to discover meaningful data patterns, disentangle the influence of social determinants of health, and provide the foundation for proactive ways to mitigate and prevent health inequity in all its forms.

But for AI to succeed in this challenge, we need more than data, and we need more than talented data scientists. We need a cross-community movement that is aligned around an AI adoption strategy to drive change management, engender trust in the digital solution, and present AI results in a way that empowers decision makers and frontline health care providers with the insights and direction needed to make a difference.

This year, LMI and the University of Virginia (UVA) School of Data Science are teaming up to bring an entrepreneurship competition to Grounds. This challenge calls on innovators to spur progress around health equity solutions through the design of artificial intelligence (AI) to drive insights that are coupled with adoption strategies. Following the motto of “if users don’t trust it, they won’t adopt and use it,” this competition seeks to deliver actionable solutions to impact health equity while also demonstrating how the full potential of advanced analytics and AI can only be achieved when coupled with robust change management and adoption strategies.