NudgeRank: Digital Algorithmic Nudging for Personalized Health

TL;DR

NudgeRank is a large-scale AI-driven system using graph neural networks and knowledge graphs to deliver personalized health nudges, significantly improving health behaviors and engagement across a broad population.

Contribution

The paper introduces NudgeRank, a novel digital nudging system that combines graph neural networks with knowledge graphs for personalized health recommendations in production.

Findings

6.17% increase in daily steps

7.61% more exercise minutes

13.1% user engagement and enrollment increase

Abstract

In this paper we describe NudgeRank, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRank operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.