Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.

Abstract

Intelligent systems for self-management can help patients and improve quality of life. However, designing AI-based systems is challenging because designers need to account not only for user needs, but also for capabilities and practical constraints of underlying algorithms. We propose and implement a human-centered AI framework to align human and technological requirements and constraints that can guide design of intelligent systems for personal health. We use concepts from a machine learning technique, reinforcement learning, to elicit user needs, through directed content analysis of user interviews, and uncover practical data constraints, through analysis of "in the wild" user engagement logs from a self-monitoring app. We gather and triangulate human-machine-data requirements for a self-management tool for individuals with endometriosis - a poorly understood, complex chronic condition with no reliable treatment. We present recommendations for developing a system that aligns with needs, capabilities, and constraints from human user, data, and machine learning perspectives.