Designing AI-based Conversational Agent for Diabetes Care in a Multilingual Context

TL;DR

This paper presents a blueprint for designing AI-based multilingual conversational agents in diabetes care, introducing three design principles and demonstrating their implementation with the AMANDA system, evaluated for speech quality and usability.

Contribution

It proposes three novel design principles for health-related conversational agents and develops AMANDA, a multilingual AI-based diabetes care agent, filling a gap in prescriptive healthcare chatbot design.

Findings

AMANDA achieved high speech quality scores.

System usability scale indicated good usability.

Design principles are applicable to other healthcare domains.

Abstract

Conversational agents (CAs) represent an emerging research field in health information systems, where there are great potentials in empowering patients with timely information and natural language interfaces. Nevertheless, there have been limited attempts in establishing prescriptive knowledge on designing CAs in the healthcare domain in general, and diabetes care specifically. In this paper, we conducted a Design Science Research project and proposed three design principles for designing health-related CAs that embark on artificial intelligence (AI) to address the limitations of existing solutions. Further, we instantiated the proposed design and developed AMANDA - an AI-based multilingual CA in diabetes care with state-of-the-art technologies for natural-sounding localised accent. We employed mean opinion scores and system usability scale to evaluate AMANDA's speech quality and usability, respectively. This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains.