Towards shared decision-making in digital public health: Affordances of large language models

Abstract

Shared decision-making (SDM) happens when patients are actively involved in medical decisions. Existing literature fails to show how Large Language Models (LLMs) intervene in this process from the integrated perspectives of patients and physicians. This paper leverages the Affordances Theory and adopts an exploratory approach with log analysis and semi-structured interviews to articulate affordances in a conceptual model. This will enable an understanding of how affordances differ from patients to physicians, and what this implies for SDM. This model will show policy-makers and healthcare professionals how LLMs can enable, facilitate or hinder SDM.