Digital health communication beyond computations
A distributed and phenomenological approach
DOI:
https://doi.org/10.7146/qhc.157438Keywords:
artificial intelligence (AI), digital health communication, distributed language, embodiment, empathy, large language models (LLMs)Abstract
Background: AI-driven Large Language Models (LLMs) are increasingly introduced in the healthcare sector. Recently, research has been exploring their potential in digital health communication contexts. For example, chatbots are presented as possible solutions to manage the increased workload associated with digital health communication. Aim: To illustrate how humans engage in digital health communication in embodied and distributed ways and to use these insights as a lens for reflecting on potential challenges that may arise when LLMs are introduced into digital health communication practices. Methods: Drawing on our ethnographic research in digital health communication conducted in a Danish healthcare context (a nursing home and general practice setting), we present two empirical examples: (1) how a nurse in a nursing home reads a digital correspondence from a GP and (2) how a GP in the clinic writes an e-consultation to a patient. Results: Our examples demonstrate how digital health communication involves embodied, distributed, and empathic engagement beyond computational processes. Discussion: Discursively juxtaposing computational language and synthetic empathy with real-life situated behaviour risks neglecting central aspects of human language and the relational dynamics emerging in socio-technical relations. Conclusion: Understanding how embodied and distributed processes are fundamentally tied to digital health communication adds to a nuanced assessment of LLMs’ potentials and caveats when used in clinical contexts.
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