Paper Number
1880
Paper Type
Complete Research Paper
Abstract
As global challenges, such as pandemics, population growth and widespread illnesses, continue to rise, healthcare systems are facing greater strain, resulting in a shortage of resources and increased demands for medical care. Effective communication between healthcare professionals and patients is essential for the provision of good services to prevent confusion and induced anxiety of patients, particularly when medical jargon is employed and not understood. Generative AI (GAI) presents a chance to transform healthcare communication by providing language processing capabilities that enhance patient-centered services. This paper examines how GAI-based conversational agents for explaining medical jargon in healthcare should be designed. We derived eleven design principles from a systematic literature review and evaluated them with nine clinical cardiological scenarios through a prototypical instantiation of an LLM-based conversational agent. The results provide insights for researchers and healthcare providers in form of prescriptive design knowledge to improve patient communication using GAI.
Recommended Citation
Strobel, Gero and Banh, Leonardo, "What Did the Doctor Say? Empowering Patient Comprehension with Generative AI" (2024). ECIS 2024 Proceedings. 15.
https://aisel.aisnet.org/ecis2024/track18_healthit/track18_healthit/15
What Did the Doctor Say? Empowering Patient Comprehension with Generative AI
As global challenges, such as pandemics, population growth and widespread illnesses, continue to rise, healthcare systems are facing greater strain, resulting in a shortage of resources and increased demands for medical care. Effective communication between healthcare professionals and patients is essential for the provision of good services to prevent confusion and induced anxiety of patients, particularly when medical jargon is employed and not understood. Generative AI (GAI) presents a chance to transform healthcare communication by providing language processing capabilities that enhance patient-centered services. This paper examines how GAI-based conversational agents for explaining medical jargon in healthcare should be designed. We derived eleven design principles from a systematic literature review and evaluated them with nine clinical cardiological scenarios through a prototypical instantiation of an LLM-based conversational agent. The results provide insights for researchers and healthcare providers in form of prescriptive design knowledge to improve patient communication using GAI.
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