Addressing the Productivity Paradox in Healthcare with Retrieval Augmented Generative AI Chatbots
Sajani Ranasinghe, Daswin De Silva, Nishan Mills, Damminda Alahakoon, Milos Manic, Yen Ying Lim, Weranja Ranasinghe
Abstract
Artificial Intelligence (AI) is reshaping the health-care landscape through diverse innovations, personalisations and decision-making capabilities. The human-like intelligence of Generative AI has been fundamental in driving this transformation across the sector. Despite large investments and some early successes, several studies have signalled the emergence of a productivity paradox due to inherent limitations of Generative AI that disintegrate within the complexity of healthcare systems and operations. In this study, we investigate the capabilities of Retrieval Augmented Generation (RAG) and Generative AI chatbots in addressing some of these challenges. We present the design and development of a Retrieval Augmented Generative AI Chatbot framework for consultation summaries, diagnostic insights, and emotional assessments of patients. We further demonstrate the technical value of this framework in service innovation, patient engagement and workflow efficiencies that collectively move to address the productivity paradox of AI in healthcare.