Leveraging ChatGPT and Contextual Frameworks for Optimal SPIKES protocol implementation in Ophthalmology, Gynecology, and Palliative Care

Abstract

The SPIKES protocol serves as a standard method for delivering bad news within healthcare environments. This study explores the integration of ChatGPT with varying contextual prompts to enhance communication between patients and healthcare providers and to gauge patient perception across three distinct scenarios in: ophthalmology, gynecology, and palliative care. In the first two scenarios, the inclusion of more intricate contextual elements in the prompts resulted in more favorable responses, both in terms of response length and recipient perception. Conversely, in the third scenario, responses did not vary significantly between the two generated contexts. While this may indicate a potential limitation of ChatGPT, it also highlights a positive perception regarding the utilization of the SPIKES protocol. In situations where experience and established methodologies for delivering bad news are lacking, leveraging language model frameworks like ChatGPT proves to be a viable option, particularly showing positive perceptual feedback.

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