Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions
Man Luo, Christopher J. Warren, Lu Cheng, Haidar Abdul‐Muhsin, Imon Banerjee
Abstract
The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support through the development of empathetic, patient-facing chatbots. This study investigates an intriguing question Can ChatGPT respond with a greater degree of empathy than those typically offered by physicians? To answer this question, we collect a de-identified dataset of patient messages and physician responses from a hospital and generate alternative replies using ChatGPT. We then introduce a set of empathy ranking evaluation (EMRank) metrics to automatically judge the empathy degree. We further conduct human study to gauge the empathy level of responses. Our findings indicate that LLM-powered chatbots have the potential to surpass human physicians in delivering empathetic communication, suggesting a promising avenue for enhancing patient care and reducing professional burnout. To summary, this study not only highlights the importance of clinical empathy in patient interactions but also proposes a set of automatic empathy ranking metrics, paving the way for the broader adoption of LLMs in healthcare.