Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions

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.