Objective: To provide a scoping review of studies on empathy recognition in text using natural language processing (NLP) that can inform an approach to identifying physician empathic communication over patient portal messages.
Materials and methods: We searched 6 databases to identify relevant studies published through May 1, 2023. The study selection was conducted through a title screening, an abstract review, and a full-text review. Our process followed the PRISMA-ScR guidelines.
Results: Of the 2446 publications identified from our searches, 39 studies were selected for the final review, which summarized: (1) definitions and context of empathy, (2) data sources and tested models, and (3) model performance. Definitions of empathy varied in their specificity to the context and setting of the study. The most common settings in which empathy was studied were reactions to news stories, health-related social media forums, and counseling sessions. We also observed an expected shift in methods used that coincided with the introduction of transformer-based models.
Discussion: Aspects of the current approaches taken across various domains may be translatable to communication over a patient portal. However, the specific barriers to identifying empathic communication in this context are unclear. While modern NLP methods appear to be able to handle empathy-related tasks, challenges remain in precisely defining and measuring empathy in text.
Conclusion: Existing work that has attempted to measure empathy in text using NLP provides a useful basis for future studies of patient-physician asynchronous communication, but consideration for the conceptualization of empathy is needed.
Keywords: communication; empathy; machine learning; natural language processing.
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.