Paper Number
1315
Paper Type
Short
Abstract
Empathy is a crucial element of effective healthcare communication, with profound effects on patient experience and treatment outcomes. However, the increasing pressures on healthcare providers often push empathy to the sidelines. Conversational agents like chatbots offer a possible solution with their potential to integrate empathy and scalability. Yet, disparities in their design and understanding of empathy can lead to less appropriate and satisfying user responses. This research develops a comprehensive framework for empathy in healthcare contexts and assesses its application in text-based healthcare chatbots. Conducted as an online experiment with 1'000 participants who have chronic conditions, the study examines the effects of distinct verbal empathic expressions (i.e., empathic self-awareness and active listening) displayed by a healthcare chatbot in a medical interview scenario. This research aims to foster an effective healthcare environment and improve patient-chatbot-interactions by enhancing the understanding and implementation of digital empathy in healthcare chatbots.
Recommended Citation
Sou, Davinny; Kuhlmeier, Florian Onur; Kowatsch, Tobias; von Wangenheim, Florian; and Nißen, Marcia, "Towards Digital Empathy in Healthcare Chatbots: A Conceptual Framework and Empirical Study" (2024). ICIS 2024 Proceedings. 5.
https://aisel.aisnet.org/icis2024/ishealthcare/ishealthcare/5
Towards Digital Empathy in Healthcare Chatbots: A Conceptual Framework and Empirical Study
Empathy is a crucial element of effective healthcare communication, with profound effects on patient experience and treatment outcomes. However, the increasing pressures on healthcare providers often push empathy to the sidelines. Conversational agents like chatbots offer a possible solution with their potential to integrate empathy and scalability. Yet, disparities in their design and understanding of empathy can lead to less appropriate and satisfying user responses. This research develops a comprehensive framework for empathy in healthcare contexts and assesses its application in text-based healthcare chatbots. Conducted as an online experiment with 1'000 participants who have chronic conditions, the study examines the effects of distinct verbal empathic expressions (i.e., empathic self-awareness and active listening) displayed by a healthcare chatbot in a medical interview scenario. This research aims to foster an effective healthcare environment and improve patient-chatbot-interactions by enhancing the understanding and implementation of digital empathy in healthcare chatbots.
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16-HealthCare