What patients want from healthcare chatbots: insights from a mixed-methods study.

Abstract

Objectives: To understand whether patients prefer chatbots for certain tasks in healthcare, and their motivations for doing so, recognizing that chatbots are already assisting patients with various healthcare tasks.

Materials and methods: We conducted a mixed-methods study with patient-users of a healthcare system multi-task chatbot integrated in an electronic health record. We purposively oversampled by race or ethnicity to survey 617/3089 (response rate, 20.0%) chatbot users using de novo and validated survey items. We conducted semi-structured interviews with 46 patient-users and 2 chatbot developers between November 2022 and May 2024. We used modified grounded theory to analyze interviews, descriptive statistics and Chi-square tests to compare survey results, and mixed-methods techniques to integrate findings.

Results: Patient-users preferred chatbots for administrative tasks to save providers' time, because of the chatbot availability, and to avoid unpleasant interactions. Some preferred to discuss sensitive tasks (such as mental health or gender-affirming care) with chatbots due to more privacy or anonymity and less embarrassment or judgment. Developer interviews corroborated this finding. Avoiding bias and using a preferred means of communication applied to all tasks. In surveys, patient-users were less likely to worry about being judged based on chatbot interactions (153/608, 25.2%) compared to interactions with a doctor (219/606, 36.1%) (P < .001). Patient-users preferred human clinicians for diagnostic tasks.

Discussion: Patient-users appear to simultaneously prefer chatbots for simple tasks or sensitive ones, with diverse motivations. Whether chatbots best meet patient needs while balancing ethical tensions regarding access, privacy, judgment, and bias is unclear.

Conclusion: Future chatbot design must accommodate different and diverse patient preferences.