Patient Perspectives on AI-Drafted Electronic Portal Messages

Importance: Patient portal messaging volume has increased substantially and is linked to clinician burnout. In response, health systems are increasingly adopting large language models (LLMs) to generate draft replies to patient messages, yet little is known about how patients interpret and evaluate artificial intelligence (AI) involvement in this communication channel. Objective: To understand how patients perceive AI-drafted responses to electronic patient portal messages and to identify implications for patient-centered implementation. Design, Setting, and Participants: This qualitative study used vignette-based prompts mirroring scenarios from a prior survey on patient preferences regarding AI-drafted online messaging. Participants included a sample of 40 adult patients from a large academic health system who had previously been surveyed about AI-drafted portal messaging. Recruitment sought increased representation of individuals from minoritized racial and ethnic groups, older patients, and participants with lower satisfaction with AI-drafted messaging in prior survey responses. Data were collected between April and August 2025. Exposure: One-time interviews conducted via videoconference. Interviews included vignettes and draft messages varying in tone, length, and implied authorship, alongside questions about messaging preferences, AI, and health care. Main Outcomes and Measures: Patient perspectives regarding (1) portal messaging preferences, (2) comfort with AI-drafted replies, (3) preferred message tone and content, and (4) AI disclosure expectations. Results: This study of 40 patients included 30 women (75.0%), 9 men (22.5%), 14 Black or African American participants (35.0%), 13 White participants (32.5%), and 13 patients aged 65 years or older (32.5%). Patients described portal messaging as transactional, prioritizing timely problem resolution over relational depth. Patients expressed high comfort with AI-drafted messages, conditional on clinician oversight. Patient preferences for tone and expressions of empathy varied across individuals and contexts and were driven less by whether messages appeared AI-like or human-like than by whether tone, length, and detail fit the purpose and stakes of the communication. Participants broadly endorsed AI disclosure, describing transparency as important for trust, but differed in preferred disclosure timing and format. Conclusions and Relevance: In this qualitative study of patient perspectives on AI-drafted portal messaging, patients supported AI use when it improved communication efficiency, so long as clinicians reviewed and remained accountable for messages. Implementation should prioritize clinician oversight, context-sensitive communication standards, standardized disclosure practices, and monitoring for downstream effects on quality, equity, and patient trust and safety.