Understanding the Human-Computer Interaction of Ambient AI in Healthcare: A Qualitative Study of Early Provider Experiences
Year: 2025
Type: book-chapter
Abstract: Ambient artificial intelligence (AI) documentation tools offer the potential to reduce clinician burden and enhance workflow efficiency. However, limited research has explored provider experiences with ambient AI, particularly from a human-computer interaction (HCI) perspective. To qualitatively evaluate provider experiences one month after implementation of ambient AI documentation tools across a large integrated health system. This was a qualitative thematic analysis embedded within a broader mixed-methods evaluation. Open-ended survey responses were collected from providers who had adopted ambient AI systems (Abridge or DAX) and analyzed using Braun and Clarke’s reflexive thematic analysis approach. Sentiment analysis and interrater reliability assessment supported the rigor of coding. Among 204 survey respondents, 103 provided free-text feedback. Six major themes emerged: (1) improved efficiency and workflow, (2) enhanced patient interaction, (3) documentation quality and editing burden, (4) learning curve and workflow adaptation, (5) specialty-specific performance and suitability, and (6) provider skepticism and trust in AI. While many providers reported time savings and improved patient engagement, concerns persisted around documentation accuracy, editing workload, specialty-specific challenges, and patient privacy. Sentiment analysis revealed a balanced distribution of positive, negative, and mixed perceptions. Early experiences with ambient AI highlight both its promise and its challenges. Successful integration will require specialty-specific customization, enhanced training, transparent data practices, and sustained efforts to build provider trust. Attention to HCI principles and real-world workflow alignment will be critical for broader adoption.
Source: Artificial intelligence
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4412033207