ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department
Luyang Luo, Emma Chen, Xiaoman Zhang, Julián Acosta, Boyang Tom Jin, Fatma Gunturkun, Christian C. Rose, Carl M. Preiksaitis, Brian P. Suffoletto, Pranav Rajpurkar, David A. Kim · 2025
This paper presents ED-Explain, an integrated, AI-driven system that transforms emergency department (ED) discharge instructions and electronic health records into personalized video presentations featuring a virtual healthcare provider. By leveraging multimodal ED data, large language models, and video generation, we aimed to produce accessible discharge instructions tailored to patients' ED visits. Four board-certified Emergency Medicine physicians reviewed 39 pairs of original and ED-Explain-produced discharge instructions. AI video summaries received significantly higher (p<0.001) average ratings (1-5) of completeness (4.1 vs. 3.1), correctness (3.9 vs. 3.5) and patient accessibility (3.4 vs. 2.9). Physicians expressed reservations about 13.3% of ED-Explain's discharge instructions for patient viewing, and only 4.6% of ED-Explain's instructions were found inappropriate for use with patients. Physician feedback suggests that AI-enhanced video discharge summaries have potential to improve communication of discharge information to ED patients, though patient-centered evaluation is needed. This work contributes to the growing field of AIassisted healthcare communication and offers insights into the potential for AI to improve physician-patient communication and patient self-efficacy.