AI-Drafted Replies to Patient Portal Messages

Large-scale use started in 2023, when Epic and Microsoft began offering GPT-4-based draft replies through Azure OpenAI. Early adopters included UC San Diego Health, UW Health and Stanford Health Care. In a typical setup, the system reads a patient message and some chart context, then shows a suggested reply. The clinician can start from the draft or start blank. Nothing goes to the patient automatically. The research is mixed. A 2023 study by Ayers and colleagues in JAMA Internal Medicine compared ChatGPT answers with physician answers to public health questions posted on Reddit. Evaluators preferred the chatbot answers in most comparisons and rated them higher for quality and empathy. That setting was not a real clinical inbox. Studies inside health systems give a more modest picture. A Stanford study by Garcia and colleagues in JAMA Network Open in 2024 found clinicians used drafts for about a fifth of messages, with reductions in task load and burnout scores but no significant time savings. A 2024 UC San Diego study by Tai-Seale and colleagues found no drop in reply time, longer time spent reading messages, and longer replies. Safety research shows why review matters. A 2024 simulation study at Mass General Brigham, published in Lancet Digital Health, found that a small share of unedited GPT-4 drafts could have caused severe harm if sent. Physicians who used drafts sometimes left the model's content in place. That is automation bias: people tend to accept a fluent draft. The main misconception is that drafting is mainly a time-saver. The evidence so far points more to reduced mental burden than to saved minutes. Disclosure is becoming a legal issue too. California's AB 3030, effective in 2025, requires health providers to disclose AI-generated patient communications unless a licensed provider reviewed them.