Special Topic Burnout: An AI-Powered Strategy for Managing Patient Messaging Load and Reducing Burnout

Objective: This study aims to evaluate the impact of using a large language model (LLM) for generating draft responses to patient messages in the electronic health record (EHR) system on clinicians and support staff workload and efficiency. Methods: We partnered with Epic Systems to implement OpenAI's ChatGPT 4.0 for responding to patient messages. A pilot study was conducted from August 2023 to July 2024 across 13 ambulatory specialties involving 323 participants, including clinicians and support staff. Data on draft utilization rates and message response times were collected and analyzed using statistical methods. Results: The overall mean generated draft utilization rate was 38%, with significant differences by role and specialty. Clinicians had a higher utilization rate (43%) than scheduling staff (33%). Draft message usage significantly reduced all users' message response time (13 seconds on average). Support staff experienced a more substantial and statistically significant time saving (23 seconds) compared to negligible time savings seen by clinicians (3 seconds). Variability in utilization rates and time savings was observed across different specialties. Conclusion: Implementing LLMs for drafting patient message replies can reduce response times and alleviate message burden. However, the effectiveness of AI-generated draft responses varies by clinical role and specialty, indicating the need for tailored implementations. Further development and personalization of AI (Artificial Intelligence) tools are recommended to maximize their utility and ensure safe and effective use in diverse clinical contexts. Keywords: Generative Artificial Intelligence Clinician Workload Patient Messaging Burnout Reduction Electronic Health Records (EHR)

Publication History

Received: 31 October 2024

Accepted after revision: 04 April 2025

Accepted Manuscript online:
08 April 2025