ArticleApplied clinical informatics2025
An AI-Powered Strategy for Managing Patient Messaging Load and Reducing Burnout.
Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Guardrails for GenAI drafted replies in patient portal messaging.NPJ digital medicine · 2026Article
- Providing Beyond Clinic Hours: A Single-center Analysis of Patient Portal Communication in Pediatric Orthopaedic Care.Journal of the Pediatric Orthopaedic Society of North America · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.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.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.Implementing LLMs for drafting patient message replies can reduce response times and alleviate message burden. However, the effectiveness of artificial intelligence (AI)-generated draft responses varies by clinical role and specialty, indicating the need for tailored implementations. Further investigation into this variability, and development and personalization of AI tools are recommended to maximize their utility and ensure safe and effective use in diverse clinical contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.