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Updated: Sep 17, 2025

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AI-generated draft replies to patient messages: exploring effects of implementation.

Charlotte M H H T Bootsma-Robroeks1,2, Jessica D Workum3,4, Stephanie C E Schuit5

  • 1Department of Health Information Office, Information Management Healthcare, University Medical Center, Groningen, Netherlands.

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|June 27, 2025
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Summary
This summary is machine-generated.

Large Language Models (LLMs) integrated into Electronic Health Records (EHRs) show 58% physician adoption for drafting patient messages. While not yet saving time, LLM drafts are utilized with significant text similarity, indicating potential for future efficiency gains.

Keywords:
LLM generated draft responsesadoptionelectronic health recordsinbasket messageslarge language model (LLM)time saving

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Electronic Health Records (EHRs) present administrative challenges for clinicians.
  • Large Language Models (LLMs) offer potential solutions to reduce physician workload.
  • Real-world validation of LLM tools in clinical settings is crucial for adoption.

Purpose of the Study:

  • To evaluate the adoption, usage, and time-saving potential of LLM-generated draft responses to patient messages within an EHR system.
  • To assess the impact of LLM integration on physician workflow and response quality.

Main Methods:

  • A prospective observational cohort study involving 100 physicians across 14 specialties in a non-English academic hospital.
  • Physicians chose between LLM-generated draft replies and blank replies for 919 patient messages over 16 weeks.
  • Analysis included adoption rates, text similarity scores (ROUGE-1, BLEU-1), and time spent on message adjustments.

Main Results:

  • Physicians adopted LLM-generated drafts for 58% of patient messages.
  • 43% of adopted drafts showed significant text similarity (≥10% match, ROUGE-1: 86%) to the final sent message.
  • No significant difference in total response time was observed between LLM-assisted and blank replies (153s vs. 157s).

Conclusions:

  • General adoption of LLM-generated draft responses reached 58%, with significant variation across specialties.
  • Current implementation did not yield immediate time savings, suggesting a learning curve.
  • LLM integration shows promise for safe use in a tertiary, non-English clinical setting, with potential for future efficiency improvements.