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Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages.

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Generative artificial intelligence (AI) in healthcare shows promise, with a pilot study finding a 20% utilization rate for AI-drafted patient messages. This implementation reduced clinician burden and burnout without impacting response times.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Workflow Optimization

Background:

  • Generative artificial intelligence (AI) is emerging as a transformative technology in healthcare.
  • Evaluating the practical implementation of generative AI tools in clinical settings is crucial for strategic decision-making.
  • Large language models (LLMs) offer potential for automating administrative tasks, such as drafting patient communications.

Purpose of the Study:

  • To assess the implementation of a large language model (LLM) designed to assist clinicians in drafting responses to patient messages.
  • To evaluate the utilization, impact on time efficiency, and clinician experience associated with using an AI tool for electronic inbox management.

Main Methods:

  • A 5-week prospective quality improvement study was conducted at a single academic medical center.
  • The study involved 162 clinicians across primary care and gastroenterology/hepatology divisions.
  • An electronic health record-integrated, HIPAA-compliant LLM generated draft replies to patient portal messages.

Main Results:

  • The mean utilization rate of AI-generated draft replies was 20% across all participating clinicians.
  • No significant changes were observed in reply action, write, or read times.
  • Statistically significant reductions were noted in physician task load scores and work exhaustion, indicating improved clinician experience.

Conclusions:

  • Early implementation of generative AI for drafting patient messages demonstrated notable adoption and usability.
  • The AI tool led to improvements in clinician-reported burden and burnout without negatively affecting time metrics.
  • Further research and testing are necessary to guide the future development and strategic integration of AI in clinical practice.