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Related Experiment Video

Updated: Jan 15, 2026

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System
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Artificial intelligence in occupational therapy documentation: Chatbot vs. Occupational Therapists.

Si-An Lee1, Jin-Hyuck Park2

  • 1Department of ICT Convergence, The Graduate School, Soonchunhyang University, Asan, Republic of Korea.

Digital Health
|October 14, 2025
PubMed
Summary

Artificial intelligence (AI) tools can generate high-quality, empathetic occupational therapy documentation, but human notes show greater consistency. Further AI development is needed for real-world clinical application.

Keywords:
ChatGPTdocumentationempathylarge language modelsoccupational therapyquality

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Area of Science:

  • Medical Documentation
  • Artificial Intelligence in Healthcare
  • Occupational Therapy

Background:

  • AI language models show potential for medical documentation generation.
  • Effectiveness of AI in occupational therapy (OT) documentation, particularly quality and empathy, is understudied.

Purpose of the Study:

  • Compare quality and empathy of OT documentation generated by human therapists versus ChatGPT-3.5.
  • Evaluate AI's role in clinical documentation for occupational therapy.

Main Methods:

  • Standardized OT case scenarios used for human and AI documentation generation.
  • Evaluated documentation using Likert scales for quality (completeness, correctness, concordance) and empathy (cognitive, affective, behavioral).
  • Analyzed inter-rater reliability and correlations between quality and empathy.

Main Results:

  • AI-generated documentation received significantly higher ratings for quality and empathy (p < 0.001).
  • Human-generated documentation showed stronger quality-empathy correlations and higher inter-rater reliability.
  • AI outputs may offer higher perceived quality but exhibit greater variability in interpretation.

Conclusions:

  • AI tools can potentially reduce therapist documentation burden with high-quality, empathetic notes.
  • Human-authored documentation currently offers greater consistency among evaluators.
  • Further AI development is needed to improve contextual sensitivity and evaluator reliability in clinical settings.