ArticleDigital health
Artificial intelligence in occupational therapy documentation: Chatbot vs. Occupational Therapists.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI)-based language models such as ChatGPT show promise in generating medical documentation. However, their effectiveness in occupational therapy (OT) documentation-particularly in terms of perceived quality and empathy-remains underexplored. Objective: This study aimed to compare the quality and empathy of clinical documentation generated by licensed occupational therapists versus ChatGPT-3.5, using standardized OT case scenarios. Methods: Fifteen standardized OT cases were used to generate human- and AI-written assessment and plan sections. Five occupational therapists and five patients or caregivers independently evaluated the documentation using 5-point Likert scales across three quality subdomains (completeness, correctness, concordance) and three empathy dimensions (cognitive, affective, behavioral). Inter-rater reliability and correlations between quality and empathy were also analyzed. Results: Artificial intelligence-generated documentation received significantly higher ratings across all quality and empathy dimensions than human-generated documentation (all Conclusion: Artificial intelligence-based tools may help reduce documentation burdens for therapists by generating high-quality, empathetic notes. However, human-authored documentation remains more consistent across evaluators. These results underscore the potential and limitations of AI in clinical documentation, highlighting the need for further development to enhance contextual sensitivity, communication coherence, and evaluator reliability. Future research should examine AI performance in real-world OT practice settings.
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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.