⚡ Quick Summary
This study explored the effects of a generative AI solution on clinical documentation in rehabilitation settings, revealing that while documentation time remained unchanged, the quality of documentation improved significantly and time pressure decreased for healthcare professionals.
🔍 Key Details
- 👩⚕️ Participants: 12 rehabilitation professionals including physical therapists, occupational therapists, and speech-language pathologists.
- 🗓️ Study Periods: Comparison between conventional documentation (Period A) and AI-assisted documentation (Period B).
- 📏 Measures: Documentation time, NASA-TLX workload scores, and documentation quality.
- 📊 Analysis Tools: R version 4.2.3 for statistical analysis.
🔑 Key Takeaways
- 🕒 No significant difference in documentation time between Periods A and B.
- 📉 Lower NASA-TLX time pressure score in Period B, indicating reduced stress.
- 📈 Higher quality of clinical documentation observed in Period B.
- 🔗 Strong positive correlation (r = 0.71, p < 0.01) between documentation time reduction and voice memo usage.
- 👍 Positive correlation (r = 0.67, p < 0.05) between voice memo frequency and willingness to adopt the AI system.
- 💡 Generative AI shows promise in enhancing documentation processes in rehabilitation.
- 🤝 Adoption considerations should focus on healthcare professionals’ motivation and cooperation.
📚 Background
Healthcare professionals often find themselves overwhelmed by the demands of clinical documentation, which can consume a significant portion of their working hours. The introduction of generative AI aims to alleviate this burden, potentially transforming the way documentation is handled in rehabilitation settings.
🗒️ Study
Conducted with 12 rehabilitation professionals, this study compared traditional documentation methods with those enhanced by a generative AI system. The aim was to assess the impact of AI on documentation time, workload, and quality, providing insights into the feasibility of AI integration in clinical practices.
📈 Results
The findings indicated that while the time required for documentation did not significantly change, the quality of documentation improved in the AI-assisted period. Additionally, participants reported lower time pressure, suggesting that the AI system may help reduce stress associated with documentation tasks.
🌍 Impact and Implications
The implications of this study are profound. By integrating generative AI into clinical documentation, healthcare facilities can not only enhance the quality of their records but also improve the overall well-being of their staff. This could lead to better patient care and more efficient use of healthcare resources, paving the way for broader applications of AI in various medical fields.
🔮 Conclusion
This study highlights the potential of generative AI to transform clinical documentation in rehabilitation. With improved documentation quality and reduced time pressure, the integration of AI solutions could significantly enhance the efficiency of healthcare professionals. Future research should continue to explore the adoption of such technologies, focusing on the motivations and needs of healthcare workers.
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Effects of Introducing Generative AI in Rehabilitation Clinical Documentation.
Abstract
Introduction Healthcare professionals reportedly spend a significant proportion of their working hours on documentation. Therefore, we developed a generative AI solution specialized in creating clinical documentation for rehabilitation. This study aimed to examine the impact of generative AI on clinical documentation tasks. Methods Twelve rehabilitation professionals (physical therapists, occupational therapists, and speech-language pathologists) participated in this study. We compared conventional clinical documentation (Period A) with clinical documentation using a generative AI system (Period B). Measures taken for both periods included time required to complete the clinical documentation (documentation time), workload assessed using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), and quality of the clinical documentation. Between-group comparisons of these measurements were performed. Additionally, we recorded the number of non-conversational voice memos (voice data inputs) in Period B. After the study, we assessed the participants’ willingness to adopt generative AI (implementation intent) on a five-point scale. For statistical analysis, we compared documentation time, NASA-TLX scores, and documentation quality between the two periods. Time saved was determined by subtracting the documentation time of Period B from that of Period A, and a correlation analysis between the number of voice memos (voice data input) and the willingness to adopt the technology was conducted. Analyses were performed using R version 4.2.3 (R Core Team, Durham, NC), with the level of significance set at 0.05. Results No significant difference was observed in the time required to prepare clinical documentation between Periods A and B. However, in Period B, the NASA-TLX time pressure score was significantly lower, while the quality of clinical documentation was significantly higher. Additionally, a strong positive correlation was observed between the reduction in documentation time and the number of voice memos (r = 0.71, p < 0.01), as well as a significant positive correlation with the willingness to adopt the system (r = 0.67, p < 0.05) during clinical documentation in Period B. Conclusion Our findings indicate that using generative AI for clinical documentation tasks can reduce time pressure and improve documentation quality. Moreover, the reduction in documentation time was associated with the frequency of voice memos and the degree of participants' willingness to adopt the system. These results suggest that, to achieve further reductions in workload and costs, considering the motivation and cooperative framework of healthcare professionals when introducing generative AI solutions is essential.
Author: [‘Omon K’, ‘Sasaki T’, ‘Koshiro R’, ‘Fuchigami T’, ‘Hamashima M’]
Journal: Cureus
Citation: Omon K, et al. Effects of Introducing Generative AI in Rehabilitation Clinical Documentation. Effects of Introducing Generative AI in Rehabilitation Clinical Documentation. 2025; 17:e81313. doi: 10.7759/cureus.81313