Effects of Introducing Generative AI in Rehabilitation Clinical Documentation

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.