Date of this Version
11-28-2023
Document Type
DNP Project
Abstract
Background: Ultrasound guided regional anesthesia (UGRA) is associated with a high learning curve and requires advanced training. Patient access to UGRA is restricted due to the limited number of anesthesia providers with the appropriate knowledge and training. These underlying issues are compounded by patient specific variables, such as normal anatomical variations and extremes in body habitus as seen with obesity and the elderly. Ultrasound systems that integrate artificial intelligence (AI) have been developed to combat these problems, and are able to highlight and label important anatomical structures in real time.
Methods: The literary search was conducted utilizing the databases PubMed, Google Scholar, and MEDLINE accessed via the Florida International University (FIU) database. Peer-reviewed research studies that examine the use of ultrasound that integrates AI for UGRA were utilized to guide the quality improvement project. An online educational module was implemented to anesthesia providers that focuses on the challenges of UGRA and the benefits of AI-integrated ultrasound for regional and neuraxial anesthesia. Pre- and post-test surveys served to evaluate the efficacy of the educational module and assess improvement in anesthesia provider knowledge and attitude.
Results: Analysis of the literature revealed six common themes among 14 research articles that elucidate the advantages of ultrasound integrating AI for UGRA and serve as an organizational basis for the quality improvement project. Following implementation of the educational module, all participants (n=4) improved overall from the pre- to the post-test. There was a 100% percent change in knowledge on neuraxial anesthesia and AI, and a 249.91% percent change in AI ultrasound knowledge. The results demonstrated a 50% percent change in attitude towards inclination to utilize AI ultrasound in anesthesia practice.
Discussion: Artificial intelligence ultrasound confirms the correct ultrasound view, improves the identification of sonoanatomical structures, and reduces the risk of needle injury. For neuraxial anesthesia, AI ultrasound decreases the number of needle passes and redirections, assists in identification of the needle insertion point, and predicts needle depth. The results of the quality improvement project show an increase in anesthesia provider knowledge and attitude regarding AI ultrasound for UGRA and allows providers to play an integral role in increasing patient access to regional and neuraxial anesthesia.
Recommended Citation
Hermida, Samantha; Miller, Ann; and Salgado, Alexis, "A Quality Improvement Project on the Utilization of Ultrasound Guided Regional Anesthesia Integrating Artificial Intelligence to Increase Provider Success" (2023). Nicole Wertheim College of Nursing Student Projects. 244.
https://digitalcommons.fiu.edu/cnhs-studentprojects/244