Leveraging machine learning to enhance nurses' practice for autism spectrum disorder care: A narrative review - Stephanie Sandanasamy; Philip McFarlane; Yu Okamoto; Alannah L. Couper (Journal of Nursing Reports in Clinical Practice)
Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition marked by social interaction and communication difficulties, repetitive behaviors, and restricted interests. The increasing prevalence of ASD underscores the urgent need for specialized training for nurses who play a critical role in managing and supporting autistic individuals. Traditional practice methods often fail to equip nurses with the hands-on experience to interact with this population effectively. This literature review explores the transformative potential of machine learning (ML) in enhancing nurse training for ASD care. ML offers personalized and adaptive learning experiences, realistic simulations, continuous assessment, and evidence-based practices. Personalized learning through ML tailors educational content to individual nurses' needs, enhancing their competence and confidence. Realistic virtual simulations provide safe environments to practice real-life scenarios, improving nurses' ability to handle diverse behavioral and communication challenges. Continuous feedback and assessment ensure ongoing professional development, while data-driven insights support the creation of effective care strategies. Interdisciplinary collaboration is also facilitated through ML, integrating knowledge from various healthcare fields to offer comprehensive training. The improved training results in better patient outcomes, including fewer behavioral issues and enhanced interactions with autistic children. However, challenges such as ensuring data quality, ethical considerations, and fostering acceptance of new technologies must be addressed. This review highlights ML's significant impact on nurse training, aiming to elevate the quality of care for individuals with ASD through innovative, data-driven approaches.