AI-Powered Nursing Assistants: Enhancing Efficiency and Reducing Workload in Healthcare

Background: The use of AI-powered nursing aides in the healthcare sector promises to improve efficiency, reduce staff workload, and increase patient outcomes. Still, how healthcare workers are utilizing and benefiting from AI solutions remains an area of study. This research seeks to understand the effectiveness, perceptions, and challenges of AI-powered nursing aides in the clinical context. Methods: This study utilized a quantitative approach in the form of a descriptive cross-sectional survey targeting 250 healthcare workers, including doctors, nurses, and hospital managers. A pretested structured questionnaire with a Likert-type scale and closed questions captured the participants’ level of awareness, perceptions, reported efficiency gains, and implementation barriers regarding AI-powered nursing assistants. Data was analyzed descriptively and tested for reliability (Cronbach’s Alpha), normality (Shapiro-Wilk), and measurement validity using Principal Component Analysis (PCA). Results: As anticipated, the data from the Shapiro-Wilk test showed a significant deviation from normality (p < 0.05), which called for the application of non-parametric approaches statistical approaches. Cronbach’s Alpha (0.1682), indicated a lack of reliability on survey items which requires an adjustment. Even so, PCA results indicated that the first two components captured 69.25% of the variance which indicates moderate construct validity. The results demonstrate distinct perceptions of AI-enabled nursing assistants and suggest the need for more investigation of acceptance determinants among professionals in the healthcare field. Conclusion: AI-enabled nursing assistants are a great option to help alleviate the workload and improve the efficiency of healthcare services, but issues such as staff reluctance, inadequate training, and lack of trust add to the challenges of adoption. This illustrates the need to revise the measurement tools, incorporate comprehensive AI training, and undertake more qualitative work to analyze the obstacles and positive influences of AI use in healthcare. The institutions have to work towards building confidence in AI to fully harness its effects in clinical settings.