Artificial Intelligence In Anesthesia Management
DOI:
https://doi.org/10.64149/J.Carcinog.24.8s.63-72Keywords:
Artificial Intelligence, Anesthesia Management, AI in Healthcare, Patient Safety, Survey Analysis, Cronbach's Alpha, Validity Analysis, Reliability Analysis, Healthcare Professionals, Non-parametric AnalysisAbstract
Objective: The objective of this study is to explore the role and perceptions of Artificial Intelligence (AI) in anesthesia management, focusing on its effectiveness in enhancing patient safety, reducing errors, and optimizing anesthesia delivery. The research is expected to evaluate the awareness, effect, and familiarity of the use of AI technologies in the healthcare profession of anesthesia practice.
Methods: A cross-sectional survey involved a quantitative study of 275 healthcare professionals working in the field of anesthesiology, as well as nurse anesthetists and other medical personnel engaged in the process of managing anesthesia. The survey involved the use of a structured questionnaire whereby the respondents were measured based on the level of familiarity with AI, their views about the impact of AI on anesthesia, and factors that affected the integration of AI. Descriptive statistics, normality tests, reliability analysis (Cronbach's Alpha), and validity analysis (correlation matrix) were applied to analyze the data.
Results: The Shapiro-Wilk normality test revealed that the data did not follow a normal distribution (p = 0.0), suggesting the need for non-parametric statistical methods for further analysis. The Cronbach's Alpha for the survey items measuring perceptions of AI in anesthesia was low (α = 0.30402), indicating poor internal consistency among the items. The correlation matrix showed that there were different relationships between the key items, which means that items do not capture the same concept of the same underlying phenomenon of the impact of AI in control of anesthesia.
Conclusions: The paper indicates the degree to which healthcare professionals are well acquainted with AI, and that the questionnaire deployed during the research needs to be improved so that it can obtain better reliability and validity. In its application to the management of anesthesia, AI is likely to have a remarkable potential, yet subsequent studies are to aim at fine-tuning the survey design, which would help to measure more accurately and further understand the role of AI in clinical practice. This should be subjected to further research to justify the application of AI in patient safety improvement, the minimization of errors that could be caused by humans, and improving efficiency in the delivery of anesthesia in health facilities.
