The Relationship Between Nurse Wellbeing and Artificial Intelligence: A Quantitative Correlational Study

Abstract

Background:

This quantitative, correlational study examined the relationship between artificial intelligence (AI) and intensive care nurses’ physiological and psychological well-being. Disruptive innovations in healthcare are generating technology advances that aim to improve patient outcomes and optimize patient data usability in nursing practice such as wearable devices for disease management and prevention, AI in clinical decision-making support systems with predictive analytics and smart devices using AI to enhance the ability to identify patient deterioration. Disruptive innovations provide traction in achieving organizational goals such as the three elements of the Quadruple Aim: better health care, better health, and lower costs.

Methods:

Using the combined Wellbeing Index (WBI) and Unified Theory of Acceptance and Use of Technology (UTAUT) survey instruments, data were collected from 32 registered nurses working in an ICU. The survey results informed the study of two research questions: to what extent does the use of AI correlate with intensive care nurses’ physiological and psychological well-being, and to what extent does behavioral intent (BI) to use AI correlate with the UTAUT variables of performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). Nonparametric analysis was conducted using a Spearman correlation analysis, two-tailed test for 0.05 significance.

Results:

All variables revealed a positive correlation. A non-significant, positive, moderate relationship was demonstrated between WBI mean and the UTAUT mean. A significant, positive, moderate relationship was demonstrated between PE mean and the BI mean. A significant, positive, strong correlation was demonstrated between EE mean and the BI mean. A non-significant, positive, weak correlation was demonstrated between SI mean and the BI mean. A significant, positive, strong correlation was demonstrated between FC mean and the BI mean.

Conclusions:

AI and nursing can lead enhancements in standard patient care processes and workflows to improve quality of care, impact cost and optimize the patient and provider experience. Understanding and addressing the UTAUT factors that influence future technology adoption and implementations can also support achieving the Quadruple Aim. This study contributes to the gap that exists in the lack of research with the relationship known between AI and intensive care nurses’ physiological and psychological well-being

Hypothesis Testing: Summary

Research Question	Hypothesis	Test Performed	Result
RQ1: To what extent does the use of AI correlate with intensive care nurses’ physiological and psychological well-being?	H1o: Intensive care nurses’ physiological and psychological well-being does not significantly relate to the use of AI.	Spearman’s Correlation:rs (32) = .0378, p >.05	H1o Fail to reject
RQ2: To what extent does behavioral intent to use AI correlate with the UTAUT variables of performance expectancy, effort expectancy, social influence and facilitating conditions in intensive care settings?	H2o: The UTAUT predictor variable performance expectancy, PE, does not significantly relate to the behavioral intent to use AI with intensive care nurses.	Spearman’s Correlation:rs (32) = .354, p < .05	H2o Reject
RQ2: To what extent does behavioral intent to use AI correlate with the UTAUT variables of performance expectancy, effort expectancy, social influence and facilitating conditions in intensive care settings?	H3o: The UTAUT predictor variable effort expectancy, EE, does not significantly relate to the behavioral intent to use AI with intensive care nurses.	Spearman’s Correlation:rs (32) = .599, p < .05	H3o Reject
RQ2: To what extent does behavioral intent to use AI correlate with the UTAUT variables of performance expectancy, effort expectancy, social influence and facilitating conditions in intensive care settings?	H4o: The UTAUT predictor variable social influence, SI, does not significantly relate to the behavioral intent to use AI with intensive care nurses.	Spearman’s Correlation:rs (32) = .284, p > .05	H4o Fail to Reject
RQ2: To what extent does behavioral intent to use AI correlate with the UTAUT variables of performance expectancy, effort expectancy, social influence and facilitating conditions in intensive care settings?	H5o: The UTAUT predictor variable facilitating conditions, FC, does not significantly relate to the behavioral intent to use AI with intensive care nurses.	Spearman’s Correlation:rs (32) = .568, p < .05	H5o Reject