Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

Abstract

Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizational factors are related to staff engagement, limited research is available regarding what drives staff engagement and the degree of their importance in healthcare. Leveraging data-driven approaches, in this article, we employ three machine learning algorithms, random forests, gradient boosting, and extra trees, to identify the relative importance of organizational factors affecting staff engagement. We use hospitallevel aggregate survey data from hospitals in the U.K. While staff engagement is the outcome variable, the following factors are used as organizational factors in the prediction model and feature importance analysis: equality, diversity, and inclusion, safety culture, health and wellbeing, immediate managers, quality of appraisals, quality of care, bullying and harassment, violence, and team working.All the algorithms provide comparable prediction results with similar feature importance ranking with respect to prediction accuracy. The results suggest that safety culture is the most influential factor related to staff engagement, followed by the team working. Healthcare managers and decision makers can benefit from this data-driven application to make informed decisions in resource allocation and prioritization efforts to improve staff engagement.

The Impact of Artificial Intelligence on Organizational Justice and Project Performance: A Systematic Literature and Science Mapping Review

“…These studies provide organizations with valuable strategies and methods to achieve higher levels of performance and competitiveness. Al-Nammari et al used UK NHS staff survey data collected over a five-year period from 2015 to 2019 to analyze the impact of nine organizational factors on employee engagement and found that safety culture was the factor most associated with employee engagement [76]. The findings have important implications for project managers, not only to increase employee engagement, but also to significantly improve project performance.…”

“…In addition, AI can improve communication and collaboration within organizations, thus enhancing organizational justice [76]. With smart collaboration tools and automated processes, employees can more easily share information and collaborate on tasks regardless of location or time.…”

“…Similarly, in the field of project management, issues related to culture and change management are a key area of study. The successful implementation of a project management approach often requires cultural change within the organization and active employee engagement [64,73,76]. Therefore, research should focus on the challenges of managing change, fostering a project-oriented culture, and developing strategies that are aligned with project management goals and values.…”

“…Leveraging AI technology, a deliberate team formation can cultivate an agile, cohesive, and equitable team, harnessing individual strengths for a synergistic impact that enhances project performance [75]. Integrating an AI-driven analysis to enhance employees' fairness perceptions not only fosters a sense of security among team mem-bers, but also bolsters project performance effectively [76,77], thereby intertwining agility with intelligent team dynamics.…”

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

“…The efficiency of health-monitoring systems can be increased by the privacy-preserving decision tree (PPDT) classification scheme [52]. Three tree-based algorithms-random forest (RF), gradient boosting (GB), and extra trees (ET)-are used to examine the importance of several aspects of medical staff engagement in healthcare organizations [53]. A novel segment-based cognitive radio vehicle ad hoc network (CR-VANET) architecture can solve the spectrum scarcity problem by using fuzzy and naïve Bayes algorithms [54].…”