Organizational culture for sustainable healthcare: An NLP-ML-SEM framework with the Sustainability Culture Alignment Index (SCAI)
Abstract
There are increasing pressures globally for health care organizations to become environmentally sustainable due to both climate change and public demand. The health care sector alone accounts for about 5% of total global greenhouse gas emissions, which creates an urgent requirement to incorporate both environmental and social sustainability into health care delivery. However, organizational culture continues to be a significant but underdeveloped resource for creating sustainable health care. Little information exists concerning which type of organizational culture may be best suited to promote sustainability within health care settings. This paper provides a solution to this gap by presenting an analytical framework combining Natural Language Processing (NLP), Machine Learning (ML) and Structural Equation Modeling (SEM) for quantifying and assessing alignment between hospital culture and sustainability goals. A new index for measuring alignment was developed based upon textural analysis of both staff responses and hospital documentation, Sustainability Culture Alignment Index (SCAI). The SCAI indexes the extent to which the cultural values of an organization align with sustainable health care practices. Using a global sample of hospitals, we applied NLP to identify cultural factors from hospital policy documents and mission statements, trained ML models to predict sustainability performance using these factors, and employed SEM to confirm the relationship among these factors. Results showed that hospitals with the highest SCAI scores demonstrated significantly improved environmental performance (i.e., waste reduction, increased energy efficiency) and increased staff job satisfaction (p < 0.001), thus indicating that the positive relationship between culture-sustainability alignment and both environmental and human outcomes is very strong. We have also found that the proposed NLP-ML-SEM framework provided excellent predictive accuracy and model fit (CFI=0.96, RMSEA=0.04), and identified several culturally-driven factors as critical for promoting sustainability in health care. The findings of this study offer practical guidance for hospital administrators and policymakers, demonstrating that investments in cultural transformation will result in measurable advancements towards sustainable health care delivery systems.
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References
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