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We propose and test a novel graph learning-based explainable artificial intelligence (XAI) approach to address the challenge of developing explainable predictions of patient length of stay (LoS) in intensive care units (ICUs). Specifically, we address a notable gap in the literature on XAI methods that identify interactions between model input features to predict patient health outcomes. Our model intrinsically constructs a patient-level graph, which identifies the importance of feature interactions for prediction of health outcomes. It demonstrates state-of-the-art explanation capabilities based on identification of salient feature interactions compared with traditional XAI methods for prediction of LoS. We supplement our XAI approach with a small-scale user study, which demonstrates that our model can lead to greater user acceptance of artificial intelligence (AI) model-based decisions by contributing to greater interpretability of model predictions. Our model lays the foundation to develop interpretable, predictive tools that healthcare professionals can utilize to improve ICU resource allocation decisions and enhance the clinical relevance of AI systems in providing effective patient care. Although our primary research setting is the ICU, our graph learning model can be generalized to other healthcare contexts to accurately identify key feature interactions for prediction of other health outcomes, such as mortality, readmission risk, and hospitalizations.