Machine learning models are increasingly used in healthcare, yet similar predictive performance across models may conceal divergent explanations, introducing explanatory uncertainty in clinical decision-making. To address this challenge, we propose two novel metrics in the context of stroke care for predicting the National Institutes of Health Stroke Scale (NIHSS) at hospital discharge. The XAI Quality Index (XAIqi) quantifies the consistency and robustness of feature importance across heterogeneous models, identifying variables that remain relevant regardless of model architecture. The XAI Complexity Index (XAIci) characterizes task complexity based on the variability of explanatory patterns between models, reflecting how consistently a prediction task can be interpreted across algorithms. Using different machine learning algorithms we demonstrate how integrating explainability across models reduces model-specific artifacts and strengthens confidence in clinically meaningful predictors. Together, XAIqi and XAIci provide a unified framework for assessing explainability quality and task complexity in AI-driven stroke outcome prediction.