<p>The quality of healthcare services is influenced by a multitude of unpredictable events. Changes in patient clinical conditions and challenges in service organization are only some of the vivid examples that can make the management in healthcare difficult. Estimating patient journeys, known as clinical pathways (CPs), can support care providers in resource planning and enhancing service efficiency. This study presents a decision support system to assist clinicians in predicting CPs and outcomes for patients with traumatic brain injuries (TBIs). This machine learning framework employs an optimal decision tree next to a Markov-based trace clustering as predictive model components. A Shapely value approach extract knowledge of features contribution at both individual and population levels. The proposed approach is validated through a real-life event data, demonstrating high accuracy and providing insights into the rationale behind specific CP predictions which facilitate the adoption of machine learning models in clinical settings.</p>