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Case management programs are intended to reduce avoidable utilization and improve coordination for patients with complex medical, social, and engagement needs. Because case management capacity is limited, health systems need prioritization tools that are both clinically sensible and transparent. Existing referral methods often rely on clinician judgment, simple utilization thresholds, or proprietary risk scores that provide limited explanation. These approaches may overlook social needs, missed appointments, and care gaps that shape patient complexity and influence whether an intervention is feasible. This article proposes an explainable machine learning model that stratifies patients by risk of future high utilization and provides patient-specific reasoning. The model is designed around prior utilization, chronic disease burden, social needs documentation, missed appointments, and care gap indicators. The conceptual architecture uses a gradient-boosted classification model with a SHAP-based post-hoc explanation layer. The model would output both a risk score and a ranked list of contributing factors for each patient considered for case management referral. Conceptually, the model would identify patients who may benefit from case management and explain why each patient was prioritized. These explanations could help case managers tailor outreach, match patients to intervention pathways, and distinguish medical complexity from social instability or disengagement. An explainable risk stratification model could turn a blind referral process into a transparent, clinically sensible prioritization workflow. Its value would depend on careful implementation, fairness monitoring, and alignment with real case manager decision-making.