Explainable AI (XAI) is increasingly being used in the healthcare domain. In health management, clinicians and patients are critical stakeholders, requiring tailored XAI explanations based on their unique needs. Our study investigates the differences in explanation needs between clinicians and patients and designs corresponding explanation interfaces for each group. Using a scenario-based approach, we assessed stakeholder-tailored needs, analyzed differences, and designed interfaces using theoretical frameworks. The results demonstrate diverse stakeholder motivations for seeking explanations, leading to varied requirements. The designed interfaces effectively address these requirements, as validated by the preference selection and qualitative feedback from clinicians and patients. Their suggestions provide design insights and highlight the divergent needs of these stakeholder groups. This study contributes practical and theoretical implications to XAI research, emphasizing the importance of understanding diverse stakeholder needs and incorporating relevant theoretical concepts into user-centered interface design.