1. Clinical Science Team
Assumptions:
- Specific Needs Exist: We assume that oncology patients have distinct psychological and educational needs at different treatment stages that can be effectively addressed through VR.
- Personalization Enhances Efficacy: The effectiveness of VR interventions is believed to increase with personalization tailored to individual patient needs and cancer types.
- Integration is Feasible: It's assumed that psychological assessments and VR interventions can be seamlessly integrated into the initial evaluation process without overburdening clinicians or disrupting workflows.
- Regulatory Compliance is Achievable: There is an assumption that any regulatory constraints related to VR use in oncology can be navigated successfully.
- Billing Codes are Applicable: We believe that existing billing codes can be used or adapted for VR interventions in oncology, allowing for proper reimbursement.
2. Pain Specialists
Assumptions:
- VR is Effective for Pain Management: The assumption is that VR environments can significantly alleviate chronic pain symptoms and that specific scenarios have proven efficacy.
- Personalization is Key: We assume that individualizing VR experiences will lead to better outcomes for pain patients.
- Physiological Data is Valuable: It's believed that collecting physiological data during VR sessions will provide actionable insights to improve patient care.
- Billing is Possible: We assume that billing codes exist or can be established to reimburse VR interventions in pain management.
3. AI Developers and Technical Team
Assumptions:
- Personalization Layers are Technically Feasible: We believe that implementing various levels of personalization in the AI model (voice, language, cultural context) is technically achievable.
- One AI Model May Not Suffice: There is an assumption that a single AI model might not effectively address both oncology and pain management nuances, potentially necessitating multiple models.
- Data Availability for Fine-Tuning: We assume that sufficient data will be available for fine-tuning AI models to improve personalization over time.