1. Unanswered Questions for the Oncology and Pain Segment
a. Clarification of Specific VR Scenarios for Oncology and Pain Patients
- Action Item: Collaborate with the clinical science team, including oncologists, oncology psychologists, pain specialists, pain psychiatrist to identify and prioritize specific VR scenarios and environments that are most beneficial for oncology and chronic pain patients particularly those with OUD, chronic low back pain and breast cancer.
- Unanswered Questions:
- What are the key psychological and educational needs of oncology patients at different stages of their treatment?
- Which VR or non VR lifestyle habits, CBT, psycho-education, interventions have shown the most efficacy in existing literature for oncology and pain support?
- What are key limitations to the interventions like duration, context or edge cases that need to be considered?
- What specific VR environments have been proven to alleviate chronic pain symptoms?
- How can personalization enhance the efficacy of these VR interventions?
b. Integration of Psychological Evaluations
- Action Item: Determine appropriate psychological assessment and evaluation tools to stratify patients based on their psychological status.
- Unanswered Questions:
- What are the key components of bio-psycho-social health that acts as determinant of health for oncology and pain patients and should be evaluated in priority?
- How should evaluations be stratified along the treatment to arrive at a productive amount of progress tracking for patients without overwhelming the care team with information but providing the sufficient information for them and monitoring codes?
- How does a pre-screening tool inform the initial evaluation process?
- How will the data from these assessments inform personalized VR and AI interventions? (cross-functional collaboration)
c. Personalization Across Cancer Types and Pain Conditions
- Action Item: Research the differences in psychological needs among various cancer types and pain conditions to ensure adaptability.
- Unanswered Questions:
- How do treatment routes and psychological impacts differ among breast, prostate, and lung cancers or other most common cancer types?
- Do we need specialized content for each cancer type and pain condition, or can we create adaptable modules?
- What modules are generic to help start development and focus on those?
- Do different types of chronic pain require different VR approaches?
- How can patient feedback be incorporated to refine personalization?
d. Workflow Integration and Implementation
- Action Item: Map out the oncology and pain patient journey in detail to understand where and how the product can be integrated without disrupting existing workflows.
- Unanswered Questions:
- What are the specific points in the patient journey where VR interventions would be most effective?
- How can nurses and paraprofessionals benefit from being involved in the implementation process to ensure smooth adoption?
- How do VR interventions fit into existing pain management protocols?
e. Physiological Data Integration
- Action Item: Explore the feasibility of collecting physiological data (e.g., heart rate variability, skin conductance) during VR sessions.
- Unanswered Questions:
- What physiological metrics are most indicative of pain levels or stress in patients?
- How will this data be used to adjust treatment plans or interventions?
- How can real-time physiological data enhance the therapeutic effect of VR?
- What are the technical challenges in collecting and interpreting this data?
2. Key Questions to Improve the Conversational AI
a. Defining Levels of Personalization
- Action Item: Determine the feasible layers of personalization (e.g., voice, language, context, culturally sensitivity and inclusivity) that can be implemented in the AI model.
- Key Questions:
- What aspects of personalization have the most significant impact on patient outcomes?
- How can we balance personalization with the need for scalability?
- How should the AI's behavior adapt to reduce cultural biases?
- How can we modify our user testing and data collection processes to ensure demographic adaptivity?
- How can we incorporate cultural and identity nuances into AI conversations?
- What mechanisms are in place to continually update and improve cultural responsiveness?