Building a Natural Language Processing Augmented Information Support System to Enhance Supportive Care for Patients...

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Patient Support Systems

Background:

• Prostate cancer patients and families face challenges accessing and understanding health information.
• Care transitions and post-treatment self-management are particularly difficult.
• Effective information and support are crucial for improving patient outcomes.

Purpose of the Study:

• To develop the Interactive Prostate Cancer Information, Communication, and Support Program (iPICS).
• To create an NLP-augmented eHealth platform to enhance care continuity and decision-making.
• To improve health outcomes for prostate cancer patients and their families.

Main Methods:

• User-centered design approach with iterative development and refinement.
• Informed by responsible AI principles and thematic analysis of patient/family feedback.
• Utilized semistructured interviews and focus groups with diverse participants.

Main Results:

• Developed iPICS with three core components: Inform (information hub), Dialog (NLP summarization), and Snap (peer support).
• Key features include evidence-based multimedia information, NLP-powered communication tools, and moderated peer support.
• Platform design prioritized usability, accessibility, security, and HIPAA compliance.

Conclusions:

• The NLP-augmented iPICS platform supports prostate cancer patients and families through care transitions.
• It offers a scalable, ethical, and inclusive solution for survivorship care.
• Further studies will evaluate patient-reported outcomes and explore broader applications.