MESTRUALIA Qorus-NTT DATA Innovation in Insurance Awards 2025

Concept and Objectives: This project focuses on leveraging artificial intelligence to predict and measure the efficiency of treatments for chronic pelvic pain (CPP). By analyzing patient data, treatment responses, and historical patterns, our AI system aims to provide healthcare providers and insurance companies with data-driven insights. The ultimate goal is to optimize long-term treatment plans, improve patient quality of life, and reduce overall therapy costs.

Reasons Behind the Project: Chronic pelvic pain is a persistent and often debilitating condition affecting millions worldwide. Traditional treatment approaches are costly, inefficient, and lack personalized adaptation to individual patient responses. Many patients undergo prolonged therapy without clear indications of effectiveness, leading to unnecessary expenses for insurance companies and dissatisfaction for patients. This project seeks to address these challenges by providing a more convenient and predictive approach inspired by Chinese complementary medicine, integrating holistic methods with AI-driven analysis.

State of Competition: Currently, various digital health solutions exist for pain management, including AI-based diagnostic tools and treatment monitoring systems. However, few focus specifically on CPP and its unique complexities. Most competing solutions rely on generalized pain management models rather than incorporating alternative medicine approaches or insurance cost optimization strategies. Our AI-driven system differentiates itself by offering a specialized, predictive approach tailored to CPP while integrating complementary medicine techniques to enhance treatment outcomes.

Sources of Inspiration: • Traditional Chinese Medicine (TCM), which has long offered alternative pain management methods, including acupuncture and herbal treatments. • Existing AI applications in personalized medicine and treatment optimization. • Insurance models that favor cost reduction through preventive and predictive care strategies. • Real-world patient struggles with chronic pain, highlighting the need for better-tailored solutions. Departments Involved: • AI & Data Science: Development of machine learning models for treatment prediction and efficiency measurement. • Medical & Research: Collaboration with specialists in chronic pain and complementary medicine to ensure accurate data interpretation. • Healthcare Economics & Insurance: Analysis of cost-saving opportunities and integration with insurance models. • Software Development & IT: Implementation of user-friendly platforms for healthcare providers and patients. • Regulatory & Compliance: Ensuring adherence to medical and data privacy regulations. Main Results So Far: • AI Model Development: Initial machine learning models have been trained on patient data to identify patterns in treatment efficacy. • Data Collection & Analysis: Gathering historical patient records and treatment outcomes to refine predictive capabilities. • Partnerships Established: Collaboration with TCM practitioners and healthcare providers to integrate complementary medicine insights. • Insurance Interest: Preliminary discussions with insurance companies to explore cost-reduction opportunities through AI-driven personalized care plans. • Patient-Centric Approach: Positive feedback from early adopters regarding improved treatment personalization and better symptom management.

By combining AI-driven analytics with complementary medicine insights, this project aims to provide a breakthrough approach to chronic pelvic pain management, enhancing both patient outcomes and economic sustainability in long-term care