EscalAItor – AI-Powered Chatbot for Health Quality of Life Monitoring

Use Case:

Integration of automated speech and voice recognition with a mobile chatbot interface to help cancer patients complete standardized QoL forms (e.g. EORTC) more comfortably and efficiently from home.

Outcome:

• Net Promoter Score (NPS) of 75 among cancer patients for chatbot usability;
• Seamless UI built with Flutter for cross-platform compatibility;
• 100% completion rate of QoL questionnaire via chatbot in pilot trials;
• NLP confidence levels exceeding 95% for text-based responses.

Ecosystem Support:

Supported by StairwAI expert matchmaking and AI guidance. Experts assisted in UX wireframing, NLP pipeline tuning, and integration with Google Cloud for data processing and sustainability planning.

AI Relevance:

1. Combines Natural Language Processing (NLP), Automated Speech Recognition (ASR), and sentiment detection.
2. Offers low-friction, patient-centric QoL data collection using mobile AI.
3. Demonstrates transparent metrics for patient usability (NPS, UEQ-S).
4. Aligns with personalized healthcare vision via future integration of sensor data and emotion prediction

Summary:

iBreve Ltd., a digital health SME, sought to streamline and humanize the way cancer patients report their health experiences from home. Their solution, EscalAItor, is a conversational AI interface designed to guide users through the EORTC Quality of Life (QoL) questionnaire via voice or text. Built using Flutter for cross-platform compatibility, the chatbot supports automated speech recognition, natural language understanding, and real-time interaction. The StairwAI project provided key resources—AI experts contributed to UX prototyping, selected NLP backends, and optimized performance across platforms. Dialogflow ES was chosen for backend integration due to its affordability, flexibility, and multilingual capabilities. The backend was fully Dockerized and deployed on Google Cloud, ensuring efficient scaling and sustainability. Pilot tests showed high user satisfaction, with patients preferring the chatbot experience to traditional paper-based forms. A Net Promoter Score of 75 confirmed strong engagement. The AI system handled speech-to-text conversion reliably, even for non-native speakers, and included fallback mechanisms for repeated or ambiguous input. Clinically validated FAQs were integrated to provide contextual replies and improve user trust.
Interviews with oncologists emphasized the importance of tracking patient responses over time. EscalAItor was designed to support follow-up use and allow users to complete the questionnaire in multiple sittings. Additionally, a future-facing architecture was created to enable the combination of chatbot data with wearable sensor inputs, opening the door to emotion prediction and personalized care at home. EscalAItor exemplifies how conversational AI can enhance patient monitoring, relieve clinical burden, and deliver a more compassionate digital health experience.