Concultnote.ai is transforming the future of patient care by redefining the interaction between doctors and patients through AI. Our mission was to craft a seamless, emotionally intelligent, and regulation-compliant AI interface that reduces physician workload, increases consultation efficiency, and enhances the consistency of care. We envisioned a system that brings the warmth of a human doctor and the precision of evidence-based medicine together under one digital roof.

Problem Statement

The traditional outpatient consultation is time-consuming, inconsistent, and heavily dependent on physician availability—especially in rural and underserved areas. Doctors spend the majority of consultation time on history taking and explaining diagnosis, which could be streamlined. Meanwhile, patients often receive varying levels of care depending on location, physician skill, and personal biases.

Objective

Design a human-centered, AI-driven medical assistant that:

The AI assistant is designed to conduct empathetic, intelligent patient interviews in real-time, capturing detailed medical histories with the warmth and clarity of a human doctor. It then summarizes these findings and suggests a treatment plan for the doctor to review and approve, significantly reducing the physician’s workload while maintaining clinical oversight. Once approved, the AI delivers clear, trustworthy explanations back to the patient, ensuring understanding and comfort. It seamlessly integrates both telehealth and in-person care workflows, allowing for flexibility in consultation modes. With built-in safeguards against hallucinations and full compliance with medical regulations, the system prioritizes patient safety and trust. By streamlining processes, it boosts doctor productivity and satisfaction, while also minimizing patient wait times and addressing the challenges of rural healthcare delivery through scalable, AI-powered support.

Design Process

Immerse

During the first phase, we immersed ourselves in the project to empathize and understand the pain points of the end-user. This phase is crucial in developing a user-centric design and for developing an immersion map for the project.

Research summary

• Stakeholder Interviews: Conducted interviews with doctors, medical administrators, and patients to understand pain points in current outpatient workflows.
• Competitive Benchmarking: Reviewed AI and telehealth tools such as Babylon Health, Ada Health, and Google's Med-PaLM.
• Clinical Regulations Review: Analyzed medical compliance frameworks (HIPAA, FDA AI guidelines, etc.) for design constraints.
• AI Safety Exploration: Explored dual-AI verification models to minimize hallucinations and ensure trustworthiness.

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Key takeaways from research :

• Identified common pain points and challenges faced by farmers in managing calf health
• Analyzed the current mobile app and identified areas of improvement for tablet version
• Gathered insights on user expectations and needs for the tablet app

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Discovery Workshop