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Encoding Clinical Intuition: The Maya Colour-Coded Three-Symptom System for AI-Enabled TriageBackground Digital triage tools are increasingly shaping patient access to healthcare, yet many follow outdated, algorithmic, single-symptom decision trees designed in the late 20th century. These approaches can under-prioritise urgent multi-symptom presentations and over-refer mild cases, limiting their safety and efficiency. Methods The Maya system was developed over decades of acute care experience, drawing on a catalogue of ~600 conditions. Symptoms were classified into subgroups and assigned one of four urgency codes: Red (emergency), Blue (infection risk), Yellow (pharmacist/self-care), Green (safe to monitor). Multiple clinicians refined classifications. Analysis identified three concurrent symptoms as the optimal threshold for differentiating urgent from non-urgent cases. The system was subsequently embedded into an AI platform, Dr Maya GPT, enabling real-time pattern recognition and triage recommendations. Findings In nurse-led and AI-assisted triage scenarios, the Maya system supported rapid, safe decision-making, reduced inappropriate referrals, and improved infection control. AI integration preserved the system’s clinician-style reasoning, delivering decisions within seconds without the delays inherent in linear question-based algorithms. Interpretation The Maya three-symptom colour-coded system offers a validated, experience-based triage framework that can be encoded into AI for scalable, equitable, and safe pre-hospital triage worldwide.