triageai-mcp

TriageAI MCP is a healthcare-focused AI agent designed to solve one of the most expensive and painful bottlenecks in care delivery: triage.

Today, healthcare systems lose time, capacity, and clinical quality because too many patients enter the system without structured routing. Some go to the ER when they do not need emergency care. Others delay care because they do not know the urgency of their symptoms. Staff spend valuable hours answering repetitive after-hours questions that could be triaged safely and systematically.

TriageAI MCP addresses this gap with an MCP-native architecture that turns symptom intake into structured triage decisions, care pathway routing, and practical follow-up guidance.

What it does

TriageAI MCP exposes a set of MCP tools that can be called from assistants, EHR-connected workflows, or healthcare service layers:

• symptom_triage — analyzes symptoms, duration, age, and optional vitals to classify urgency
• route_to_care — maps the result to the most appropriate care setting
• get_care_instructions — generates concise self-care and escalation guidance for lower-acuity cases

Why MCP matters here

Instead of building a closed chatbot, we built triage as composable tooling. That means any MCP-compatible agent or workflow can use the triage engine as a reusable capability. This gives clinics and product teams a modular path to deploy triage in patient portals, intake systems, AI assistants, and internal workflows without rebuilding logic from scratch.

Why it is important

Healthcare AI often fails in one of two ways:

• it is too generic to be operationally useful
• or it is too rigid to handle real patient variation

TriageAI MCP is designed as a practical middle ground: structured enough for operational use, flexible enough for real symptom narratives, and explicit enough to support safe escalation logic.

Intended impact

• reduce unnecessary pressure on urgent care / ER pathways
• shorten time-to-guidance for patients
• save operational hours for clinics handling repetitive symptom intake
• create a reusable triage layer for MCP-based healthcare agents

Key Features

• MCP-native clinical tool architecture
• structured urgency classification
• care pathway recommendation
• lower-acuity follow-up guidance
• clear modular design for future FHIR integration and EHR workflows

Future Improvements

• FHIR R4 live integration
• clinician-facing dashboard
• audit logging for triage events
• test suite with validated triage scenarios
• stronger evaluation against nurse-reviewed benchmark cases

Built With

• ai-agents
• apis
• clinical-triage-logic
• healthcare-ai
• mcp
• python
• structured-outputs