This is a submission for the KendoReact Free Components Challenge.
Healthcare in Africa faces critical accessibility challenges. Millions of lives are lost because patients cannot effectively communicate their symptoms to healthcare providers. With 1,500 to 3,000 languages spoken across Africa, language barriers between patients and healthcare providers create significant obstacles to quality care.
What I Built
Consultify revolutionizes telemedicine by enabling context-aware RAG and multilingual communication. The platform addresses the critical language barrier problem in African healthcare through intelligent symptom understanding, automated doctor selection, and real-time multilingual consultation capabilities.
Key Features:
Intelligent Symptom Clerking - Users describe symptoms to an AI agent with vectorized access to medical textbooks
Smart Doctor Selection - Automatically matches patients with the most suitable specialists
Multilingual Consultation - Real-time translation allowing patients and doctors to communicate in their native languages
Prescription Assistance - AI-powered prescription recommendations using the British National Formulary
The platform achieves the simplest onboarding experience in telemedicine while providing seamless multilingual communication and prescription assistance for healthcare providers.
Demo
Testing Instructions
Option 1: Onboard as a new patient and create a consultation to test the complete clerking workflow.
Option 2: Use pre-configured test accounts to experience the multilingual consultation feature:
Test Accounts
Doctor (English):
- Email: [email protected]
- Password: 12345678
Patient (French):
- Email: [email protected]
- Password: 12345678
Quick Test: Log in with either account, navigate to "Previous Consultations," and select the existing test consultation to see real-time multilingual communication in action.
Tip: Open the second account in a private/incognito window to test the multilingual chat between both users simultaneously.
KendoReact Components Used
Button, Data Grid, Date Input, DropDown, Badge, Input, TextArea, Progress Bar, Avatar, Notification
Hero Page
Components Used: Button, Avatar (Navbar)
The hero page features prominent action buttons for getting started and navigating the platform, with avatar components in the navigation bar for user identification.
Onboarding Pages
Components Used: DropDown, Input, Progress Bar
The onboarding flow utilizes dropdown components for specialty selection, input fields for user information, and progress bars to guide users through the multi-step registration process.
Patient and Doctor Dashboard
Components Used: Button, Data Grid, Avatar (Navbar)
Both patient and doctor dashboards feature data grids for displaying consultation history and appointments, with action buttons for quick navigation and avatar components in the navigation.
Consultations Page
Components Used: Data Grid
The consultations page uses data grid components to display comprehensive consultation records with filtering and sorting capabilities.
Consultation Chat Page
Components Used: Button, Badge, TextArea, Avatar
The real-time chat interface includes text area components for message input, badges for status indicators, avatars for participant identification, and action buttons for consultation management.
Prescription Modal
Components Used: Date Input
The prescription creation modal incorporates date input components for medication scheduling and prescription validity periods.
Toast Notifications
Components Used: Notification
Notification components are implemented throughout the application for user feedback on actions like successful logins, prescription submissions, and system updates.
[Optional: RAGs to Riches prize category] Nuclia Integration
Consultify leverages Nuclia's RAG-as-a-service infrastructure to power its intelligent medical consultation system. The integration includes:
Medical Knowledge Base: Vectorized access to authoritative medical resources including:
- The British National Formulary for evidence-based drug information
- "Microbiology, Pharmacology, and Immunology for Pre-Clinical Students" textbook
Intelligent Workflows: Two primary RAG-powered agents:
Symptom Clerking Agent - Analyzes patient symptoms against medical literature to recommend appropriate specialists
Prescription Assistant Agent - Reviews patient-doctor conversations to suggest evidence-based prescriptions
Every response from the RAG workflow is grounded with information from these medical textbooks, ensuring clinically accurate and contextually appropriate recommendations for both symptom assessment and prescription guidance.
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