Tech Stack:

The technology stack varies across the project components as follows:

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Core Functionality:

• Case Simulation Engine: Uses GPT agents to dynamically generate prompts and simulate full medical cases, including history tracking and vitals simulation.
• User & Role Management: Supports multiple user roles (Student, Resident, Instructor, Admin) with secure Azure AD authentication and role-based API access.
• Evaluation & Scoring: Automatically evaluates submitted cases via a GPT response display and scoring panel, providing detailed transcripts and score history.
• Performance Dashboard & Gamification: Features a user dashboard for tracking performance and skills, a leaderboard with rankings, and a gamification system including badges, achievements, and points.
• Admin Panel: Provides tools for full CRUD (Create, Read, Update, Delete) management of cases and users, alongside usage analytics and trend reporting.

Technical Stack:

• Systems & Infrastructure: Python, OpenAPI, and Azure.8
• Front-End: Python Django with a responsive layout for mobile and tablet.9
• Back-End: Python, Django, and OpenAPI.8
• Database: PostgreSQL.8
• AI Integration: GPT API (specifically utilizing Agent A for case generation and Agent B for scoring).5
• Authentication: Azure AD.10

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Core Functionality:

• Multi-Tenant Architecture: A base setup that uses partitioning to isolate data by site_id, ensuring users only access records belonging to their specific site.
• Audio Processing & Ingestion: A voice-activated (VAD-enabled) interface for recording, uploading, and playing back audio files.
• Machine Learning Integration: Dynamic loading of AI prompts and models (e.g., GPT-4o) from a database to categorize and extract patient data from audio.
• Site Configuration Framework: A management system to customize AI behavior (prompts, thresholds, model selection) for different clinical sites.
• Azure-Based Event Routing: Automated workflows where audio uploads trigger events in Azure Event Hub, which are then processed by Azure Functions for transcription and LLM-based analysis.7
• Dashboards & Reporting: Frontend interfaces for site-level metrics (token usage, patients processed) and administrative tools for billing and audit logging.8
• Security & Access Control: Role-based access control (RBAC) and authentication integrated with Microsoft Azure AD to manage user permissions and data isolation.

Technical Stack:

• Back End: Python.
• Front End: Python Django (using Django templates and JavaScript for the audio recorder).
• APIs: OpenAPI.
• Cloud Infrastructure: Microsoft Azure (Blob Storage, Event Hub, and Azure Functions).
• Database: PostgreSQL.
• AI/ML: OpenAI (including models like GPT-4o or GPT-3.5).
• Authentication: Microsoft Azure AD (OIDC login flow).

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Core Functionality:

• Audio Transcription: Utilizing AI to transcribe audio recordings.
• Information Extraction: Identifying and extracting specific patient data from those transcriptions.
• Consolidation: Organizing the extracted data, with a specific focus on handling critical health events.2Data Validation: Using the Pydantic library for data handling and validation.

Technical Stack:

• Languages: Python.
• APIs: OpenAI’s Whisper API.
• Libraries: Pydantic.
• Systems: OpenAPI.

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Core Functionality:

The application leverages Retrieval-Augmented Generation (RAG) to provide accurate, cited answers from trusted internal sources. Its primary functions include:

• Data Ingestion: Supports manual uploads of PDF, HTML, and Text files, as well as an automated, scheduled web crawler to pull content from internal websites.
• Document Processing: Chunks documents into meaningful paragraphs, prepends contextual location data, and converts them into vector embeddings for storage.4
• Intelligent Retrieval: Performs vector searches to provide direct answers to user queries, complete with links to the exact document sections used as sources.
• Multimodal Interaction: Includes support for voice-to-text queries and audible voice output for hands-free use.
• User Management: Features a multi-tiered dashboard (Admin, Editor, Reader) with role-based access control.
• Asynchronous Scaling: A scalable system (v3) uses asynchronous job queues to handle large files and high-volume processing simultaneously.

Technical Stack

The project utilizes a robust modern technology stack:

• Backend: Python Django and Django REST Framework for the core API and application logic.
• Frontend: Next.js and React for the customer-facing dashboard and user interfaces.
• AI & LLM:
  • Models: GPT-4, GPT-4o, and GPT-4o-mini.
  • Embeddings: OpenAI text-embedding-3 (large and small).
  • OCR: PaddleOCR for multilingual text extraction.
• Data Storage:
  • Relational: PostgreSQL.
  • NoSQL: MongoDB.
  • Vector Database: pgvector within Postgres.
• Infrastructure (AWS):
  • Compute: Lambda and EC2
  • Storage: S3 for document and artifact storage.
  • Messaging: SQS, Redis, and Celery for task queuing and event handling.
  • Monitoring: CloudWatch for logging.
• Security: Keycloak (OIDC) for authentication and Role-Based Access Control (RBAC), PropelAuth for session management, and encryption at rest

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Core Functionality

• Multi-Tenant Portals: Enables the creation of dynamic, client-branded portals for volunteer registration, each with unique forms, pricing, and logos.
• Automated Background Checks: Integrates with the TazWorks API to automatically initiate background checks upon payment and track real-time status updates.
• Centralized Management: Features an Admin Control Panel to manage all clients and packages, alongside a Client Control Panel for organizations to oversee their specific portals and reports.
• Advanced Analytics: Generates detailed financial and performance reports, including revenue per portal, conversion rates, and package performance.
• Security & Data Isolation: Utilizes AWS Cognito for secure authentication and ensures strict data isolation between tenants by filtering all database queries by department.

Technical Stack

• Backend: PHP (Laravel).
• Frontend: ReactJS, Next.js, and Bootstrap.
• Database: MySQL for relational data and MongoDB for high-volume, unstructured analytics.
• Infrastructure: AWS (S3 for storage, Cognito for authentication) and SendGrid for automated communications.

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