Feb 1, 2026

CriticalFirst

Link to open source: https://github.com/amruthagoje/CriticalFirst

Project Overview

CriticalFirst is a scalable, Firebase-powered patient queue and triage optimization system designed to manage high patient inflow in clinics and hospitals while ensuring accurate prioritization of critical cases.

The system leverages token-based queueing, rule-driven triage scoring, and real-time data synchronization to dynamically adapt to changing operational conditions such as patient surges and reduced staff availability.

Project Goals

Implement a priority-aware patient queue system
Ensure critical-condition patients are served first
Maintain queue stability during sudden load increases
Support staff shortage scenarios through adaptive scheduling
Provide an offline-first, fault-tolerant architecture

System Functionality

The build includes:

Patient registration with automatic token generation
Rule-based triage scoring using symptoms and urgency levels
Priority queue management with real-time updates
Dynamic queue reordering based on system mutations:
- Mutation A: Patient volume doubling
- Mutation B: 40% reduction in staff availability
Firebase Firestore for real-time, consistent state management
Offline data persistence with background synchronization

Design Rationale

Traditional clinical queue systems often rely on static ordering or manual triage, which becomes unreliable during peak loads.
CriticalFirst addresses this limitation by introducing a deterministic, data-driven triage mechanism that preserves prioritization correctness under stress conditions.

The project was built to model real-world healthcare constraints and demonstrate how cloud-native technologies can be applied to critical infrastructure systems.