📢 Announcement: Lunara placed first at RUHealthHacks, winning the Womens Health Category!
A web-based platform that empowers call center agents with AI-powered triage, improving the accuracy of connections to subspecialists by almost 20%.
Every day, women’s health call centers face overwhelming complexity — agents must sort through mountains of patient histories, symptoms, and sub-specialty decisions. Misrouted referrals delay care, especially for high-risk pregnancies and cancer patients.
We asked: What if triage could be smarter, faster, and more accurate — from the very first call?
Lunara is an AI-powered triage assistant built specifically for women’s health.
It empowers call center agents to make evidence-based referral decisions in under 30 seconds.
Agents enter only minimal information (name, DOB, initial symptoms).
Lunara then:
- Uses Natural Language Processing (NLP) to interpret patient-reported symptoms
- References prior medical history for contextual accuracy
- Generates a ranked list of conditions, sub-specialties, and suitable doctors
- Displays confidence scores, top recommendations, and a summary card for transparency
The result: faster, smarter, and more accurate triage — routing patients to the right provider the first time.
- 25% of women’s health calls are misrouted today — Lunara reduces this drastically.
- Cuts average triage time from minutes to under 30 seconds.
- Reduces cost of care and wait times for patients.
- Improves safety by ensuring high-risk cases reach the right specialists immediately.
Note: Our entire tech stack was developed with HIPAA compliance in mind!
Tech Stack Overview
-Frontend: React / RadixUI
-Backend: FastAPI / Python
-ML/AI: TF-IDF (NLP), SGD
-Database: AWS Aurora Serverless v2 (Postgres Compatible)
-Hosting: Local + Terraform scripts for using AWS Cognito (user auth), Lambda (hosting backend), & S3 with CloudFront (hosting frontend)
Pipeline
- Agents input minimal information via the frontend.
- The backend processes the data and performs NLP-based symptom interpretation.
- The ML model ranks likely conditions and corresponding sub-specialties.
- Results, confidence scores, and doctor recommendations are sent back to the agent dashboard.
- All triages are logged with algorithm version, timestamp, and confidence — ensuring auditability and HIPAA compliance.
Admin Tools
- Modify or override mappings between symptoms and conditions
- Review logs and monitor model performance across 140 tracked conditions
- Add applicable AWS keys to .env file (in project root directory)
- Start a python virtual environment and download python dependencies
python3 -m venv .venv
source .venv/bin/activate
pip install uv
uv pip install -r requirements.txt- Download node dependencies
cd frontend
npm i- Start the dev server
npm run devfThe command devf runs dev (frontend) and dev:backend (backend) concurrently. To deploy in production, use terraform scripts under /infra*