An Intelligent, Predictive Revenue Cycle Management system designed to eliminate claim denials and automate hospital billing operations using Machine Learning.
Built with ❤️ by Buggi Coders
In the modern healthcare system, claim denials cost hospitals millions of dollars and countless administrative hours. MediVista revolutionizes the RCM (Revenue Cycle Management) pipeline. By intelligently analyzing patient records, EMR data, and billing systems before claims are submitted, MediVista predicts denial risks, categorizes portfolios, and suggests the Next Best Action to ensure maximum revenue realization.
- 🧠 Predictive Denial AI: A custom Machine Learning engine that predicts if a claim will be approved or denied, providing a confidence score and identifying exact bottlenecks before submission.
- 📊 Premium Financial Insights: A breathtaking, dynamic dashboard offering real-time AI-driven analytics on:
- 📉 Revenue Cycle Leakage
- 🍩 Denial Risk Portfolios
- 🏢 Departmental Profitability
- ⚡ Payout Velocity Forecasts
- 💬 Intelligent AI Chat Assistant (Upcoming Update!): Talk to your data! A deeply integrated conversational AI that allows hospital administrators to query financial health, retrieve specific claim statuses, and get actionable insights instantly.
- ⚡ Frictionless Smart Intake: A beautifully animated, step-by-step digital handshake between clinical data and insurance carriers.
- 🔄 Enterprise Integration Ready: Conceptually wired to interface seamlessly with major EMR systems (Epic, Practo) and Billing logic software.
MediVista is built as a highly scalable microservices-inspired architecture, designed for enterprise healthcare demands.
- Framework: React 18 + TypeScript + Vite
- Styling: Tailwind CSS + Framer Motion (for buttery smooth, premium animations)
- Charting: Recharts (Bespoke glassmorphic data visualization)
- State & Forms: React Hook Form for robust validation, Context API.
- Framework: Spring Boot 3 (Java)
- Database: MySQL
- Architecture: Strict adherence to layered architecture (Controller-Service-Repository), featuring automated DTO mapping, secure RESTful endpoints, and intelligent error handling.
- Framework: Python + FastAPI
- Core Logic: Predictive ML serving layers returning Confidence Scores, Risk Analysis, and Expected Payout Timelines. Includes simulated dynamic jitter to generate highly realistic forecasting timelines on the fly.
We are Buggi Coders – a 5-person powerhouse team:
- 👨💻 Kaushik Kumar — Machine Learning & AI Core
- 👨💻 Prajjwal Saggar — Backend Architecture
- 👨💻 Sameer — Frontend UI/UX
- 👨💻 Akash Singh — Dev OOPs
- 👨💻 Prakhar — CI/CD & Testing Pipeline
Experience the autonomous RCM platform live:
- Patient Registration: Administrative staff enters patient demographics via the Smart Intake Form.
- AI Pre-Auth: The data is instantly streamed to the Python ML Server, assessing risk based on the specific carrier, department, and policy type.
- Data Sync: The analyzed claim—complete with AI-backed Action Plans—is securely stored via the Spring Boot Java backend.
- Insights Dashboard: The claim appears in the live workflow, and the overarching financial metrics update dynamically for executives to track.
- 🔌 Live AI Agent Chat for autonomous, natural-language data querying and reporting.
- 🔗 Real-time Webhooks for live Insurance Carrier API integration.
- 🛡️ Automated Anomaly Detection for procedural billing discrepancies.