Overview
ScreeningPlus is a modernized, cloud-based lung-screening and diagnostic workflow platform developed by Intellitech Solutions in partnership with Accumetra and Paraxial.
Built on the foundation of the VA-PALS system created for the U.S. Department of Veterans Affairs, ScreeningPlus re-imagines that proven model for the modern era — introducing a flexible architecture, integrated AI-assisted findings, and global scalability. Intellitech's involvement with this system dates back to the original VA deployment, where we built modern web-based interfaces that integrated with the legacy MUMPS backend, giving us deep domain knowledge that informed the complete modernization effort.
Today, ScreeningPlus is in active use at Mount Sinai Hospital and multiple international sites in Ethiopia, with continued expansion into new global screening initiatives.
Challenge
The original VA-PALS system was a groundbreaking program for veteran lung-cancer screening, but its legacy MUMPS codebase and on-premise infrastructure made evolution difficult.
Accumetra and Paraxial sought to:
- Modernize the application for cloud deployment and multi-institution scalability
- Integrate AI-based nodule detection and scoring into radiologist workflows
- Reduce maintenance complexity while preserving clinical accuracy and workflow fidelity
- Enable global deployments where infrastructure and connectivity vary
The challenge was to preserve the trust and reliability of a clinical-grade system while rebuilding it on a foundation ready for continuous improvement.
Solution
Intellitech Solutions led a full-stack modernization effort, transforming VA-PALS into ScreeningPlus, a secure, cloud-ready, and AI-enabled system.
Core Improvements
- Modern Architecture – Rebuilt using NestJS backend and Angular frontend, with modular REST APIs and Dockerized components.
- MUMPS → TypeScript Migration – Legacy logic re-engineered into a maintainable, well-structured TypeScript codebase for both frontend and backend.
- AI Integration – Seamless ingestion of Accumetra's AI-generated CT findings — including nodule characterization and screening metrics — directly into structured reports.
- Global Deployability – Designed for both U.S. hospital environments and resource-constrained international sites, with hybrid cloud/on-prem deployment options managed via Paraxial's network infrastructure.
- Secure & Compliant – Encrypted MongoDB data storage, JWT-based authentication, and HIPAA-aligned best practices for PHI handling.
- Clinical Workflow Optimization – Automated report generation with customizable templates, significantly reducing radiologist time per case.
System in Action
CT Evaluation Form – Comprehensive diagnostic interface showing patient study details, CT scan imagery, nodule measurements, and AI-assisted findings integrated into clinical workflow
Results
In Use Worldwide
Actively deployed at Mount Sinai Hospital and two Ethiopian clinical sites, with additional global rollouts planned
AI-Augmented Diagnosis
Radiologists can now integrate verified AI findings directly into their reports, improving consistency and diagnostic accuracy
Sustainable Codebase
Migrated away from MUMPS to a modern, fully testable code architecture that supports ongoing innovation
Cloud Scalability
Supports multi-institution deployments and hybrid connectivity for sites with variable bandwidth
Operational Efficiency
Automated data processing and structured reporting have measurably reduced turnaround times for screening results
Published Research
Engineering work contributed to peer-reviewed publication in the Journal of Thoracic Oncology
Impact
ScreeningPlus exemplifies Intellitech Solutions' approach to intelligent modernization: preserving the clinical trust of legacy healthcare systems while re-engineering them for scalability, maintainability, and AI integration.
Through its partnerships with Accumetra and Paraxial, ScreeningPlus is now enabling global access to early lung-cancer screening — bringing advanced diagnostic technology from major U.S. institutions to developing healthcare systems worldwide.
This work contributed to research published in the Journal of Thoracic Oncology, demonstrating how engineered software systems can enable medical professionals to run algorithms against anonymized CT scans from around the world — utilizing artificial intelligence and quantitative imaging to detect and treat cancers sooner.
It stands as a model for how thoughtful modernization can transform specialized medical software into a global, cloud-driven platform for clinical impact.
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