at Ai4 in Las Vegas | August 11–13
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A healthcare organization recognized the critical gap between specialized medical expertise and frontline clinical delivery. They identified how varying levels of clinical service and diagnostic capabilities could impact patient outcomes and treatment consistency. The vision was ambitious: develop an AI-powered clinical decision support system that could democratize specialized knowledge, ensuring every patient receives optimal care regardless of staff expertise levels. The question wasn't whether AI could transform healthcare delivery – it was how to build the right solution and integrate it into the existing workflows.
This landscape presents a unique paradox. Patients arrive with complex conditions requiring immediate attention, yet clinical staff often lack access to specialized expertise or decision support tools. The clinic observed that diagnostic inconsistencies and treatment variations were not just quality concerns – they represented missed opportunities for optimal patient outcomes. The challenge extended beyond simple knowledge gaps. It encompassed the need for real-time decision support, standardized documentation, and seamless integration with existing HIPAA-compliant systems. Traditional training methods couldn’t scale at the pace required, and accessing specialized consultation for every case was neither practical nor cost-effective.
Master of Code Global developed a comprehensive discovery and POC strategy for an AI-powered clinical decision support system, establishing the foundation for a transformative healthcare solution.
Our team designed a multi-layered approach spanning from initial discovery through MVP development, creating a roadmap for an AI system capable of providing real-time clinical decision support. The discovery phase focused on identifying optimal AI model combinations, specifically evaluating MedLM, GPT-4.5, and Gemini with reasoning engines for clinical documentation and diagnostic assistance. We established a framework for integrating authoritative medical guidelines from relevant professional organizations into a retrieval-augmented generation (RAG) system.
Beyond the discovery phase, we architected a proof-of-concept that demonstrated the system’s viability in real-world healthcare scenarios. The POC validated our AI model selection through controlled testing environments, proving the system’s ability to assist medical staff with varying experience levels while maintaining diagnostic accuracy. Our solution architecture prioritized cloud-based deployment while maintaining flexibility for on-premises integration when institutional policies demanded it.
This initial collaboration established the strategic foundation for a long-term partnership aimed at scaling the system far beyond the initial implementation. We created detailed compliance frameworks ensuring HIPAA adherence throughout the entire data flow process, from patient information handling to secure AI model interactions. Our approach included preliminary assessment of existing diagnostic imaging solutions, establishing pathways for seamless integration without compromising patient data security. The project represents just the beginning of our ongoing collaboration to revolutionize healthcare delivery through intelligent automation.
AI Models: MedLM, GPT-4.5, Gemini with reasoning engines
Cloud Infrastructure: Scalable cloud-based deployment architecture
Compliance Framework: HIPAA-compliant data handling and encryption standards
Integration Protocols: Existing certified healthcare system compatibility
RAG Implementation: Medical guideline knowledge base integration
Image Processing: Diagnostic imaging solution assessment
Conducted thorough evaluation of healthcare-trained foundation models to identify optimal combinations for clinical documentation and decision support
Designed comprehensive technical architecture ensuring seamless integration with existing compliant healthcare systems
Established detailed HIPAA compliance protocols including secure data handling, encryption standards, and PHI protection measures
Developed key performance indicators and success metrics framework for MVP effectiveness tracking
Created detailed project timelines, resource allocation, and budget planning for implementation phases
Established long-term collaboration framework for scaling the solution beyond initial implementation
Dmytro Hrytsenko
CEO
John Colón
VP of Global Sales
Ted Franz
VP Sales & Partnerships
