1.5 | IT & Infrastructure Management
Public Stakeholder connect
Public Stakeholder connect
Active 3 months ago
Purpose: For IT directors, systems architects, network engineers, cybersecurity specialists, and other... View more
Public Stakeholder connect
Organizer:
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Group Description
Purpose: For IT directors, systems architects, network engineers, cybersecurity specialists, and other IT professionals responsible for the underlying technology infrastructure, system integration, and operational maintenance of AI solutions within healthcare IT environments.
Description: This group addresses the specific technical challenges and best practices related to deploying, managing, and securing the hardware, software, network, and data pipelines required for AI implementation in healthcare from an IT operational and architectural perspective. It’s for the “enablers” who ensure AI systems run smoothly and securely within existing IT ecosystems.
Intended Use: For sharing expertise on IT readiness for AI, discussing the technical nuances of interoperability with existing systems (like EHRs), managing data pipelines at scale, and ensuring the robust, secure, and performant operation of AI systems in production.
Limitations:
No AI Model Development: Discussions about specific AI algorithms, model training, or data science techniques are for “AI Innovators & Developers” (1.3).
No High-Level Organizational Strategy: Broad AI strategy and business case discussions are for “Healthcare Leadership & Strategy” (1.2).
No Policy/Regulatory Interpretation: Detailed discussions about legal or compliance frameworks are for “Policy & Regulatory/Legal Experts” (1.5) or “Foundational Pillars > Regulatory Landscape” (3.3).
No Step-by-Step Implementation Roadmaps: While closely related, the process steps of implementation for AI projects belong to “AI Adoption Journey > Implementation & Integration” (2.2). This group focuses on the technical, operational role of IT in supporting that process.
Key Activities:
Discussions on optimal hardware (e.g., GPUs, specialized AI chips) and software platforms for AI deployment (e.g., MLOps tools, containerization).
Strategies for ensuring robust data interoperability and data flow between AI systems and existing healthcare IT (e.g., EHRs, PACS).
Troubleshooting network latency, bandwidth, and storage challenges for large AI datasets and real-time inference.
Best practices for IT security, data privacy, and cybersecurity specifically within AI environments.
Operational management of deployed AI systems, including monitoring, scaling, and maintenance.
Potential Users: IT & Infrastructure Professional, Healthcare Leader/Administrator (with an IT focus).
Possible Discussions:
“Building scalable IT infrastructure to support large-scale AI deployment across a health system.”
“Integrating AI tools with legacy healthcare IT systems: technical challenges and architectural solutions.”
“Securing AI endpoints and protecting sensitive patient data within the IT environment.”
“Optimal hardware considerations for AI model inference in clinical settings.”
“DevOps/MLOps pipelines for AI model deployment and management in healthcare.”
Other Important Notes: This group is essential for bridging the gap between theoretical AI models and their practical, secure, and efficient operation in real-world healthcare environments.