Regional Healthcare Network Achieves HIPAA-Compliant AI
60% cost reduction while maintaining complete HIPAA compliance
A regional healthcare network with 12 hospitals and 200+ clinics serving over 2 million patients needed to deploy AI for medical imaging analysis and clinical decision support. Their existing workflow relied on manual radiologist review, creating bottlenecks during peak hours and overnight shifts. Previous attempts to use cloud-based AI services failed compliance review—the security team could not accept patient data leaving the network perimeter, even with BAA agreements in place.
The healthcare network faced a critical decision point. Their cloud-based AI costs had escalated to over $2M annually, and their legal and compliance teams were increasingly concerned about patient data leaving their controlled environment. Beyond cost, the radiologists needed AI-assisted analysis within 30 seconds of image acquisition—latency that cloud solutions couldn't reliably deliver. Integration complexity added another layer: 15+ different imaging systems across facilities needed unified AI access, and complete traceability of every AI inference was required for regulatory compliance.
- HIPAA compliance: Patient data could not leave infrastructure or be processed by third-party cloud services
- Latency requirements: Radiologists needed AI-assisted analysis within 30 seconds of image acquisition
- Integration complexity: 15+ different imaging systems across facilities needed unified AI access
- Audit requirements: Complete traceability of every AI inference for regulatory compliance
- Cloud AI costs exceeding $2M annually with unpredictable scaling
- IT team lacking GPU infrastructure expertise for on-premises deployment
SLYD deployed a sovereign AI infrastructure solution designed for healthcare compliance. The architecture included 4× 8-GPU H100 servers distributed across 2 data centers in a high-availability configuration with automatic failover. The system features 100GbE connectivity to PACS systems, a dedicated management network for monitoring, and an isolated VLAN for AI infrastructure with no external network connectivity (air-gapped for PHI). Software components include containerized NVIDIA Triton inference servers, a custom integration layer for PACS connectivity, comprehensive logging and audit systems, and real-time monitoring with alerting. The deployment followed a phased approach: 4 weeks planning, 8 weeks infrastructure build, 6 weeks PACS integration, and 2 weeks clinical staff training.
- 4× Dell PowerEdge XE9680 Servers (8× H100 SXM each)
- High-availability configuration across 2 data centers
- 100GbE connectivity to PACS systems
- NVIDIA Triton Inference Server (containerized)
- Custom HL7/DICOM integration bridge
- Kubernetes with GPU operator for orchestration
- Prometheus, Grafana, and DCGM for monitoring
- Isolated VLAN with air-gapped PHI processing
The deployment exceeded expectations across all metrics. Time-to-preliminary-read dropped 85% from 45 minutes to 7 minutes average. System uptime achieved 99.7%, exceeding the 99.5% SLA requirement. Annual savings reached $2.4M through reduced outsourced radiology coverage. The network passed its first HIPAA audit with zero findings. Beyond the numbers, radiologist satisfaction improved as AI handles routine cases while humans focus on complex interpretations. Overnight coverage quality improved with AI catching findings for morning review, and interpretation consistency improved across all facilities.
— Chief Technology OfficerThe transition to local AI infrastructure was transformative for our organization. We now have complete control over our patient data while running more sophisticated models than we could afford in the cloud. The deployment took 20 weeks from contract to production—faster than our previous cloud migration attempts.
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