IFO4
Loading...EHR cloud migrations, medical imaging archives, clinical AI infrastructure, and telehealth platforms. Healthcare cloud spend is growing faster than any other sector.
The move from on-premise EHR to cloud is the largest infrastructure investment most health systems will make this decade.
Epic Systems running on Azure, AWS, or GCP. Hosting costs, data replication, disaster recovery, and performance tuning for the platform that powers over 250 million patient records.
Oracle Health cloud migration economics. Evaluating the total cost of moving from on-premise Cerner to Oracle Cloud Infrastructure including data migration, integration, and ongoing operations.
Clinical data warehouses, analytics platforms, and reporting infrastructure. The storage and compute cost of making EHR data queryable for research, operations, and regulatory reporting.
FHIR APIs, HL7 integration engines, and health information exchange (HIE) platforms. The cost of making patient data flow between systems, providers, and payers.
Radiology, cardiology, pathology, and dermatology all generate massive imaging datasets that must be stored, accessed, and retained for years.
Picture Archiving and Communication Systems hold petabytes of diagnostic images. Cloud migration decisions involve storage tiering, retrieval latency, and compliance with retention requirements.
DICOM files range from kilobytes to gigabytes per study. Storage cost governance must account for hot, warm, and cold tiers based on clinical access patterns and legal retention periods.
3D reconstructions, AI-assisted detection, and quantitative imaging require significant compute. Governing the GPU and storage cost of next-generation imaging workflows.
Consolidating imaging data from multiple departments and modalities into a single archive. The cost of normalization, deduplication, and long-term management across enterprise imaging.
Every AI model deployed in a clinical setting carries compute cost that scales with patient volume.
Running AI models for radiology reads, pathology analysis, and clinical decision support in production. Cost per inference, GPU utilization, and model versioning economics.
Model training, fine-tuning, and validation on clinical datasets. GPU cluster cost, data preparation, annotation labor, and the iterative expense of improving clinical AI performance.
Clinical AI can run at the edge (in the operating room, at the imaging device) or in the cloud. Each deployment model carries different cost, latency, and compliance tradeoffs.
Post-pandemic telehealth is permanent infrastructure. Cost governance must scale with adoption.
Real-time video encoding, WebRTC infrastructure, recording storage, and bandwidth cost. Scaling telehealth platforms from hundreds to millions of visits per year.
IoT device data ingestion, time-series storage, alerting infrastructure, and integration with clinical workflows. The cost of continuous patient monitoring at scale.
FDA-cleared digital therapeutics applications require clinical-grade infrastructure. Compliance, uptime guarantees, data security, and patient engagement features all carry cost.
Authentication, scheduling, messaging, records access, and payment processing. The operational cost of patient-facing digital experiences that must be always available.