Hospitals & Health Systems
Clinical AI you can deploy without your data leaving your data center.
Inpatient workflows, regulatory constraints, and Epic-scale data — all assumed, all engineered for.
Hospitals run on data that cannot leave their walls. RonanLabs builds AI infrastructure that respects that constraint by default — synthetic data for development, custom models for deployment, knowledge graphs for retrieval — all of it sized for inpatient operations.
The data problem in hospitals & health systems
Hospital data is the most sensitive data in healthcare. PHI, billing, imaging, genomics, and clinician notes all live behind layered controls — and most off-the-shelf AI systems require sending that data outside the building to function.
On top of privacy, inpatient data is structurally hard. Epic, Cerner, and MEDITECH each store the same concepts differently. ICD/CPT/SNOMED coverage is uneven across departments. Free-text notes carry critical information that structured fields don't capture.
Generic foundation models trained on the open internet generalize poorly to inpatient workflows. They miss clinical context, hallucinate dosages, and don't respect the local protocols that distinguish your hospital from any other.
Off-the-shelf vendor AI deployed cloud-side creates an audit and compliance overhead that scales linearly with use. Many hospitals find it easier to say no than to track every inference.
What we deliver
Synthetic data
Clinical-grade synthetic patient records validated against your real-world distributions for development, model training, and regulatory submissions — without exposing real PHI.
Custom models
Foundation and specialty models fine-tuned on your de-identified data, deployed on-premise behind your firewall, owned by you.
Knowledge & retrieval
A knowledge graph derived from your protocols, formularies, and clinical pathways, queryable by language models for retrieval-grounded answers.
See the full architecture for how these layers fit together.
Common deployments
Documentation assistance
On-premise model that drafts notes from dictation, grounded in the hospital's own templates and order sets.
Predictive scheduling
Capacity and length-of-stay forecasts trained on the hospital's own utilization patterns.
Clinical decision support
Retrieval-grounded answers using the hospital's protocols, formulary, and recent literature, with explicit citations.
Synthetic data for research
Statistically valid patient cohorts for IRB-exempt model development and regulatory work.
Frequently asked
Discuss your hospitals & health systems deployment
Tell us about your data, your constraints, and your workflows. We'll design the layers around them.
Start the conversation