Guidelines to quality care plans.

Those tools are very good at answering "what does the evidence say?" when a clinician asks. This is a different job, in two ways.

It knows your organization. A reference tool gives the same answer in every hospital. This system ingests your own protocols alongside the published guidelines: your formulary, your referral pathways, which imaging you can do in house, how your clinics like to follow up. The plan it drafts is your organization's plan, not a generic one.

It produces something that does work. A reference tool gives you a paragraph to read and then re-type as orders. This gives you a plan in the patient's record, with activities detailed enough to drive orders, follow-ups, and outreach. Reference tools stay useful for reading. This is the layer that turns evidence and your own policies into the plan.

It doesn't pop up. It drafts a plan when you ask for one, for the patient you're seeing, with all of that patient's guidelines considered at once. The tighter blood pressure target because of the kidney disease. The metformin note because of the eGFR. The follow-up interval your own protocol calls for. Click any activity and you see the sentence in the guideline it came from. Where two sources disagree, you see that too, with the reasoning, and you decide.

The gap between what guidelines recommend and what patients actually receive is well documented. The problem was never that clinicians don't know the guidance. It's that reconciling six hundred pages per patient doesn't fit in the visit. This is meant to give that time back to the patient.

No, and it's built so it can't. Decisions come from reviewed decision tables in a rules engine, not from a language model's opinion. Nothing reaches a patient's record without a clinician approving it, and the record carries that approval, following the HL7 AI Transparency on FHIR standard. The system drafts. You decide. The plan is yours, and the record says so.

It runs on Red Hat OpenShift AI in your environment, on premises or in your cloud, with your choice of models, including models that never leave your walls. Patient data is not sent to a vendor.

Each AI agent runs in a kernel-level sandbox with a strict allow list, so the piece that talks to the language model physically cannot reach the patient record, and the piece that reads the record cannot reach the model. Every connection is logged. The clinician's view opens inside the EHR through SMART on FHIR, so there is no second login and no second system to live in.