LLM.co · Private AI & Custom AI DevelopmentPrivate AI for healthcare

Why health systems choose private AI for healthcare

Clinicians and revenue-cycle staff spend a large share of their day gathering information that already sits in the record. AI can do that gathering well. The concern is where protected health information goes in the process. A public AI service adds a new business associate, a new data flow and a new set of retention terms to review.

Private AI for healthcare runs open-weight models inside your own network or cloud account. PHI stays in the environment your privacy and security teams already govern, under the access controls, audit logging and risk analysis your HIPAA program already covers.

HIPAA compliant AI starts with the program you already run

Searches for HIPAA compliant AI usually mean a tool that fits inside a covered entity's existing safeguards. No software is compliant on its own. Compliance belongs to the organization. We build each system to support your program in specific ways.

• Minimum necessary. Each use case is scoped to the data elements it needs, and retrieval is limited to those fields and encounters.
• Access controls. Users see only what their EHR role already allows.
• Audit controls. Every request is logged with the user, the patient context, the records retrieved and the output.
• Business associate agreement. LLM.co makes a BAA available where our work involves PHI, and we design builds so our engineers need as little PHI access as possible.

Clinical documentation AI and revenue-cycle workflows

The best fits are tasks where staff assemble information for someone else to review.

• Chart summaries for handoffs, consults and transitions of care, with each statement linked to its source note.
• Prior-authorization packets built from payer criteria and the supporting documentation in the record.
• Coding support that suggests codes alongside the documentation behind each one.
• Drafted patient letters and portal replies that staff review, edit and send.

What to pilot first, and what to avoid

Start with an administrative or documentation task that a person already reviews, such as prior-authorization packet assembly for one service line. Validate it with clinicians and coders on historical cases, measure accuracy against what was actually submitted, and only then run it on live work.

Avoid first projects that produce diagnoses, treatment recommendations or triage decisions without clinician review. Those uses raise patient safety questions and may fall under FDA oversight of clinical decision support software. Avoid screen scraping too. We connect through FHIR, HL7 and vendor-supported EHR interfaces.

Custom AI development for your environment

Our custom AI development work connects to Epic, Oracle Health or athenahealth through supported interfaces and deploys on-premises or in your private cloud. Outputs are drafts. Nothing is written back to the record until a person accepts it. You own the code, prompts and evaluation sets.