Healthcare AI consulting and automation.
RVNOPS provides healthcare AI consulting for leaders who need a clear plan. We help your team choose useful projects, get the data ready, set rules for safe use, and put the work into daily practice.
What usually goes wrong
The board asks for an AI plan, so the company starts several pilots. The pilots never reach daily work because nobody owns the next step. Staff get new tools but keep using old spreadsheets because the tools do not fit their jobs. Data is spread across the CRM, medical record, call system, and finance files. It is not ready for an AI project. There are also no clear rules for safe use. Staff may paste patient or company data into public tools without knowing the risk. Vendor claims move faster than privacy, security, and legal review.
What Kevin has done before
Kevin Plumlee is the principal AI architect at RVK AI. He designs AI voice and chat agents for healthcare providers. At Dreamscape, he built proprietary AI and automation tools. At Unlock Health, he brought AI and machine learning into service delivery. Machine learning is software that finds patterns in data. Kevin works directly with ChatGPT, Anthropic Claude, LangChain, and Cursor to build, test, and manage AI systems.
- years in healthcare growth
- 15+
- healthcare organizations
- 300+
- in managed media
- $500M+
- private equity transactions
- 2
What we plan with your team
We start with the work your staff does today. Then we rank each idea by value, effort, data needs, and risk. We guide your team as they build the first projects inside real workflows. Each project has an owner, a test plan, and a way to measure results.
- A ranked list of AI projects, with value, effort, risk, and an owner for each one.
- A data and systems readiness plan for the CRM, medical record, call system, and other key tools.
- An AI governance policy that states what staff may use, what data stays out, and when a person must review the work.
- Workflow designs and test plans for the first projects your team will build.
- A vendor review checklist for data use, security terms, contracts, and support.
- Team training, written guides, and a results report your leaders can keep using.
How we plan a healthcare AI strategy
Choose the first projects
We list the tasks that take time, delay care, or cause missed follow-up. Then we score each idea by value and effort. We also check its data needs and risk. The goal is a short list your team can finish and measure.
A useful first project may draft routine messages, sort documents, summarize calls, or route work. A person stays in charge of choices that affect care, access, or money.
Prepare the data and systems
AI cannot fix records that are missing, repeated, or stored in the wrong place. We map the data needed for each project. We check who can access it and how it moves between systems.
Your team gets a list of work to finish before a build starts. This may include cleaning CRM fields, linking call records, or setting one source for approved content.
Set rules for safe AI use
AI governance means the rules used to choose, use, and review AI. We state which tools staff may use. We also state what they may put into those tools. Protected health information (PHI), passwords, and private company data need strict limits.
The policy names when a person must review the result. We also review vendor contracts, data use terms, security duties, and how the vendor handles stored data.
Build and test in real workflows
We guide the people who will use the tool as they build it. Tests use real work examples with sensitive details removed when needed. Staff check if the result is correct, useful, and clear.
We track errors, time saved, use by staff, and the effect on the work. The project moves forward only when the team can run it safely.
Train the team to own it
Training covers the tool, the rules, and the review steps. Staff practice common tasks and learn when to stop and ask for help.
Leaders get a simple review schedule. Your team can update prompts, check results, train new staff, and decide when another AI project is ready.
What your team owns
- A clear list of approved AI tools and uses.
- The prompts, workflow maps, test cases, and review steps.
- Rules for patient data, company data, and human review.
- Named owners who check quality, safety, cost, and use.
- Training materials that help new staff follow the same process.
Questions about this service
How do we choose the best healthcare AI projects?
Start with a clear work problem, not a tool. Look for tasks that happen often, take staff time, and have a result you can check. Then compare value, effort, data needs, and risk. The best first project has an owner, enough clean data, and a clear review step. It should fit the systems and work your team already uses.
What is AI governance in healthcare?
AI governance is the set of rules for choosing, using, and reviewing AI. It covers approved tools, allowed data, human review, vendor contracts, security checks, and quality tests. It should also name who owns each rule. Clear governance helps staff know what they may do and when they need approval before using AI.
Can healthcare staff put patient information into AI tools?
Staff should not place patient information into an AI tool unless the company has approved that exact use. The privacy, security, and legal teams should review the vendor, contract, data handling, and access controls first. Staff also need written rules and training. Public AI accounts should never be treated as approved places for protected health information.
How long does a healthcare AI project take?
The schedule depends on the workflow, data, systems, vendor review, and level of risk. A focused project with clean data can move faster than one tied to several systems. We first define the work and readiness steps. Then we set a build and test plan. We do not set a launch date until the main risks and owners are clear.
How do we know if an AI pilot is working?
Set the measure before the pilot starts. Check quality, error rates, staff use, review time, and the result of the work. For intake, that may include correct routing and complete notes. For office work, it may include fewer manual steps. Staff feedback matters too. A pilot is useful only if the team can run it in daily work.
Let's talk about which AI is worth the money.
Tell us where AI could help your team. Kevin Plumlee reads every message and usually replies within one business day.