About the practice
Healthcare context. Data discipline. Practical AI.
ref(health) Consulting connects 21 years of healthcare operating experience with data, analytics, insights, dbt, and the fast-moving work of building useful AI systems.
Healthcare is not one data domain.
The practice is grounded in work across payer, provider, wellness, and navigation environments. That range matters because the same metric, workflow, or AI idea can behave differently depending on the operating model around it.
- Payer sideUnderstand the questions around claims, cost, quality, utilization, network, members, and regulation.
- Provider sideUnderstand the pressure around clinical operations, access, quality, revenue, referrals, and care delivery.
- Wellness and navigationUnderstand the signals between engagement, recommendations, service use, and outcomes.
- Data, analytics, and insightsConnect technical delivery to the decision the business actually needs to make.
The work has to cross the room.
Data teams need business context. Business teams need a clear explanation of what the data can support. Executives need a decision they can fund and measure.
- Business questionWhat needs to change, what is it worth, and who owns the decision?
- Data realityWhat exists, what is trustworthy, and what needs to be repaired or made visible?
- Delivery choiceWhat should be modeled, automated, orchestrated, governed, or left alone?
- Operating handoffWhat will the team measure, maintain, review, and improve after the engagement?
Build the company’s Data AI Operating System.
AI work is strongest when it is connected to the data landscape, the operating workflow, the model strategy, the controls, and the people who will use it.
Model/provider orchestration. Orchestrate multiple models across labs and providers, or go deep inside one provider when policy, integration, current commitment, or delivery speed makes that the right path.
Operating agents. Create practical patterns for research, task guidance, executive review, strategy feedback, and the operating rhythm around knowledge work.
Data landscape analysis. Make sources, models, ownership, redundancy, risk, and documentation gaps visible before the company automates the wrong work.
Documentation and resilience. Put rigor around repository documentation, handoffs, role coverage, single points of failure, and the controls that keep systems useful.
Senior judgment at the point of leverage.
Bring the problem that keeps crossing teams.
Share the business decision, data systems, deadline, and who needs to act. The next step can be a paid diagnostic, focused build, or fractional engagement.