METRIS™
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AI Decision Consulting for Healthcare
Health plans, hospitals and provider groups already run AI in prior authorization, coding, claims and patient messaging. Before your leaders renew a contract or expand a use case, we measure whether your organization is ready for that AI: who owns it, how the work changed around it, what limits are enforced, whether your people catch its mistakes, and whether anyone can show what it delivered.
They renew the vendor contract, expand the use case to a new line of business, add limits, or stop it. Each of those decisions needs evidence that the AI is delivering what the organization bought it for.
Many times a day, physicians, nurses, reviewers and coders accept what the AI produced, correct it, override it or escalate it. A prior authorization reviewer who approves a wrong AI recommendation has made an AI decision. So has a coder who accepts a suggested code without checking the chart.
A leader's decision is only as sound as the everyday decisions underneath it. That is why our measurement and our training belong together.
Organizational AI readiness is not merely the absence of AI failures in one use case or one department. It is the measured state of every AI use case, across every department, delivering the business outcome the organization expected from it, and the organization's ability to keep that state as its AI changes.
Services
We measure how ready your organization is for the AI it already uses, one use case at a time and across every department, and we show you which condition is holding you back. The service has two core parts, and a third that you can choose to add.
We review your AI policies, vendor contracts, approval records and oversight practices against the rules that apply to you, including CMS requirements, Section 1557, state law and the Joint Commission's responsible use of AI standards. We show you who owns each AI system, where ownership or oversight is missing, and what to put in place first.
Every day, the people who work alongside AI decide what to do with its output: accept it, correct it, override it or escalate it. We run live trainings for them: physicians, nurses, prior authorization reviewers, coders, and the managers who oversee them. Each training is built from cases in your own workflows. Your teams learn to recognize when the AI is wrong or has left something out, when to override it, and whom to escalate to. We measure what your people can do before and after the training, so you see the change in what they do rather than a count of completed modules.
Readiness does not stay put. A vendor updates its model, a new AI use case goes live, a policy is revised, or experienced reviewers leave and new ones arrive, and each of these can quietly undo the readiness you built. If you want us to stay with you, we keep measuring: we measure again when those changes happen, run regular practice cases so your teams' skills don't fade, and give your leadership and board a readiness report they can act on. You decide whether to continue and how often.
What you receive: a readiness baseline, a plan that names what to fix and who owns each fix, training for the teams that need it, and a second measurement once the changes are in place. If you choose continuous measurement, you also receive ongoing reports showing whether your readiness is holding, improving or slipping.
Frameworks
We measure your starting point, evaluate what your AI changed and what that change is worth, judge how far the evidence behind that value can be trusted, reconcile the trusted value against what was promised and spent, improve what the measurement shows, and sustain the gains by measuring again whenever your AI, your vendors or your people change.
An AI system can work technically while the organization around it is not ready. We look at five conditions for every use case:
Someone owns the AI system and is accountable for what it does.
The work has been redesigned around the AI, with a clear handoff whenever a person needs to step in.
Enforced limits on what the AI may do on its own.
People know what the AI is allowed to do, can catch its mistakes, and know when to step in.
There is a before number and an after number, so the value can be shown rather than assumed.