There is a difference between an AI that can talk intelligently about healthcare finance and one that can actually do healthcare finance at the appropriate level of rigor.

Consider a denied claim. An AI agent can draft a polished appeal while failing to address the actual reason for the denial. Or consider a physician enterprise trying to understand falling collections. An agent might produce a beautiful analysis while confusing charges with collections or overlooking a change in provider attribution.

The spreadsheet looks right. The conclusions are wrong.

This is the problem with evaluating AI in technical fields: an answer can be convincing and mostly correct while getting the one important thing wrong.

Our approach starts with people who know the work. We bring together domain intelligence—subject-matter experts who understand healthcare finance—and customer delivery experts who understand how the work gets done in practice. Together, they define what good performance looks like, identify subtle failure modes, and turn their judgment into evaluations that can be applied consistently.

We use three complementary mechanisms: rubrics, golden examples, and pairwise comparisons.

Rubrics: What Are We Actually Rewarding?

Suppose an agent is assigned to work a denial queue. It needs to identify the denial reason, review the relevant documentation, and determine the appropriate next action.

Our domain experts help define what constitutes a technically correct decision. Our delivery experts ensure the criteria reflect the actual workflow: whether the proposed action is useful, whether required steps have been completed, and whether the result meets the customer's needs.

The result is a rubric that breaks performance into explicit criteria. Did the agent identify the correct denial reason? Did it use the relevant evidence? Was its proposed action appropriate? Did it follow the required workflow?

Rubrics can also serve as reward signals for model optimization. In reinforcement learning, a reward provides feedback about which outcomes are preferable. In healthcare finance, success often requires judgment that cannot be captured by a single automated test. A well-designed rubric makes that judgment more structured and potentially usable for improving model behavior.

The catch is that a rubric can reward the wrong thing very consistently. If we primarily reward completeness and persuasive writing, we might get excellent appeal letters that don't address the denial. That's why the criteria need to be grounded in expert judgment, not simply in what is easiest to score.

Golden Examples: Show Us What Good Looks Like

A rubric defines the criteria. A golden example puts them into practice.

Our experts curate reference cases that pair a task with a vetted answer, decision, or successful outcome. They bring the judgment needed to distinguish genuinely correct work from something that merely looks plausible.

Consider three superficially similar denials: one requires correcting a claim detail, another requires addressing missing authorization, and a third involves medical necessity. An agent that responds to all three by writing a generic appeal has learned to imitate the work, not do it.

For physician enterprise analytics, a reference case might test whether an agent uses the right reporting period, preserves provider attribution, distinguishes work relative value units (wRVUs) from collections, and applies the correct compensation assumptions.

Delivery experts help ensure these examples represent the situations customers actually encounter, including workflow constraints and inconvenient edge cases. The resulting golden set can then serve as a benchmark and catch regressions when we change a model, its instructions, or its tools.

Pairwise Comparisons: Which Answer Is Better?

Sometimes assigning a score is harder than comparing two answers.

Imagine two agents investigating a decline in physician-group collections. One produces a concise analysis identifying a change in payer mix. The other offers more possible explanations but makes a questionable assumption about the data. Which is better?

Pairwise comparison asks an evaluator to compare two outputs for the same task and select the better one, with options for a tie or for rejecting both. People may struggle to decide whether an answer deserves a seven or an eight out of ten but find it easier to judge which of two answers is more accurate or useful.

Our domain experts provide the subject-matter judgment needed to compare the outputs; our delivery experts help assess whether the difference matters in the actual job. Those comparisons can help rank models, prompts, and agent configurations. Preference data can also inform training methods such as direct preference optimization, which trains models using preferred and dispreferred outputs.

There is a catch here, too: relative preference is not the same as correctness. One bad answer can be better than another bad answer. Pairwise comparisons work best alongside rubrics that establish minimum standards for accuracy and task completion.

Turning Expertise Into a Repeatable Evaluation

The important part is not just that we use rubrics, golden examples, and pairwise comparisons. It is how we produce them.

Domain experts bring the technical knowledge needed to judge whether the work is correct. Customer delivery experts bring the operational context needed to judge whether it solves the actual problem. Together, they turn experience that might otherwise live in people's heads into explicit criteria, vetted reference cases, and consistent comparative judgments.

That gives us a repeatable way to test realistic work, identify failure modes, and measure whether changes to the system produce actual improvements.

No single score proves that an AI system can be trusted. What matters is the evidence behind it: whether it handles representative cases correctly, respects the constraints of the workflow, and continues to perform when the next case looks different.

In healthcare finance, that is the difference between an AI that looks like it knows what it is doing and one that has been tested to do the job.

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