In most industries, a model “hallucination” may result in a minor UX friction. In healthcare, it’s a clinical risk, especially if model outputs inform clinical decision making.
Large Language Models (LLMs) are exceptionally powerful, but they are prone to going haywire without a rigorous evaluation framework. When outputs guide clinical decision-making, “good enough” is a liability. If you are building in this space, your primary product isn’t just the model or its outputs; it’s the eval engine that provides the necessary guardrails to ensure output soundness.
The Stakes of High-Fidelity Data
We saw this tension firsthand while using LLMs to extract insights from Health Information Exchange (HIE) and medical claims data. Our goal was to process messy, often inconsistent inputs: hospital discharge notes, claims line items, and encounter descriptions, to inform our view on patient severity.
As we started development, we quickly learned that without robust evals, the distance between a helpful insight and a noisy misclassification is razor-thin.
Here are the three lessons we learned about building clinical-grade evaluation loops.
Clinical Grounding: Clinician-in-the-Loop is the Starting Line
When clinical judgment is involved, human clinician involvement is still key. We relied on trained, seasoned clinicians to review model outputs early and often.
We discovered that models frequently fall into keyword traps. An LLM might flag a patient as higher severity because of certain diagnosis names detected in the input data, while an expert clinician might have a different take on the patient profile given the full context provided.
We learned to not trust language models to understand clinical nuance out of the box. Use human experts to build gold standard datasets to power model fine tuning.
Maximize Explainability as an Eval Signal
Raw model outputs (especially classification labels) are a black box that is difficult to debug. To build reliable classifiers, we learned the importance of pushing a model to show its work.
We moved toward an eval architecture which pushed our models to output not just a final decision, but a set of structured metadata and reasoning steps. By requiring the LLM to cite the specific line in the HIE data that drove its conclusion, we created a secondary signal for our eval process.
Even if the metadata itself is subject to hallucination, it provides a useful paper trail that makes it significantly easier for humans to identify logic loops or source misattributions during the evaluation phase.
Implement Rigorous Regression Testing
In software, we test for broken code. In healthcare AI, it is paramount to test for semantic drift. Every time one updates a prompt or switches model versions, one risks large, unexplained changes in classification outputs.
Establish a growing eval dataset representing the nuances of your most complex clinical cases. Every model tweak should undergo Regression Testing against these cases to ensure that optimizing for model integrity in one domain does not hurt it in another.
Conclusion
Building production use cases deploying LLMs to aid clinical judgment requires a shift from “moving fast” to “moving with precision.”
● Phase 1: Human-in-the-loop to define the clinical “truth.”
● Phase 2: Structured outputs to enable explainable evals.
● Phase 3: Continuous regression testing to prevent performance drift.
If you want to move the needle in healthcare, don’t just focus on the model’s capabilities. Focus on the guardrails that prove those capabilities are consistent, safe, and clinically sound.
