Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Healthcare AI Applications · Depth: Expert, quick

Summary

A deployment-centered evaluation of a Large Language Model (LLM) system integrated into electronic health records at an academic medical center focuses on predicting user rejection risk. This study addresses the limitations of static benchmarks by training a pre-response classifier to estimate the likelihood of a user rejecting an LLM response based on query content and deployment-specific context. Over 4.5 months of user feedback, the prediction model achieved an AUROC of 0.719. The research highlights that incorporating deployment-specific context, such as provider type, department name, and the specific language model used, significantly enhances the ability to predict user rejection compared to relying solely on query content. This approach demonstrates the feasibility of using such predictions for targeted guardrails and abstention mechanisms in clinical LLM deployments.

Key takeaway

For MLOps Engineers deploying clinical LLM systems, you should integrate pre-response classifiers that utilize deployment-specific context, such as provider type and department, to predict user rejection risk. This allows you to proactively trigger guardrails or enable system abstention for high-risk queries, significantly improving system reliability and user trust. Your evaluation strategy must extend beyond static benchmarks to reflect real-world clinical utility.

Key insights

Predicting user rejection in clinical LLM systems is significantly improved by incorporating deployment-specific context.

Principles

• Static benchmarks often fail to capture real-world user acceptance in clinical LLMs.
• Deployment-specific context, beyond query content, enhances rejection risk prediction.

Method

A pre-response classifier estimates user rejection risk by analyzing query content and deployment context (e.g., provider type, department, LLM used) prior to response generation.

In practice

• Deploy pre-response classifiers to predict clinical LLM user rejection.
• Trigger guardrails based on high predicted rejection risk.
• Implement system abstention for queries with high rejection probability.

Topics

• Clinical LLM Systems
• User Rejection Prediction
• Deployment-Centered Evaluation
• Electronic Health Records
• Guardrails
• Pre-response Classifier

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.