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

Researchers developed a pre-response classifier for clinical LLMs that predicts user rejection risk with 71.9% accuracy by leveraging deployment-specific context like provider type and department. This deployment-centered evaluation approach addresses a critical gap in clinical AI assessment, moving beyond static benchmarks to measure real-world user acceptance in a healthcare system.

Clinical LLM integration represents a frontier in AI applications where traditional benchmarking methodologies fail to capture real-world utility. This research tackles a fundamental problem: existing evaluation frameworks measure correctness in isolation but miss why users actually reject system outputs in operational settings. The study's prospective analysis over 4.5 months of genuine user feedback from an academic medical center provides rare empirical grounding that most AI research lacks.

The key innovation centers on incorporating deployment-specific context—provider type, department, language model version—rather than relying solely on query content. This contextual integration improved predictive accuracy significantly, suggesting that clinical AI performance varies substantially across institutional contexts. The 0.719 AUROC demonstrates reasonable predictive power for identifying problematic interactions before they occur, enabling proactive guardrail deployment.

For healthcare systems implementing LLM solutions, this work validates the importance of monitoring and understanding user rejection patterns rather than trusting aggregate accuracy metrics. The ability to identify high-risk queries enables targeted interventions that improve provider trust and system utility. Two proposed downstream applications—guardrail triggering and abstention strategies—provide practical pathways for reducing unhelpful outputs.

The research establishes deployment-centered evaluation as a necessary complement to benchmark-based assessment for clinical AI. As healthcare organizations accelerate LLM adoption, understanding which contextual factors drive user rejection becomes increasingly valuable for system refinement and safe deployment scaling. Future work addressing why certain provider-department combinations show higher rejection rates could yield deeper insights into clinical decision-support effectiveness.

• →Deployment-specific context (provider type, department, model version) significantly improves prediction of user rejection risk compared to query content alone.
• →Prospective evaluation over 4.5 months of real clinical use revealed 0.719 AUROC for pre-response rejection prediction, demonstrating feasibility of this approach.
• →Traditional benchmarks measuring correctness miss critical adoption barriers; real-world user feedback captures actual system utility in clinical settings.
• →Guardrail triggering and abstention strategies enabled by rejection prediction could reduce harmful outputs and improve clinical provider trust.
• →Institutional context matters: clinical AI performance varies substantially across provider types and departments, requiring localized evaluation frameworks.