PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage

2026-06-03 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

PSEBench is a new 5,074-case benchmark designed to evaluate Large Language Models (LLMs) in patient safety event triage, a critical task of determining reportability under jurisdiction-specific policies. This benchmark addresses the limitations of existing evaluation methods, which often fail to capture evidence-grounded policy reasoning, proactive information seeking for incomplete reports, and principled abstention in ambiguous scenarios. PSEBench employs a policy-grounded construction methodology centered on "clause cards," structured representations that factorize regulatory text into auditable decision specifications. This scalable pipeline, combining clause cards with anchor-driven instantiation and closed-loop verification, generates narratives with by-construction ground truth, including missing information and uncertain variants. Instantiated using Minnesota's 29 Reportable Adverse Health Events, PSEBench includes an agentic evaluation environment. Initial evaluations on 15 representative LLMs demonstrated its utility by revealing consistent capability trends and identifying actionable gaps in current LLM performance for patient safety applications.

Key takeaway

For AI Scientists and Machine Learning Engineers developing LLMs for clinical decision support, particularly in patient safety, you should recognize that traditional benchmarks often fall short in evaluating policy adherence and proactive information seeking. PSEBench provides a robust, verifiable framework to assess LLM capabilities in high-stakes triage tasks, offering by-construction ground truth and an agentic evaluation environment. Consider adopting its policy-grounded construction methodology to build more reliable and auditable LLM applications, thereby identifying and addressing critical performance gaps before deployment.

Key insights

PSEBench offers a verifiable benchmark for LLMs in patient safety triage via policy-grounded construction and agentic evaluation.

Principles

• Regulatory text can be factorized into auditable decision specifications.
• Ground truth for complex policy reasoning can be constructed by design.

Method

A scalable pipeline combines clause cards, anchor-driven instantiation, and closed-loop verification to produce narratives with by-construction ground truth, including missing information and uncertain variants.

In practice

• Evaluate LLMs for high-stakes clinical decision support.
• Identify specific LLM capability gaps in policy adherence.

Topics

• Patient Safety
• Large Language Models
• LLM Benchmarking
• Clinical Decision Support
• Regulatory Compliance
• Triage Systems

Best for: AI Architect, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.