PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage
arXiv:2606.05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts. Although LLMs may support this workflow, reliable evaluation is limited by the lack of benchmarks to capture evidence-grounded policy reasoning, proactive information seeking for incomplete reports, and principled abstention in irreducibly ambiguous cases. We address this gap with a policy-grounded construction methodology centered on the clause card, a st
The increasing sophistication of large language models (LLMs) requires robust and specialized benchmarks to validate their performance in critical, high-stakes applications like patient safety.
This benchmark addresses a significant gap in evaluating LLMs for patient safety event triage, which is crucial for their reliable deployment in healthcare and ensuring patient well-being.
The development of 'PSEBench' provides a verifiable and controllable method for assessing LLMs' ability to reason, seek information, and abstain in ambiguous medical scenarios, thereby accelerating their responsible integration into healthcare workflows.
- · AI developers
- · Healthcare providers
- · Patients
- · Medical AI researchers
- · Manual patient safety review processes
- · Developers of unverified medical AI
Improved accuracy and efficiency in patient safety event triage through validated AI assistance.
Increased trust and adoption of AI tools within critical healthcare operations, potentially reducing human error and workload.
The establishment of industry-wide standards for AI performance in regulated applications, extending beyond healthcare to other high-stakes domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI