Healthcare Agent

NurseSim-Triage evaluates an agent's ability to perform safety-critical clinical triage in Emergency Department scenarios. The agent receives patient presentations (chief complaint, vital signs, demographics, medical history) and must assign the correct Manchester Triage System category (1-5) while providing clinical reasoning. Tasks assess: Risk Stratification - Correctly identifying life-threatening conditions (Category 1: Cardiac arrest, Anaphylaxis, Sepsis) Demographic Context Integration - Weighing age and gender as risk modifiers (e.g., chest pain in 72M vs 20M) Safety-Critical Decision Making - Avoiding dangerous under-triage that could delay life-saving treatment Clinical Reasoning - Explaining triage decisions with medically sound rationale The benchmark includes 15 gold-standard scenarios spanning all 5 MTS categories, evaluated by GPT-5.2 judges for both accuracy and safety complia

The green agent evaluates whether a medical AI (purple agent) can correctly perform FHIR-based clinical reasoning tasks. These tasks fall into three categories: Query tasks: Retrieve and compute patient information from the FHIR server, such as identifying patients, calculating age, and extracting recent or averaged lab values. Write tasks: Create valid FHIR resources, including vital sign observations and consultation or lab service requests, with correct clinical structure and content. Conditional (protocol-driven) tasks: Apply clinical decision logic based on patient data (e.g., electrolyte levels or test recency) and, when criteria are met, generate appropriate medication orders or lab requests according to predefined medical protocols. Overall, the green agent checks data retrieval accuracy, clinical calculations, correct use of FHIR APIs, and adherence to clinical protocols, validating each task with task-specific grading logic.