Submission Abstract: NurseSim-RL
Project Name
NurseSim-RL: A Healthcare Agent Environment for Clinical Triage
Abstract (for submission form)
NurseSim-RL is a Gymnasium-compatible reinforcement learning environment that simulates clinical triage in an Emergency Department (A&E) setting. The environment challenges AI agents to assess patients based on natural language chief complaints and vital sign data, then assign appropriate triage categories (1-5) according to the Manchester Triage System (MTS).
Key Contributions:
- Novel Healthcare RL Environment: A safety-critical environment where incorrect decisions carry severe penalties, modeling real-world clinical risk.
- Synthetic Clinical Dataset: 500+ diverse patient scenarios covering all 5 MTS categories, with realistic vital sign variations.
- Fine-Tuned LLM Agent: A Llama 3.2 3B model trained using Unsloth (4-bit QLoRA) demonstrating rapid domain adaptation (2.8 → 0.08 loss in 100 steps).
- Reproducible Pipeline: Complete training notebook, Dockerfile, and Gradio demo for immediate deployment.
Evaluation Focus: Healthcare Agent Track - The benchmark evaluates clinical reasoning, safety awareness, and resource allocation under time pressure.
Impact: This environment enables development and testing of AI agents for healthcare decision support, with direct applications in triage training, clinical education, and NHS workforce optimization.
Suggested Answers for Form Fields
Participation Category: Create a new benchmark
Evaluation Track(s): Healthcare Agent
Specific Benchmarks: N/A (new benchmark)
Demo Video Title: "NurseSim-RL: AI Triage Agent Demo - OpenEnv Challenge 2026"