{
  "id": 5334,
  "url": "https://arxiv.org/abs/2604.23949v1",
  "title": "Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs",
  "summary": "Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization trends during large-scale healthcare disruptions (e.g., operational failures or pandemics). Forecasting models can assist in this task by analyzing large volumes of resource-related data at the facility level, but they must be reliable for decision-making under real-world data conditions. Recent work shows that large language models (LLMs) can inco",
  "authors": "Rhea Makkuni, Ananya Joshi",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-27T01:56:55.000Z",
  "fetched_at": "2026-07-14T16:31:40.221Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5334",
  "original_url": "https://arxiv.org/abs/2604.23949v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}