LongHealth: A Question Answering Benchmark with Long Clinical Documents

Abstract

Abstract Recent advancements in large language models (LLMs) offer potential benefits in healthcare, particularly in processing extensive patient records. However, existing benchmarks do not fully assess LLMs’ capability in handling real-world, lengthy clinical data. We present the LongHealth benchmark, comprising 20 detailed fictional patient cases across various diseases, with each case containing 5090 to 6754 words. The benchmark challenges LLMs with 400 multiple-choice questions in three categories: information extraction, negation, and sorting, challenging LLMs to extract and interpret information from large clinical documents. We evaluated eleven open-source LLMs with a minimum of 16,000 tokens and also included OpenAI’s proprietary and cost-efficient Generative Pre-trained Transformers-3.5 Turbo for comparison. The highest accuracy was observed for Mistral-Small-24B-Instruct-2501 and Llama-4-Scout-17B-16E-Instruct, particularly in tasks focused on information retrieval from single and multiple patient documents. However, all models struggled significantly in tasks requiring the identification of missing information, highlighting a critical area for improvement in clinical data. In conclusion, while LLMs show considerable potential for processing long clinical documents, their current accuracy levels are insufficient for reliable clinical use, especially in scenarios requiring the identification of missing information. The LongHealth benchmark provides a more realistic assessment of LLMs in a healthcare setting and highlights the need for further model refinement for safe and effective clinical application. We make the benchmark and evaluation code publicly available.

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