Background: 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.
Methods: We present the LongHealth benchmark, comprising 20 detailed
fictional patient cases across various diseases, with each case containing
5,090 to 6,754 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.
Results: We evaluated nine open-source LLMs with a minimum of 16,000 tokens
and also included OpenAI's proprietary and cost-efficient GPT-3.5 Turbo for
comparison. The highest accuracy was observed for Mixtral-8x7B-Instruct-v0.1,
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 interpretation.
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.

LLMs 在处理医疗保健领域的大量患者记录方面具有潜在的优势，但现有的基准测试不能充分评估 LLMs 在处理真实世界中的冗长临床数据方面的能力。通过提供包含 20 个详细虚构病例的 LongHealth 基准测试，每个案例包含 5090 至 6754 个单词，我们评估了九个开源 LLMs，并将 OpenAI 的专有成本效益高的 GPT-3.5 Turbo 作为比较。结果显示，Mixtral-8x7B-Instruct-v0.1 在从单个和多个患者文档中检索信息的任务上表现最好，但所有模型在需要识别缺失信息的任务中遇到了很大困难，强调了临床数据解释中需要改进的关键领域。然而，当前 LLMs 的准确性水平对于可靠的临床使用是不足的，尤其是在需要识别缺失信息的情景中。LongHealth 基准测试提供了在医疗保健环境中更真实的 LLMs 评估，并强调了进一步改进模型以实现安全有效的临床应用的需要。我们公开提供基准测试和评估代码。