LongHealth: A Question Answering Benchmark with Long Clinical Documents
Lisa Adams Felix Busch Tianyu Han Jean-Baptiste Excoffier Matthieu Ortala Alexander Löser Hugo JWL. Aerts Jakob Nikolas Kather Daniel Truhn Keno Bressem
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.