Generative Large Language Models for Question Answering from Electronic Health Records: A Systematic Review

Abstract

This systematic review synthesises evidence on the performance, methodological quality, and clinical applicability of generative large language models for question-answering tasks using electronic health record data. We searched PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for original empirical studies evaluating decoder-only or encoder-decoder transformer architectures applied to clinical question-answering over EHR data. Risk of bias was assessed using an adapted QUADAS-2 framework with signalling questions modified to address LLM-specific methodological concerns including data contamination, LLM-as-judge bias, and reproducibility. Due to anticipated heterogeneity in task definitions and evaluation metrics, findings are synthesised narratively rather than through meta-analysis. A preliminary clinical implementation decision framework is developed to translate research findings into actionable guidance for healthcare institutions evaluating these systems for deployment.

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