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This paper addresses the underexplored NLP tasks of structured tabular reporting from nurse dictations and medical order extraction from doctor-patient consultations, which are critical for reducing healthcare provider documentation burden. The authors evaluate the performance of both open- and closed-weight LLMs on these tasks using private and newly released open-source datasets (SYNUR and SIMORD). They also propose an agentic pipeline for generating realistic, non-sensitive nurse dictations to facilitate structured extraction of clinical observations.
LLMs can now automate structured reporting from nurse dictations and medical order extraction from doctor-patient consultations, thanks to two new open-source datasets and an agentic pipeline for generating realistic training data.
Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. Nonetheless, two high-impact NLP tasks - structured tabular reporting from nurse dictations and medical order extraction from doctor-patient consultations - remain underexplored due to data scarcity and sensitivity, despite active industry efforts. Practical solutions to these real-world clinical tasks can significantly reduce the documentation burden on healthcare providers, allowing greater focus on patient care. In this paper, we investigate these two challenging tasks using private and open-source clinical datasets, evaluating the performance of both open- and closed-weight LLMs, and analyzing their respective strengths and limitations. Furthermore, we propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. To support further research in both areas, we release SYNUR and SIMORD, the first open-source datasets for nurse observation extraction and medical order extraction.
Microsoft Research