Michalowski Martin, Topaz Maxim, Peltonen Laura Maria
School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.
School of Nursing, Columbia University, New York, New York, USA.
J Adv Nurs. 2025 Mar 24. doi: 10.1111/jan.16911.
To explore the potential of multimodal large language models in alleviating the documentation burden on nurses while enhancing the quality and efficiency of patient care.
This position paper is informed by expert discussions and a literature review.
We extensively reviewed nursing documentation practices and advanced technologies, such as multimodal large language models. We analysed key challenges, solutions and impacts to propose a futuristic multimodal large language model-driven model for nursing documentation.
Multimodal large language models offer transformative capabilities by integrating multimodal audio, video and text data during patient encounters to dynamically update patient records in real time. This reduces manual data entry, enabling nurses to focus more on direct patient care. These systems also enhance care personalisation through predictive analytics and interoperability, which support seamless workflows and better patient outcomes. While predictive analytics could improve patient care by identifying trends and risk factors from nursing documentation, further research is required to validate its accuracy and clinical utility in real-world settings. Ethical, legal and practical challenges, including privacy concerns and biases in artificial intelligence models, require careful consideration for successful implementation.
Transitioning to multimodal large language model-driven documentation systems can significantly reduce administrative burdens, improve nurse satisfaction and enhance patient care. However, successful integration demands interdisciplinary collaboration, robust ethical frameworks and technological advancements.
Implementing multimodal large language models could alleviate professional burnout, improve nurse-patient interactions, and provide dynamic, up-to-date patient records that facilitate informed decision making. These advancements align with the goals of patient-centred care by enabling more meaningful engagement between nurses and patients.
The problem being addressed is the administrative burden of nursing documentation. We suggest that multimodal large language models minimise manual documentation, enhance patient care quality and significantly impact nurses and patients in diverse healthcare settings globally.
探讨多模态大语言模型在减轻护士文档负担的同时提高患者护理质量和效率的潜力。
本立场文件基于专家讨论和文献综述撰写。
我们广泛回顾了护理文档实践和先进技术，如多模态大语言模型。我们分析了关键挑战、解决方案和影响，以提出一个未来主义的多模态大语言模型驱动的护理文档模型。
多模态大语言模型通过在患者诊疗过程中整合多模态音频、视频和文本数据来实时动态更新患者记录，从而具有变革性能力。这减少了人工数据录入，使护士能够将更多精力集中在直接的患者护理上。这些系统还通过预测分析和互操作性增强护理个性化，支持无缝工作流程并改善患者结局。虽然预测分析可以通过从护理文档中识别趋势和风险因素来改善患者护理，但需要进一步研究以验证其在实际环境中的准确性和临床效用。伦理、法律和实际挑战，包括隐私问题和人工智能模型中的偏差，需要仔细考虑才能成功实施。
向多模态大语言模型驱动的文档系统过渡可以显著减轻管理负担，提高护士满意度并改善患者护理。然而，成功整合需要跨学科合作、强大的伦理框架和技术进步。
实施多模态大语言模型可以减轻职业倦怠，改善护患互动，并提供动态、最新的患者记录，便于做出明智的决策。这些进步通过使护士和患者之间能够进行更有意义的互动，符合以患者为中心的护理目标。
所解决的问题是护理文档的管理负担。我们建议多模态大语言模型将手动文档记录减至最少，提高患者护理质量，并对全球不同医疗环境中的护士和患者产生重大影响。
