Johnson Lisa G, Madandola Olatunde O, Dos Santos Fabiana Cristina, Priola Karen J B, Yao Yingwei, Macieira Tamara G R, Keenan Gail M
Author Affiliations: College of Nursing, University of Florida College of Nursing, Gainesville, Florida (Ms Johnson, Mr Madandola, Ms Priola, and Drs Yao, Macieira, and Keenan); and School of Nursing, Columbia University, New York, New York (Dr Dos Santos).
J Perinat Neonatal Nurs. 2025;39(1):10-19. doi: 10.1097/JPN.0000000000000831. Epub 2025 Jan 29.
Extensive time spent on documentation in electronic health records (EHRs) impedes patient care and contributes to nurse burnout. Artificial intelligence-based clinical decision support tools within the EHR, such as ChatGPT, can provide care plan recommendations to the perinatal nurse. The lack of explicit methodologies for effectively integrating ChatGPT led to our initiative to build and demonstrate our ChatGPT-4 prompt to support nurse care planning.
We employed our process model, previously tested with 22 diverse medical-surgical patient scenarios, to generate a tailored prompt for ChatGPT-4 to produce care plan suggestions for an exemplar patient presenting with preterm labor and gestational diabetes. A comparative analysis was conducted by evaluating the output against a "nurse-generated care plan" developed by our team of nurses on content alignment, accuracy of standardized nursing terminology, and prioritization of care.
ChatGPT-4 delivered suggestions for nursing diagnoses, interventions, and outcomes comparable to the "nurse-generated care plan." It accurately identified major care areas, avoided irrelevant or unnecessary recommendations, and identified top priority care. Of the 24 labels generated by ChatGPT-4, 16 correctly utilized standardized nursing terminology.
This demonstration of the use of our ChatGPT-4 prompt illustrates the potential of leveraging a large language model to assist perinatal nurses in creating care plans. The next steps are improving the accuracy of ChatGPT-4-generated standardized nursing terminology and integrating our prompt into EHRs. This work supports our broader goal of enhancing patient outcomes while mitigating the burden of documentation that contributes to nurse burnout.
在电子健康记录（EHR）中花费大量时间进行文档记录会妨碍患者护理，并导致护士职业倦怠。EHR 中基于人工智能的临床决策支持工具，如 ChatGPT，可以为围产期护士提供护理计划建议。缺乏有效整合 ChatGPT 的明确方法促使我们开展这项计划，以构建并展示我们的 ChatGPT-4 提示，以支持护士护理计划制定。
我们采用了之前在 22 种不同的内科 - 外科患者场景中进行测试的流程模型，为 ChatGPT-4 生成一个定制提示，以便为一名出现早产和妊娠期糖尿病的典型患者生成护理计划建议。通过将输出结果与我们护士团队制定的“护士生成的护理计划”在内容一致性、标准化护理术语的准确性以及护理优先级方面进行评估，进行了对比分析。
ChatGPT-4 给出的护理诊断、干预措施和结果建议与“护士生成的护理计划”相当。它准确识别了主要护理领域，避免了不相关或不必要的建议，并确定了首要护理重点。ChatGPT-4 生成的 24 个标签中，有 16 个正确使用了标准化护理术语。
我们对 ChatGPT-4 提示的使用演示表明，利用大语言模型协助围产期护士制定护理计划具有潜力。下一步是提高 ChatGPT-4 生成的标准化护理术语的准确性，并将我们的提示集成到 EHR 中。这项工作支持了我们更广泛的目标，即改善患者预后，同时减轻导致护士职业倦怠的文档记录负担。
