Keim-Malpass Jessica, Moorman Liza P
School of Nursing, Department of Acute and Specialty Care, University of Virginia, Charlottesville, VA, USA.
Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, USA.
Int J Nurs Stud Adv. 2021 Nov;3:100019. doi: 10.1016/j.ijnsa.2021.100019. Epub 2021 Jan 5.
As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.
随着全球对新冠疫情的应对持续进行，护士将承担对患者进行合理分诊、应对临床病情恶化情况以及在医疗系统不堪重负的情况下制定以家庭为中心的护理计划等任务。预测分析监测是一种基于人工智能的工具，可将流式临床数据转化为对患者风险的实时可视化评估，能在患者处于症状前状态时进行动态的 acuity 评估并检测临床病情恶化情况。在新冠疫情中护士处于前线，基于人工智能的预测分析监测的使用可能有助于处理认知复杂的临床决策任务，并为早期发现有失代偿风险的患者铺平道路。我们必须制定策略和技术来研究基于人工智能的技术对患者护理结果和临床工作流程的影响。本文概述了护理与精准预测分析监测交叉领域的关键概念。 （注：原文中“acuity”此处可能有误，结合语境推测可能是“病情严重程度”之类含义，因未明确准确信息，暂按原文翻译）
