Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.
Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.
复制标题
让多学科临床用户参与设计人工智能驱动的图形用户界面，以支持重症监护病房不稳定决策。
DOI：
10.1055/s-0043-1775565
复制
发表时间：
2023
影响因子：
2.9
通讯作者：
Clermont,Gilles
中科院分区：
文献类型：
--
作者：
Helman,Stephanie;Terry,MarthaAnn;Pellathy,Tiffany;Hravnak,Marilyn;George,Elisabeth;Al-Zaiti,Salah;Clermont,Gilles
关键不稳定性预测和治疗可以通过人工智能（AI）支持的临床决策支持来优化。重要的是，面向用户的人工智能输出显示有助于临床思维和床边护理涉及的所有学科的工作流程。我们的目标是让多学科用户（医生、执业护士、医师助理）参与图形用户界面（GUI）的开发，以呈现人工智能衍生的风险评分。方法重症监护病房（ICU）临床医生参与焦点小组，寻求对原型GUI中不稳定风险预测的输入。两个分层轮（三个焦点小组[只有护士，只有提供者，然后合并]）由焦点小组方法学家主持。在第1轮之后，GUI设计更改在第2轮进行展示。焦点小组的记录、转录和去识别转录由三位研究人员独立编码。代码被合并成新兴主题。结果共有23名ICU临床医生参与调查，其中护士11人，医务人员12人[3名中层，9名内科医生]。出现了六个主题：(1)分析透明度，(2)图形可解释性，(3)对实践的影响，(4)动态患者数据趋势综合的价值，(5)决策权重（权衡决策过程中的人工智能输出），以及(6)显示位置（可用性，对患者/家属GUI视图的关注）。护士强调有GUI客观信息来支持沟通和最佳GUI定位。而提供者强调建议的可解释性和对损害学员批判性思维的关注。所有学科都重视生命体征、干预措施和风险趋势的综合观点，但在证明值得信赖之前，对人工智能输出的决策权重持怀疑态度。结论在设计人工智能衍生的图形用户界面时，获得所有临床用户的输入是重要的考虑因素。结果强调，卫生保健智能决策支持系统技术的工作方式需要透明，易于阅读和解释，对当前工作流程造成的干扰很小，决策支持组件需要用作人类决策的辅助工具。
BackgroundCritical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care.ObjectivesOur objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score.MethodsIntensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes.ResultsTwenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy.ConclusionGaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.
登录
查看更多内容
给予正常大鼠的抗体-蓖麻毒素 A 链缀合物的命运。
影响因子：
5.8
作者：
N. Worrell;A. Cumber;G. Parnell;W. Ross;J. Forrester
通讯作者：
J. Forrester
抗 Mr 67,000 蛋白免疫毒素对裸鼠模型中人类肿瘤的细胞毒性作用。
影响因子：
11.2
作者：
Weil-Hillman,G;Runge,W;Jansen,FK;Vallera,DA
通讯作者：
Vallera,DA
免疫毒素显示白喉毒素可通过 T3 抗原快速进入，但蓖麻毒素则不然。
DOI：
--
发表时间：
1986
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Youle,RJ;Uckun,FM;Vallera,DA;Colombatti,M
通讯作者：
Colombatti,M
免疫毒素：结合高特异性和强细胞毒性的混合分子
影响因子：
8.7
作者：
Franz K. Jansen;H. Blythman;D. Carrière;Pierre Casellas;Olivier Gros;P. Gros;Jean Claude Laurent;Francis Paolucci;Bernard Pau;P. Poncelet;Gilbert Richer;H. Vidal;Guy Andrá Voisin
通讯作者：
Guy Andrá Voisin
γ-球蛋白分解代谢的理论模型
影响因子：
64.8
作者：
F. W. Brambell;W. A. Hemmings;I. Morris
通讯作者：
I. Morris