The Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, United States.
The Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States.
Int J Med Inform. 2022 Mar;159:104643. doi: 10.1016/j.ijmedinf.2021.104643. Epub 2021 Nov 11.
Artificial Intelligence (AI) is increasingly used to support bedside clinical decisions, but information must be presented in usable ways within workflow. Graphical User Interfaces (GUI) are front-facing presentations for communicating AI outputs, but clinicians are not routinely invited to participate in their design, hindering AI solution potential.
To inform early user-engaged design of a GUI prototype aimed at predicting future Cardiorespiratory Insufficiency (CRI) by exploring clinician methods for identifying at-risk patients, previous experience with implementing new technologies into clinical workflow, and user perspectives on GUI screen changes.
We conducted a qualitative focus group study to elicit iterative design feedback from clinical end-users on an early GUI prototype display. Five online focus group sessions were held, each moderated by an expert focus group methodologist. Iterative design changes were made sequentially, and the updated GUI display was presented to the next group of participants.
23 clinicians were recruited (14 nurses, 4 nurse practitioners, 5 physicians; median participant age ∼35 years; 60% female; median clinical experience 8 years). Five themes emerged from thematic content analysis: trend evolution, context (risk evolution relative to vital signs and interventions), evaluation/interpretation/explanation (sub theme: continuity of evaluation), clinician intuition, and clinical operations. Based on these themes, GUI display changes were made. For example, color and scale adjustments, integration of clinical information, and threshold personalization.
Early user-engaged design was useful in adjusting GUI presentation of AI output. Next steps involve clinical testing and further design modification of the AI output to optimally facilitate clinician surveillance and decisions. Clinicians should be involved early and often in clinical decision support design to optimize efficacy of AI tools.
人工智能（AI）越来越多地用于支持床边临床决策，但信息必须在工作流程中以可用的方式呈现。图形用户界面（GUI）是用于传达 AI 输出的前端展示，但临床医生通常不会被邀请参与其设计，这阻碍了 AI 解决方案的潜力。
通过探索临床医生识别高危患者的方法、将新技术纳入临床工作流程的先前经验以及用户对 GUI 屏幕变化的看法，为早期用户参与设计旨在预测未来心肺功能不全（CRI）的 GUI 原型提供信息。
我们进行了一项定性焦点小组研究，以从临床最终用户那里获得对早期 GUI 原型显示的迭代设计反馈。共进行了五次在线焦点小组会议，每次均由一名专家焦点小组方法学家主持。顺序进行迭代设计更改，并将更新的 GUI 显示呈现给下一组参与者。
共招募了 23 名临床医生（14 名护士、4 名护士从业者、5 名医生；中位数参与者年龄约为 35 岁；60%为女性；中位数临床经验为 8 年）。从主题内容分析中出现了五个主题：趋势演变、背景（相对于生命体征和干预措施的风险演变）、评估/解释/解释（子主题：评估的连续性）、临床医生直觉和临床操作。基于这些主题，对 GUI 显示进行了更改。例如，颜色和比例调整、临床信息的集成以及阈值个性化。
早期的用户参与设计对于调整 AI 输出的 GUI 呈现非常有用。下一步是对 AI 输出进行临床测试和进一步的设计修改，以最佳地促进临床医生的监测和决策。临床医生应尽早并经常参与临床决策支持设计，以优化 AI 工具的效果。
