Aalborg University, Department of Health and Science, Aalborg, Denmark.
Research Unit for Clinical Nursing, Aalborg University Hospital, Aalborg, Denmark.
Stud Health Technol Inform. 2023 Oct 20;309:23-27. doi: 10.3233/SHTI230732.
Artificial intelligence (AI) can potentially increase the quality of telemonitoring in chronic obstructive pulmonary disease (COPD). However, the output from AI is often difficult for clinicians to understand due to the complexity. This challenge may be accommodated by visualizing the AI results, however it hasn't been studied how this could be done specifically, i.e., considering which visual elements to include.
To investigate how complex results from a predictive algorithm for patients with COPD can be translated into easily understandable data for the clinicians.
Semi-structured interviews were conducted to explore clinicians' needs when visualizing the results of a predictive algorithm. This formed a basis for creating a prototype of an updated user interface. The user interface was evaluated using usability tests through the "Think aloud" method.
The clinicians pointed out the need for visualization of exacerbation alerts and the development in patients' data. Furthermore, they wanted the system to provide more information about what caused exacerbation alerts. Elements such as color and icons were described as particularly useful. The usability of the prototype was primarily assessed as easily understandable and advantageous in connection to the functions of the predictive algorithm.
Predictive algorithm use in telemonitoring of COPD can be optimized by clearly visualizing the algorithm's alerts, clarifying the reasons for algorithm output, and by providing a clear overview of the development in the patient's data. This can contribute to clarity when the clinicians should act and why they should act on alerts from predictive algorithms.
人工智能（AI）有可能提高慢性阻塞性肺疾病（COPD）远程监测的质量。然而，由于 AI 输出的复杂性，临床医生往往难以理解。通过可视化 AI 结果可以解决这一挑战，但尚未研究如何具体实现，即考虑包含哪些视觉元素。
研究如何将 COPD 预测算法的复杂结果转化为临床医生易于理解的数据。
进行半结构化访谈，以探讨临床医生在可视化预测算法结果时的需求。这为创建更新的用户界面原型奠定了基础。通过“出声思考”方法进行可用性测试来评估用户界面。
临床医生指出需要可视化加重警报和患者数据的变化。此外，他们希望系统能提供更多关于导致加重警报的原因的信息。颜色和图标等元素被描述为特别有用。原型的可用性主要被评估为易于理解，并在与预测算法的功能相关时具有优势。
通过清晰地可视化算法的警报、阐明算法输出的原因以及提供患者数据变化的清晰概述，可以优化 COPD 远程监测中预测算法的使用。这有助于临床医生在何时以及为何对预测算法的警报采取行动时更加清楚。
