Examining the Impact of Interface Design and Nurse Characteristics on Satisfaction With Machine Learning Decision Support Explanations.

IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ann Wieben, Linsey Steege, Roger Brown, Andrea Gilmore-Bykovskyi
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引用次数: 0

Abstract

Machine learning has the potential to drive the next generation of clinical decision support systems by identifying patterns in health data to enhance efficiency and safety. Explanatory information is intended to help clinicians understand the outputs of these complex systems. No studies have evaluated associations between display design strategies or nurse characteristics and nurse satisfaction with machine learning explanatory information. This gap leaves much unknown about designing explanatory displays that meet nurses' information needs, supporting effective use and adoption in practice settings. To address this, we aimed to describe nurses' satisfaction with explanatory information displays for machine learning clinical decision support, examine associations between the format and complexity of explanatory information and nurse satisfaction, and investigate the influence of nurse characteristics, such as numeracy and graphical literacy, on satisfaction. Our findings indicate that local feature-based explanatory information may not satisfy nurses' information needs, and that nurse age, artificial intelligence training level, and numeracy influence preferences. We found no significant effects of the format or complexity of explanatory displays on satisfaction. These insights into the usability of machine learning clinical decision support for nurses can inform the design of more effective displays.

检查界面设计和护士特征对机器学习决策支持解释满意度的影响。
机器学习有潜力通过识别健康数据中的模式来推动下一代临床决策支持系统,以提高效率和安全性。解释性信息旨在帮助临床医生理解这些复杂系统的输出。没有研究评估展示设计策略或护士特征和护士满意度与机器学习解释信息之间的关系。这一差距在设计满足护士信息需求的解释性显示,支持在实践环境中有效使用和采用方面留下了很多未知。为了解决这个问题,我们旨在描述护士对机器学习临床决策支持的解释性信息显示的满意度,检查解释性信息的格式和复杂性与护士满意度之间的关联,并调查护士特征(如计算能力和图形素养)对满意度的影响。我们的研究结果表明,基于局部特征的解释信息可能不能满足护士的信息需求,护士的年龄、人工智能培训水平和计算能力影响偏好。我们发现解释性展示的格式或复杂性对满意度没有显著影响。这些对护士临床决策支持的机器学习可用性的见解可以为更有效的显示设计提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cin-Computers Informatics Nursing
Cin-Computers Informatics Nursing 工程技术-护理
CiteScore
2.00
自引率
15.40%
发文量
248
审稿时长
6-12 weeks
期刊介绍: For over 30 years, CIN: Computers, Informatics, Nursing has been at the interface of the science of information and the art of nursing, publishing articles on the latest developments in nursing informatics, research, education and administrative of health information technology. CIN connects you with colleagues as they share knowledge on implementation of electronic health records systems, design decision-support systems, incorporate evidence-based healthcare in practice, explore point-of-care computing in practice and education, and conceptually integrate nursing languages and standard data sets. Continuing education contact hours are available in every issue.
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