基于机器学习的喘管理工具的临床需求评估:以用户为中心的设计方法
Lu Zheng1, Joshua W Ohde1, Shauna M Overgaard1
1Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
JMIR formative research
|January 15, 2024
概括
临床医生需要以用户为中心的机器学习 (ML) 工具设计,以改善儿科喘管理. 解决可用性,可解释性和实施障碍是有效的临床决策支持系统的关键.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 用户体验研究 用户体验研究
背景情况:
- 个性化喘管理受到碎片化的电子健康记录数据的阻碍.
- 机器学习 (ML) 工具提供了潜在的解决方案,但面临实施障碍.
研究的目的:
- 探索临床医生与当前喘管理系统的经验.
- 确定基于ML的临床决策支持工具的用户要求.
- 确定在喘治疗中实施ML的障碍.
主要方法:
- 采用以用户为中心的设计方法 (双钻框架).
- 对14名治疗儿科喘的临床医生进行了半结构化采访.
- 利用影像和合成分析来为研究问题提供信息.
主要成果:
- 确定了关键主题:可用性,工作流程,算法可解释性和实施障碍.
- 临床医生需要统一的信息观点和积极的风险识别.
- 算法透明度,有效性文档和适当的培训对于采用算法至关重要.
结论:
- 以用户为中心,以信息学为基础的方法对于在临床实践中开发有效的ML工具至关重要.
- 在设计过程中很早就让临床医生参与,可以确保技术满足实际需求.
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