Yang Yushi, Bass Ellen J, Sockolow Paulina S, Bowles Kathryn H
Drexel University, College of Computing and Informatics.
Drexel University, College of Nursing and Health Professions, Philadelphia PA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1127-1136. eCollection 2018.
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. However, in healthcare, many subject matter experts have limited time for such endeavors. In addition, researchers need to analyze voluminous amounts of qualitative data. Thus, we present a data collection and validation methodology: an initial focus group followed by targeted member checking, both supported by data visualization. We ground the work in a homecare admission case study. We conducted a focus group with six homecare admitting nurses at a rural agency. Our custom visualizations of the qualitative results helped to identify potential missing information. We conducted a member checking session with five nurses to validate the focus group results and to address the missing data. The member checking results were incorporated into the custom visualizations. The data collection and validation methodology shows promise for knowledge elicitation in time-constrained situations.
研究人员获取与专家决策过程相关的知识，以为信息技术设计及相关干预措施提供信息。然而，在医疗保健领域，许多主题专家从事此类工作的时间有限。此外，研究人员需要分析大量定性数据。因此，我们提出一种数据收集和验证方法：首先进行焦点小组讨论，随后进行有针对性的成员核对，两者均由数据可视化提供支持。我们以一个家庭护理入院案例研究为基础开展这项工作。我们与一家农村机构的六名家庭护理入院护士进行了焦点小组讨论。我们对定性结果的定制可视化有助于识别潜在的缺失信息。我们与五名护士进行了成员核对会议，以验证焦点小组的结果并处理缺失数据。成员核对结果被纳入定制可视化中。这种数据收集和验证方法在时间受限的情况下进行知识获取方面显示出了前景。
