A new approach to investigate serendipitous knowledge discovery (SKD) of health information is developed and tested to evaluate the information flow-serendipitous knowledge discovery (IF-SKD) model. The purpose of this paper is to determine the degree to which IF-SKD reflects physicians’ information behaviour in a clinical setting and explore how the information system, Spark, designed to support physicians’ SKD, meets its goals.,The proposed pre-experimental study design employs an adapted version of the McCay-Peet’s (2013) and McCay-Peet et al.’s (2015) serendipitous digital environment (SDE) questionnaire research tool to address the complexity associated with defining the way in which SKD is understood and applied in system design. To test the IF-SKD model, the new data analysis approach combining confirmatory factor analysis, data imputation and Monte Carlo simulations was developed.,The piloting of the proposed novel analysis approach demonstrated that small sample information behaviour survey data can be meaningfully examined using a confirmatory factor analysis technique.,This method allows to improve the reliability in measuring SKD and the generalisability of findings.,This paper makes an original contribution to developing and refining methods and tools of research into information-system-supported serendipitous discovery of information by health providers.

中文翻译：

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在线发现系统中评估医师的偶然知识发现

开发并测试了一种新的调查健康信息的偶然知识发现（SKD）的方法，以评估信息流-偶然知识发现（IF-SKD）模型。本文的目的是确定IF-SKD在临床环境中反映医师信息行为的程度，并探讨旨在支持医师SKD的信息系统Spark如何实现其目标。研究设计采用了McCay-Peet（2013）和McCay-Peet等人（2015）偶然数字环境（SDE）问卷研究工具的改编版本，以解决与定义SKD理解和定义方式相关的复杂性在系统设计中的应用。为了测试IF-SKD模型，新的数据分析方法结合了验证性因子分析，