Development of an Innovative Tool to Appraise Big Data for Best Evidence

Background: Datum from electronic sources has accumulated and resulted in the establishment of big data and data science. Big data consists of data sets that are larger than traditional data processing applications can manage. Data science is the research method used to analyze big data. Researchers are applying research methods to harness large and complex data sets to increase our understanding of population health by creating predictive models of patients using a variety of key variables or characteristics. Evidence-based practice relies on the appraisal of research to ensure rigor prior to implementation in clinical settings. Consistent with other research methods, papers based on data science should be subject to appraisal for determination of best evidence. The purpose of this paper is to present a tool that can be used to appraise research papers based on large data sets and data science research methods. Methods: The following approach was used to develop the Data Science Appraisal Tool (DSAT). Despite an exhaustive search, we were unable to locate an appraisal tool for papers based on data science research methods. We then synthesized the extant literature to form the tool. The tool is based on the common characteristics of big data: (a) verification that the data set is representative of big data; (b) preparation of the data for analysis; (c) methodology used for data analysis; (d) results; and (e) theoretically based. Linking Evidence to Action: Appraisal tools currently exist for traditional and well-known research methods. The DSAT provides a method to appraise papers based in data science for best evidence.