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In a clinical setting, Tableau dashboards can be used to visualize the patient experience, compare various data, improve care quality, and decrease costs. Tableau dashboards are designed to make it as easy as possible for users to work with relevant data. Different options of pre-sorting and the opportunity to change the types of diagrams make working with Tableau convenient and intuitive (Franklin et al., 2017). Most importantly, clinicians may use an interactive nature of the mentioned software additional information appears on a graph or diagram of interest, which allows avoiding the overload of the original diagram to observe only necessary points. For example, based on the analysis of diabetes indicators from a set of hospitals in one city in combination with lifestyles and nutrition patterns, it is possible to reveal trends associated with this disease and calibrate the corresponding intervention. The analysis by age or ethnicity groups may also be applied to understand the most vulnerable populations and assist them.
In my current clinical area, I would like to use dashboards to understand what should be improved and track the progress of a project that will be implemented based on this analysis. Since the Hispanic population that suffers from diabetes and obesity composes my target population, it is critical to integrate information regarding their disease onset, symptoms, aggravating factors, any changes, and treatment options. Reviewing the mentioned issues through a dashboard is likely to show weak points and inappropriate interventions. After that, I will be able to adjust interventions and reevaluate patient outcomes and experiences in both short- and long-term periods. Thus, the value of dashboards lies in visualizing different sets of data to make it more transparent, optimize existing strengths, and address needs.
Reference
Franklin, A., Gantela, S., Shifarraw, S., Johnson, T. R., Robinson, D. J., King, B. R.,& Rubio, A. (2017). Dashboard visualizations: Supporting real-time throughput decision-making. Journal of Biomedical Informatics, 71, 211-221.
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