Assessment of Data Visualizations for Clinical Decision Support
Ledesma, Andres (2020)
Biolääketieteen tekniikan tohtoriohjelma - Doctoral Programme in Biomedical Sciences and Engineering
Lääketieteen ja terveysteknologian tiedekunta - Faculty of Medicine and Health Technology
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Since the wide adoption of electronic health records, the amount of clinical data has increased dramatically. It has been estimated that in 2013 there was a total of 153 exabytes of clinical data, and by 2020 this number will increase to 2 314 worldwide. Another estimate has calculated that the average patient generates 80 megabytes of data per year. Clinicians rely on clinical data to make informed decisions at the point of care. However, the volume and complexity of clinical data along with time constraints, make the diagnosis process challenging, time-consuming and prone to errors. It has been estimated that the number of deaths due to clinical misdiagnosed are between 44 000 and 98 000 per year in the United States. By using computerized data visualization techniques, clinicians can extract valuable insights, reducing cognitive overload. Consistent and structured methodologies that assess clinical data visualizations and their effect on the decision making process, are still missing. The gap that the thesis aims to bridge is to develop a methodology that allows the assessment of clinical data visualizations in terms of their efficacy in supporting clinical decision making. The purpose of this thesis is to develop such methodology, which studies the reasoning derived from the visualization and how this affects the clinical decision making process at an individual level. The first experiment compared five different visualization techniques. The study measured the quantity and quality of insights obtained by the users of the visualizations. This assessment technique has not been used before in the context of clinical data. By evaluating the visualizations in this way, it was objectively determined that from the visualization techniques used in the study, the radar plots were the most effective in enabling the generation of hypotheses and in acquiring accurate understanding of the data. The second experiment studied a dashboard representing the evolution of health and wellness of a modelled patient. The dashboard included an improved version of the radar plots used in the previous study. By using methods such as heuristics, cognitive walk-through, analytic tasks, and usability questionnaires, it was objectively determined that the dashboard was effective in assisting users to find critical information and gain accurate understanding of the clinical data. The study was able to quantify and demonstrate the degree to which the dashboard proved useful for its intended audience by scoring an average of 6.02 out of 7 points in the usability studies and a completion rate of analytical tasks of 96 percent. The third and last experiment compared an existing tabular interface for clinical data against an interactive visualized timeline. The methodology used in this study was the same as in the first experiment. However, the chronological and longitudinal nature of the clinical data required adaptations to the methodology. The use of this methodology to evaluate longitudinal data visualizations has not been reported in previous studies. By applying this novel approach, it was objectively determined that the timeline enabled clinicians to deduce the underlying conditions of the patients, reflecting a deep understanding of the data by connecting information scattered over a period of time. These three assessments followed state-of-the-art methodologies in the discipline of data visualization that have not been used before for the purpose of clinical decision making. The objectives of the thesis were met by applying novel assessment techniques. By applying quantitative and qualitative research, it was possible to compare visualizations in a clinical context and provided better understanding on what makes a good visualization. The publications in this dissertation document experiments conducted to study the reasoning derived from the visualization and how this affects the clinical decision making process at an individual level. By utilizing consistent methodologies such as the insight-based, usability testing and cognitive walkthrough, different visualizations were objectively compared and assessed. These documented experiments can serve as blueprints for future studies. With a deeper understanding on the impact of visualization tools in the clinical decision making process, researchers can develop better visualizations to ease the cognitive burden of making sense of complex data. With better visualizations, clinicians can gain deeper understanding of the data, making better decisions, resulting in better patient outcome.
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