This Broadening Participation Research Initiation Grants in Engineering (BRIGE) grant provides funding to design and evaluate visualizations that will allow health care decision makers, including policy makers and hospital administrators, to understand how health care workers and patients complete health care processes. Decision makers will be able to use the visualizations to make better choices about how to improve the health care processes, thereby increasing process efficiency and reducing process-related medical errors. The researcher will design visualizations using existing process data from two studies. In the first study, researchers used eye-tracking technology to document how health care workers verified a patient's identity, with and without barcoding technology. In the second study, researchers observed the surgery process, wherein some surgeons were sleep deprived. The researcher will evaluate decision makers' attention allocation, interpretation of information, and use of information when making process improvement choices based on 1) summary statistics and charts, 2) complex numerical information, and 3) varying forms of the visualizations.
If successful, the results of this research will improve health care decision makers' abilities to redesign health care processes. By fine-tuning the design of the process visualizations, they will be able to analyze and improve more health care processes in less time. By empirically evaluating how they use the visualizations, the research will ensure that the visualizations are easy to understand and useful to the decision makers. This approach is scalable and lends itself to visualizing processes in non-health care domains.
This Broadening Participation Research Initiation Grants in Engineering (BRIGE) grant provided funding to design and evaluate visualizations that allow healthcare decision makers, including policy makers and hospital administrators, to understand how healthcare workers complete complex healthcare processes. Individuals can use the visualizations to make better choices about how to improve healthcare processes, thereby increasing process efficiency and reducing process-related medical errors. By fine-tuning the design of the process visualizations, individuals will be able to analyze and improve more healthcare processes in less time. This approach is scalable and lends itself to visualizing processes in non-healthcare domains. The researchers designed the visualizations using existing process data from a study in which researchers used eye-tracking technology to document how healthcare workers verified a patient's identity, with and without barcoding technology. The eye-tracking videos showed what each healthcare worker looked at, said, and did throughout the process. The researchers first developed a method for coding these videos based on standardized step names specific to the patient identification process. The researchers then developed a system that could take the coded data and generate three types of visualizations: Markov Chains to show transitions between different types of steps, sparklines to show frequencies of transitioning between steps, and timeline belts to show the orders in which individuals completed process steps. Once the visualization system was developed, the researchers created a toolkit that allows other individuals to input their process data and choose portions of their data to analyze. The process data can come from any domain and can be captured using a variety of methods (e.g., observations, videos, etc.) The toolkit computes a set of statistics related to the process data, and generates files that can serve as inputs to the visualization system.
