Description

The purpose of this paper is the analysis of EHR clickstream data of patient portal to determine patient usage behavior. We present our analysis of patterns found in patient clickstream data. Using directed and undirected data mining approach, data can be explored to examine whether different patient groups appear to use the portal differently. We examine changes in usage over time, and also explore difference in usage, average number of clicks per session and time spent per page based on age and gender. We then use clustering to create groups that discriminate patients by their portal usage behavior. Knowledge of these usage patterns can help service providers understand the demographics and behavioral aspects of their patients, which in turn can help them develop, enhance and improve their systems to make the best use of these portals.

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Analysis & Visualization of EHR Patient Portal Clickstream Data

The purpose of this paper is the analysis of EHR clickstream data of patient portal to determine patient usage behavior. We present our analysis of patterns found in patient clickstream data. Using directed and undirected data mining approach, data can be explored to examine whether different patient groups appear to use the portal differently. We examine changes in usage over time, and also explore difference in usage, average number of clicks per session and time spent per page based on age and gender. We then use clustering to create groups that discriminate patients by their portal usage behavior. Knowledge of these usage patterns can help service providers understand the demographics and behavioral aspects of their patients, which in turn can help them develop, enhance and improve their systems to make the best use of these portals.