Analysis & Visualization of EHR Patient Portal Clickstream Data

The purpose of this paper is the analysis of EHR clickstream data for patient portal to determine patient usage behavior by analyzing patterns found in the data. Clickstream data retains vital information trail of patient’s usage. Utilizing directed and undirected data mining techniques for data exploration and then visualizing specific patterns provide valuable information about how a patient utilizes these services. The information can help service providers to understand the demographics and behavioral aspects of the patients to develop, enhance and improve their systems to make the best use for these portals.

[1]  D. Lansky,et al.  The missing link: bridging the patient-provider health information gap. , 2005, Health affairs.

[2]  Su Myeon Kim,et al.  Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites , 2005, Expert Syst. Appl..

[3]  Lior Rokach,et al.  Clustering Methods , 2005, The Data Mining and Knowledge Discovery Handbook.

[4]  Wendy W. Moe,et al.  Capturing evolving visit behavior in clickstream data , 2004 .

[5]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[6]  F Johnson,et al.  Personal health record , 1988, Medical Journal of Australia.

[7]  Zeng Chun,et al.  A Survey of Personalization Technology , 2002 .

[8]  Padhraic Smyth,et al.  Visualization of navigation patterns on a Web site using model-based clustering , 2000, KDD '00.

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Adam Bosworth,et al.  What it takes: characteristics of the ideal personal health record. , 2009, Health affairs.

[11]  Kannan Srinivasan,et al.  Modeling Online Browsing and Path Analysis Using Clickstream Data , 2004 .

[12]  Wendy W. Moe,et al.  The Influence of Goal‐Directed and Experiential Activities on Online Flow Experiences , 2003 .

[13]  Zhongzhi Shi,et al.  Techniques, Process, and Enterprise Solutions of Business Intelligence , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[14]  Bengisu Tulu,et al.  Personal Health Records: Identifying Utilization Patterns from System Use Logs and Patient Interviews , 2012, 2012 45th Hawaii International Conference on System Sciences.

[15]  Yu Li,et al.  Research on personalized recommendation algorithm for user's multiple interests , 2004 .

[16]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[17]  Jing Hua Zhang.  Impacts of US health care reform on IT firms' revenue: The case of EHR Meaningful Use criteria , 2013, 2013 International Conference on Engineering, Management Science and Innovation (ICEMSI).

[18]  Richard Ben Cramer.  WHAT IT TAKES. , 1992 .