Web-based Application for Interactive Visualization and Analysis of Diabetes Data from an EMR

Author: Ritu Srivastava, MS

Primary Advisor: Noriaki Aoki, MD (co-author)

Committee Members: Jiajie Zhang, PhD (co-author)

Masters thesis, The University of Texas Health Science Center School of Health Information Sciences at Houston.



The paper describes the method to develop a web-based application for interactive visualization and analysis of diabetes data from an outpatient EMR implemented at a family practice associated with a major medical school in a large metropolitan area. It addresses the specific objectives to: (1) Identify quality indicators for diabetes; (2) Extract data from the EMR system; (3) Compute quality indicators and; (4) Develop a web-based application that enables interactive visualization and analysis.

Research Design and Methods:

The quality indicators for diabetes were identified by a literature search. Measures for the quality indicators were mapped to data fields in the Electronic Medical Record (EMR) database and data of patients with type 2 diabetes was imported into a backend database. The quality indicators were computed using database queries. The results of the analyses were displayed as graph images through web pages, developed using a scripting language, with dynamic data access.


Although 9 quality indicators were identified, only 7 could be mapped to data fields. Data was extracted in a Comma Separated Value (CSV) format and imported into a SQL Server database. Indicators were computed from the data using intuitive Structures Query language (SQL) queries. Graph images were created using Popchart. The web pages were developed using Active Sever Pages (ASP), which displayed the graph images by dynamically accessing the backend database.


 The web-based application technology provided distributed access to the users and enabled tracking and monitoring of quality indicators and will eventually enable benchmarking, quality development and accountability within the organization. This application can be scalable and extended to support other chronic diseases like obesity, hypertension and hypercholesterolemia.