Data Visualization for Decision Making in HIV Programs

Join MEASURE Evaluation April 27 at 11am EDT for a webinar on data visualization for decision making in HIV programs.

Data Visualization - April 2016

Join MEASURE Evaluation April 27 at 11am EDT for a webinar on data visualization for decision making in HIV programs. The webinar will be led by Tara Nutley, MEASURE Evaluation; Stacey Berlow, Project Balance; and Isabel Brodsky, MEASURE Evaluation. Registration is now open.

With the introduction and strengthening of electronic health information systems, health data have become increasingly available in digital formats. Access to data, however, is not sufficient. Data visualization can help would-be data users to see patterns, trends, and correlations that might otherwise go undetected. Multiple software platforms, either open-source or proprietary, are available to facilitate data visualization. These software applications, many of which are interactive, provide tools to develop charts, maps, infographics, timelines, and other visual tools. With the increase in data visualization resources, it’s important to investigate how they are being used, challenges and facilitators to use, and their impact on program decision making.

This webinar will discuss an investigation by MEASURE Evaluation into how HIV programs are using data visualization techniques and software to improve decision making in HIV programs. Examples from two data visualization experiences will be presented: the use of maps in South Africa improve the location of HIV services offered in relation to the areas of highest HIV prevalence and the use of dashboards in Zambia to improve prevention of mother-to-child transmission (PMTCT) services. The webinar will explore challenges, opportunities, lessons learned, and considerations for using data visualization to improve data-based decision making.

Register to attend the webinar.

When Apr 27, 2016
from 11:00 AM to 12:00 PM
Where Webinar
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Filed under: HIV , Health Information Systems , HIV/AIDS , EHealth , Data , Data visualization
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