Collaborative Visual Analytics for public health: facilitating problem solving and supporting decision-making

Author: 
Date created: 
2014-04-23
Identifier: 
etd8264
Keywords: 
Collaborative Visual Analytics
Public Health
Paired Analytics
Group Analytics
Delphi Method
Interactive Dashboard
Abstract: 

With advancement in information technology, health data are collected at an unprecedented rate. Accurate understanding, analysis and interpretation of complex, multidimensional data is critical to understand wicked health problems to make timely decisions and interventions. Injury problems as classified as wicked health problems, they are associated with numerous individual, social, environmental and policy related factors. Wicked injury problems are multidimensional and require a multidisciplinary approach for effective solutions. We studied the integration of Visual Analytics (VA) methods to solve wicked injury problems. The science of VA leverages information visualization techniques and computational analysis methods to facilitate understanding of heterogeneous data and support decisions about dynamic injury situations. We designed a proof-of-concept prototype - interactive Analytical Injury Dashboard (iAID) and demonstrated its application with injury stakeholders, using Canadian CHIRPP injury data. We adopted the Paired Analytics (PA) methodology to assess the interface design, layout and functionality of the iAID. Inspired by the Delphi method, the study adapted (PA) methodology and introduced a novel methodology - Group Analytics (GA), which was pilot tested and refined for the final research study design. GA was used to evaluate the impact of collaborative VA on facilitating problem solving and supporting decision-making within the injury sector. We conducted seven PA sessions and two GA sessions. Data included stakeholders observations, audio and video recordings, questionnaires and follow up interviews, and were analyzed to gain in-depth understanding of the collaborative VA process and its impact on problem solving and decision-making. Results demonstrated that iAID helped injury stakeholders to convert data into useful information, facilitate task completion, and support problem solving and decision-making. Based on the Joint Activity Theory and distributed cognition framework, analysis revealed that GA triggered the emergence of Common Ground among stakeholders, which evolved throughout the GA sessions to enhance their interactions, communication, coordination of joint activities and ultimately their collaboration on problem solving and decision-making. These findings will help inform the design of innovative VA tools that assist health professionals in analyzing and interpreting complex health data, and will introduce new metrics to enhance group collaboration to support timely decisions and actions.

Document type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed and for the text to be copied and pasted.
File(s): 
Supervisor(s): 
Dr. Brian Fisher
Dr. Ian Pike
Department: 
Communication, Art & Technology: School of Interactive Arts and Technology
Thesis type: 
(Dissertation) Ph.D.
Statistics: