Application of Visualization-based Analytic Methods in Population Health and Health Services Research to Rehabilitation Sciences

Visualization-based analytic methods have evolved rapidly over the last two decades, allowing knowledge generation from large healthcare datasets. However, their application in the rehabilitation sciences, particularly related to population health and health services research is limited. This research is divided into two main parts. The first presents two systematic literature syntheses of two information visualization methods, visual analytics and interactive visualization, both applied in these areas of healthcare. The second part presents two use cases illustrating the application of these methods. The first use case is an interactive visualization dashboard prototype for the Canadian Institute for Health Information's Population Grouping Methodology, where the candidate was placed as an embedded fellow. The second use case presents a proof-of-principle dashboard exploring data on rehospitalizations from a multicenter practice-based evidence study on outcomes of persons with spinal cord injury. Rehabilitation health services' research can benefit from the use of visualization-based methods alone or combined with exploratory visual and confirmatory statistical analysis to advance the field. In addition, this thesis highlights the opportunity for organizations to leverage embedded research for advancing and improving healthcare through integrated knowledge translation initiatives and building Learning Health Systems inclusive of rehabilitation services.

Department dc:contributor.department

Rehabilitation Science

Year dc:date.issued

2022

Author dc:creator

• Chishtie, Jawad Ahmed

Advisor dc:contributor.advisor

• Jaglal, Susan B.

• data visualization
• health services research
• population health
• rehabilitation
• visual analytics

Statement dc:rights

• Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Handle dc:identifier.uri

http://hdl.handle.net/1807/125167

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/125167