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Proceedings Paper

Multi-disciplinary data organization and visualization models for clinical and pre-clinical studies: A case study in the application of proton beam radiosurgery for treating spinal cord injury related pain
Author(s): Sneha K. Verma; Brent J. Liu
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Paper Abstract

An increasing adoption of electronic medical records has made information more accessible to clinicians and researchers through dedicated systems such as HIS, RIS and PACS. The speed and the amount at which information are generated in a multi-institutional clinical study make the problem complicated compared to day-to-day hospital workflow. Often, increased access to the information does not translate into the efficient use of that information. Therefore, it becomes crucial to establish models which can be used to organize and visualize multi-disciplinary data. Good visualization in turn makes it easy for clinical decision-makers to reach a conclusion within a small span of time. In a clinical study involving multi-disciplinary data and multiple user groups who need access to the same data and presentation states based on the stage of the clinical trial or the task are crucial within the workflow. Therefore, in order to demonstrate the conceptual system design and system workflow, we will be presenting a clinical trial based on application of proton beam for radiosurgery which will utilize our proposed system. For demonstrating user role and visualization design purposes, we will be focusing on three different user groups which are researchers involved in patient enrollment and recruitment, clinicians involved in treatment and imaging review and lastly the principle investigators involved in monitoring progress of clinical study. Also datasets for each phase of the clinical study including preclinical and clinical data as it related to subject enrollment, subject recruitment (classifier), treatment (DICOM), imaging, and pathological analysis (protein staining) of outcomes.

Paper Details

Date Published: 25 March 2016
PDF: 8 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890U (25 March 2016); doi: 10.1117/12.2216367
Show Author Affiliations
Sneha K. Verma, The Univ. of Southern California (United States)
Brent J. Liu, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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