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Proceedings Paper

Methodology for design of adaptive interfaces for diagnostic workstations with integrated images and reports
Author(s): Michael R. Harreld; Daniel J. Valentino; Brent J. Liu; Suzie El-Saden; Gary R. Duckwiler M.D.
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Paper Abstract

Diagnostic workstations have generally lacked acceptance due to awkward interfaces, poor usability and lack of clinical data integration. We developed a new methodology for the design and implementation of diagnostic workstations and applied the methodology in diagnostic neuroradiology. The methodology facilitated the objective design and evaluation of optimal diagnostic features, including the integration of images and reports, and the implementation of intelligent and adaptive graphical user interfaces. As a test of this new methodology, we developed and evaluated a neuroradiological diagnostic workstation. The general goals of diagnostic neuroradiologists were modeled and directly used in the design of the UCLA Digital ViewBox, an object-oriented toolkit for medical imaging workstations. For case-specific goals, an object-oriented protocol toolkit was developed for rapid development and integration of new protocols, modes, and tools. Each protocol defines a way to arrange and process data in order to accomplish diagnostic goals that are specific to anatomy (e.g., a spine protocol), or to a suspected pathology (e.g., a tumor protocol). Each protocol was divided into modes that represent diagnostic reading tasks. Each mode was further broken down into functions supporting that task. Via a data mediator engine, the workstation communicated with clinical data repositories, including the UCLA HIS, Clinical RIS/PACS and individual DICOM compatible scanners. The data mediator served to transparently integrate, retrieve, and cache image and report data. Task-oriented Reading protocols automatically present the appropriate diagnostic information and diagnostic tools to the radiologist. We describe a protocol toolkit that enables the rapid design and implementation of customized reading protocols. We also present an intelligent layer that enables the automatic presentation of the appropriate information. This new methodology for diagnostic workstation design led to an improved neuroradiology workstation. Future research will focus on developing protocols for other diagnostic specialties.

Paper Details

Date Published: 26 June 1998
PDF: 11 pages
Proc. SPIE 3335, Medical Imaging 1998: Image Display, (26 June 1998); doi: 10.1117/12.312507
Show Author Affiliations
Michael R. Harreld, UCLA School of Medicine (United States)
Daniel J. Valentino, UCLA School of Medicine (United States)
Brent J. Liu, UCLA School of Medicine (United States)
Suzie El-Saden, UCLA School of Medicine (United States)
Gary R. Duckwiler M.D., UCLA School of Medicine (United States)

Published in SPIE Proceedings Vol. 3335:
Medical Imaging 1998: Image Display
Yongmin Kim; Seong Ki Mun, Editor(s)

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