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Proceedings Paper

DICOM index tracker enterprise: advanced system for enterprise-wide quality assurance and patient safety monitoring
Author(s): Min Zhang; William Pavlicek; Anshuman Panda; Steve G. Langer; Richard Morin; Kenneth A. Fetterly; Robert Paden; James Hanson; Lin-Wei Wu; Teresa Wu
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Paper Abstract

DICOM Index Tracker (DIT) is an integrated platform to harvest rich information available from Digital Imaging and Communications in Medicine (DICOM) to improve quality assurance in radiology practices. It is designed to capture and maintain longitudinal patient-specific exam indices of interests for all diagnostic and procedural uses of imaging modalities. Thus, it effectively serves as a quality assurance and patient safety monitoring tool. The foundation of DIT is an intelligent database system which stores the information accepted and parsed via a DICOM receiver and parser. The database system enables the basic dosimetry analysis. The success of DIT implementation at Mayo Clinic Arizona calls for the DIT deployment at the enterprise level which requires significant improvements. First, for geographically distributed multi-site implementation, the first bottleneck is the communication (network) delay; the second is the scalability of the DICOM parser to handle the large volume of exams from different sites. To address this issue, DICOM receiver and parser are separated and decentralized by site. To facilitate the enterprise wide Quality Assurance (QA), a notable challenge is the great diversities of manufacturers, modalities and software versions, as the solution DIT Enterprise provides the standardization tool for device naming, protocol naming, physician naming across sites. Thirdly, advanced analytic engines are implemented online which support the proactive QA in DIT Enterprise.

Paper Details

Date Published: 17 March 2015
PDF: 9 pages
Proc. SPIE 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, 94180L (17 March 2015); doi: 10.1117/12.2082120
Show Author Affiliations
Min Zhang, Mayo Clinic Arizona (United States)
Arizona State Univ. (United States)
William Pavlicek, Mayo Clinic Arizona (United States)
Anshuman Panda, Mayo Clinic Arizona (United States)
Steve G. Langer, Mayo Clinic Rochester (United States)
Richard Morin, Mayo Clinic Florida (United States)
Kenneth A. Fetterly, Mayo Clinic Rochester (United States)
Robert Paden, Mayo Clinic Arizona (United States)
James Hanson, Mayo Clinic Arizona (United States)
Lin-Wei Wu, Mayo Clinic Arizona (United States)
Teresa Wu, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 9418:
Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations
Tessa S. Cook; Jianguo Zhang, Editor(s)

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