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Virtualized Disaster Recovery Model for Large Scale Hospital and Healthcare Systems

Virtualized Disaster Recovery Model for Large Scale Hospital and Healthcare Systems

Olivia F. Lee, Dennis C. Guster
Copyright: © 2010 |Volume: 5 |Issue: 3 |Pages: 13
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781609609221|DOI: 10.4018/jhisi.2010070105
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MLA

Lee, Olivia F., and Dennis C. Guster. "Virtualized Disaster Recovery Model for Large Scale Hospital and Healthcare Systems." IJHISI vol.5, no.3 2010: pp.69-81. http://doi.org/10.4018/jhisi.2010070105

APA

Lee, O. F. & Guster, D. C. (2010). Virtualized Disaster Recovery Model for Large Scale Hospital and Healthcare Systems. International Journal of Healthcare Information Systems and Informatics (IJHISI), 5(3), 69-81. http://doi.org/10.4018/jhisi.2010070105

Chicago

Lee, Olivia F., and Dennis C. Guster. "Virtualized Disaster Recovery Model for Large Scale Hospital and Healthcare Systems," International Journal of Healthcare Information Systems and Informatics (IJHISI) 5, no.3: 69-81. http://doi.org/10.4018/jhisi.2010070105

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Abstract

Healthcare organizations face rising costs in effective management of hospital information systems. Adding to this burden is the Joint Commission’s mandate for disaster preparedness, which demands restoring access to information after unexpected catastrophes. Disaster recovery within healthcare organizations is essential because of its inherent critical nature and the possible losses’ impact on patients’ lives. This paper presents a virtualized disaster recovery model and presents steps for setting up the recovery environment and implementing the virtualized plan across multiple network systems. A large scale hospital and healthcare system in Minnesota participated in this study, and results indicate that the virtual model can provide acceptable performance when a limited number of client workstations are functioning. However, its performance is not as good as the traditional physical model, and its workload performance decays much quicker. Future research is suggested that tests more sophisticated models and incorporates finer granularity in the tabular distribution methodology.

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