Date of Award

Fall 8-1-2013

Document Type


Degree Name

Doctor of Science in Information Systems


Business and Information Systems

First Advisor

Surendra Sarnikar

Second Advisor

Omar El-Gayar

Third Advisor

Kari Forbes-Boyte


Information Technology in healthcare is an ever-growing enterprise, with medical providers becoming more and more reliant on data to make care decisions. Medical institutions today are faced with increasing costs and declining reimbursement for services provided. This squeeze in revenue is placing additional emphasis on ensuring that IT projects in this space are architected correctly, to ensure the best results at the lowest cost. Similarly, the increasing scale of applications and data to be supported has created an imbalance in the number of servers vs. the IT administrators. Further, with the increased reliance on these applications for care, questions arise around the availability of systems, as they have become critical to providing quality care in some cases. To date, no modeling method has been available which would assist in the selection of computing infrastructure architectures. In this dissertation, a model is developed for the selection of computing infrastructure architectures in healthcare organizations. The model provides insight to both practitioners and academics by extending our understanding of the decisions regarding computing architectures within the healthcare system. The model described here utilizes the Analytics Hierarchy Process (AHP) to weigh the various criteria that come into play for decisions of this nature. Unique criteria are noted, including the requirement of considering patient criticality as one criterion of the decision process. A survey of IT professionals was undertaken in order to assess the validity of the proposed criteria and the data collected from this instrument supports the criteria list as proposed. Further, to vet the recommendations of the AHP model, and to lend quantitative data to the decision making process, simulations of the various architectural options were done for various application scenarios. The results of these simulations further supported the architectural recommendations that had been created in the AHP model, and thus serve as additional validation of its efficacy. Further, the quantitative data generated from these decisions regarding factors such as system availability and scalability are usable in the decision-making process to ensure that business goals can be met under the selected architecture. The resulting model serves to allow consideration of a wide range of architectural options for computing, and is flexible enough to be applicable in any institution, while still retaining the core abilities that the model was designed to produce. This dissertation creates several novel contributions to the field, including the model for selection of computing architectures, the criterion of considering patient criticality within the design process for healthcare IT systems, and the use of simulation data to assist in the architectural selection process for computing architectures. It is expected that these contributions will be utilized by practitioners in healthcare IT to enhance their decision making processes in regard to computing architectures.