Date of Graduation

8-2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Industrial Engineering

Advisor/Mentor

Rardin, Ronald L.

Committee Member

Pohl, Edward A.

Second Committee Member

Buyurgan, Nebil

Third Committee Member

Hardgrave, Bill

Keywords

Applied sciences; Healthcare; Identification standards; Supply chains; System dynamics; Technology adoption

Abstract

The adoption of identification standards and its associated technology in the healthcare supply chain has been slow over the past twenty five years, despite the evidence of the benefits that can be achieved. The widespread use of identification standards in the form of barcode labeled medical products can contribute to the reduction of point of care errors and can increase the efficiency of healthcare supply chain related processes. This research is focused on the analysis of the adoption of identification standards in the healthcare supply chain with a particular focus on the healthcare provider adoption challenges. The research is divided into two phases.

The first phase develops an extensive literature review on technology adoption with a particular focus on data standards. This adoption process is compared with the adoption of Electronic Health Records (EHR) and Electronic Data Interchange (EDI); main conclusions from the identification standards literature are presented, and a conceptual model to explain the identification-standards adoption process is proposed.

The second phase proposes a model for identification standards adoption using a system dynamics modeling approach. The model builds on previous findings associated to the factors affecting identification standards adoption and relates the specific elements to the adoption rate via a causal loop diagram (CLD). The model is formulated in two stages. In the first stage, the Bass Diffusion Model (BDM) of technology adoption is adapted to simulate the adoption of identification standards supporting technologies. The second stage uses most of the factors defined in the CLD to develop a simulation model. A sensitivity analysis identifies relevant model parameters that facilitated the design of interventions to move the adoption process forward. Finally, the effects of some possible interventions are simulated using the validated model. The model provides an illustration of the use of system dynamics models and diffusion theory to understand an important policy problem reported in the literature and not yet solved. Also this research informs real world practitioners and the academic community on issues like the lack of data and other challenging aspects of empirical research that can be addressed with the proposed model and methodology.

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