A Generalized System Dynamics Model for Managing Transition-Phases in Healthcare Environments

Abstract

Process change is often difficult to manage, let alone to predict. In this paper, adaptation function theory is used to illustrate how a system dynamics-based model can be used to anticipate the effects that process change policies and strategies will have on change execution. The adaptation function is a form of learning curve that incorporates a performance goal following an exponential growth/decay behavior. Adaptation function focuses on learning by doing, thus making it ideal to assess a transition-phase between two processes; that is, the process to implement a new process. The methodology proposed delineates how system archetypes can be used as the building blocks to model learning by doing transition-phases. In addition, the methodology is validated through the establishment of the theoretical foundations to build a transition phase management model, contextualized in an electronic health records (EHR) system implementation process. The resulting model and framework is validated through extremes testing and potential applications for the methodology and EHR model are discussed.