Erlang-based stochastic model for patient flow - Texas A&M University (TAMU) Scholar

The development and application of stochastic models in health care reflect a balance between the behavior of the environment and the theoretical assumptions of the models. Among Markov and semi-Markov models, this balance is particularly important as the final models typically sacrifice their assumptions for realism. While these models can be very successful in representing patient flow, limitations can arise if analytic results are desired. This paper proposes the use of the Erlang distribution, through a generalized approximation, as a tool to analytically model patient flow.

Erlang-based stochastic model for patient flow Conference Paper