A stochastic location model for designing primary healthcare networks integrated with workforce cross-training

Abstract

Health posts aim to elevate the level of public health by providing primary health services. The effectiveness of a health post network depends on factors such as the number and location of health posts. The service quality in such networks can be improved by controlling the amount of congestion at the facilities. An important policy for decreasing operational cost of these networks is to consider a workforce mix of flexible and dedicated servers. This paper integrates the network design with workforce cross-training in the presence of congestion, where the queuing system at each health post is modeled by a set of multi-class M/G/$m$ queues offering multiple service types. The problem is formulated as an integer nonlinear programming model, and a linearization method is used to solve it. A hypothetical case study illustrates how the model can be used, and interesting managerial insights are presented.