Modelling the redistribution of hospital supply to achieve equity taking account of patient's behaviour

Abstract

Policies that seek to achieve geographic equity in countries with a National Health Services (NHS) require information on how to change the distribution of supply to achieve greater equity in access and utilisation. Previous methods for analysing the impact of hospital changes have relied on crude assumptions on patients' behaviour in using hospitals. The approach developed in this study is a multi-modelling one based on two mathematical programming location-allocation models to redistribute hospital supply using different objective functions and assumptions about the utilisation behaviour of patients. These models show how different policy objectives seeking equity of geographic access or utilisation produce different results and imply trade-offs in terms of reduction in total utilisation.

Location Problems in Healthcare

“…For a review of these definitions, see Marsh and Schilling (1994). Commonly used equity objectives are: minimize the maximum distance that patients must travel (Mitropoulos et al 2006;Güneş et al 2014), minimize deviations from a standard distance (Smith et al 2009(Smith et al , 2013, minimize differences of utilization from a national norm (Oliveira and Bevan 2006), or minimize standard deviation of the distribution of the allocated populations to healthcare facilities (Güneş et al 2014). …”

“…For example, Güneş and Yaman (2009) modeled the resource re-allocation problem for a hospital network with beds as resources. Oliveira and Bevan (2006), Griffin et al (2008), Zhang et al (2009Zhang et al ( , 2010 and Güneş et al (2014) modeled the staff in each facility as a decision variable. In addition, these models can incorporate the decision about the services to offer in each facility (cf.…”

“…In addition, these models can incorporate the decision about the services to offer in each facility (cf. Oliveira and Bevan 2006;Griffin et al 2008). With R denoting the set of resource types and S the set of service types, such a model can be built by defining resource sets R s  R required to serve demand for service s 2 S .…”

“…One approach is to assume that patients patronize each facility with a certain probability that depends on its location as well as on other relevant factors. For example, Oliveira and Bevan (2006) used a gravity model to define the probability that patients in some district or region choose some hospital.…”

Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems

“…For instance, Mayhew and Leonardi (1982) analysed alternative definitions of equity, efficiency and access for resource allocation in the health-sector context. Smith et al (2010) combined efficiency and equity objectives in location models where equity is introduced with a service standard distance, from which absolute deviation is minimized (note that the definition of efficiency in these studies is defined as alternative definitions of equity in other studies Oliveira and Bevan 2006). Eiselt and Laporte (1995) presented a comprehensive review of the objectives in location problems where the minimization of the total travel time (minisum objective) is associated with the private sector and the minimization of the maximum travel time (minimax objective) with the public sector.…”