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Correction to Krohn, Ralf, Sven Müller, and Knut Haase. “Correction to: Preventive healthcare facility location planning with quality-conscious clients.” OR Spectrum (2024): 1-1. [ABSTRACT FROM AUTHOR]

Pursuing the overarching goal of saving both lives and healthcare costs, we introduce an approach to increase the expected participation in a preventive healthcare program, e.g., breast cancer screening. In contrast to sick people who need urgent medical attention, the clients in preventive healthcare decide whether to go to a specific facility (if this maximizes their utility) or not to take part in the program. We consider clients' utility functions to include decision variables denoting the waiting time for an appointment and the quality of care. Both variables are defined as functions of a facility's utilization. We employ a segmentation approach to formulate a mixed-integer linear program. Applying GAMS/CPLEX, we optimally solved instances with up to 400 demand nodes and 15 candidate locations based on both artificial data as well as in the context of a case study based on empirical data within one hour. We found that using a Benders decomposition of our problem decreases computational effort by more than 50%. We observe a nonlinear relationship between participation and the number of established facilities. The sensitivity analysis of the utility weights provides evidence on the optimal participation given a specific application (data set, empirical findings). [ABSTRACT FROM AUTHOR]

In this contribution we build on the approach proposed by Zhang et al. (OR Spectrum 34:349-370, ) to consider clients' choice in preventive health care facility location planning. The objective is to maximize the participation in a preventive health care program for early detection of breast cancer in women. In order to account for clients' choice behavior the multinomial logit model is employed. In this paper, we show that instances up to 20 potential locations and 400 demand points can be easily solved (to optimality or at least close to optimality) by a commercial solver in reasonable time if the problem is modeled by an alternative formulation. We present an intelligible approach to derive a lower bound to the problem. Our paper provides interesting insights into the trade-off between minimum workload requirement (to ensure quality of care) and participation (and thus early diagnosis of disease). We present a general definition of clients' utility (which allows for clients' characteristics, for example) and discuss some fundamental issues (and pitfalls) concerning the specification of utility functions. [ABSTRACT FROM AUTHOR]