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Top1. Introduction
The recent years have witnessed a great amount of research efforts to augment and enhance the management practices in the healthcare industry. A considerable part of these efforts has been devoted to the patients flow among the provided healthcare processes and services in the health units (hospitals, health centers, etc.) (e.g., Armony et al., 2015; Bean, Taylor, & Dobson, 2017; Dong & Perry, 2018; Fitzgerald, Pelletier, & Reznek, 2017; Vass & Szabo, 2015). These processes and services represent facilities that should be managed carefully in order to improve the patients flow. Improving such flow can result in providing a sufficient care to the admitted patients as well as achieving their satisfaction (Armony et al., 2015). An important aspect of the careful management of these facilities is tracking their performance with respect to many dimensions relating to the patients flow. These dimensions comprise the patient waiting times to benefit from these facilities, response times of these facilities, number of patients waiting in the queues of these facilities, number of patients served, utilization of these facilities, and patient satisfaction (Aziati & Hamdan, 2018; Hall, Belson, Murali, & Dessouky, 2013; Hu, Barnes, & Golden, 2018). Most of these dimensions are linked to the healthcare delivery quality. For instance, Cerda´, Pablos, & Rodriguez (2013) included the usage of the length of the waiting lists as a measure of the quality of the health system.
Accordingly, effective tools that aid the healthcare decision makers in tracking and improving the aspects of these dimensions are highly needed. Fulfilling this need has not received a wide attention on the level of the patients flow in the public hospitals. This is because the majority of the previous studies have focused on proposing tools on the level of the clinical decision making, indicating that these tools are directed to aid in the diagnosis and treatment of diseases (e.g., Cánovas-Segura, Campos, Morales, Juarez, & Palacios, 2016; Gudmundsson, Hansen, Halldorsson, Ludviksson, & Gudbjornsson, 2019; Sim, Ban, Tan, Sethi, & Loh, 2017; Yılmaz & Ozdemir, 2017).
Consequently, the present study responds to this need by developing specifications for a new decision support system (DSS) that provides the key stakeholders of the public hospitals with the required estimates for a set of crucial performance indicators pertaining to the patients flow. Producing these estimates is specifically vital for predicting the performance dimensions, supporting the decision making process, and improving the administrative processes in these hospitals. Moreover, these estimates are highly required due to the difficulty in knowing and tracking the actual values of those performance indicators. This difficulty stems from an observed lack in many public hospitals, which is that the events pertaining to the patients’ movements among the provided facilities as well as those related to their treatments and tests are either not well registered and time stamped or not given attention at all. In this regard, Hall et al. (2013) pointed out that many hospitals have encountered difficulties in making these estimates due to using inadequate information systems or not having the required resources to develop and implement the needed information systems.