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Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.

This paper aims to propose an approach by human and material resources combination to reduce hospitals crowding. Hospitals crowding is becoming a serious problem. Many research works present several methods and approaches to deal with this problem. However, to the best of the authors’ knowledge – after a deep reading of literature – in all the proposed approaches, human and material resources are studied separately while they must be combined (to a given number of material resources an optimal number of human resources must be assigned and vice versa) to reflect reality and provide better results.

Hospital inpatient unit is chosen as framework. This unit crowding reduction is carried out by its capacity increasing. Indeed, inpatient unit modeling is performed to find the adequate combinations of human and material resources numbers insuring this unit stability and providing optimal service rates. At first, inpatient unit is modeled using queuing networks and considering only two resources (beds and nurses). Then, the obtained service rate formula is improved by including other resources and parameters using Baskett, Chandy, Muntz and Palecios (BCMP) queuing networks. This work is applied to “Princess Lalla Meryem” hospital inpatient unit.

Results are patients’ average number reduction by an average (in each block) of three patients, patients’ average waiting time reduction by an average of 9.98 h and non-admitted patients (to inpatient wards) access percentage of 39.26 per cent on average.

Previous works focus their studies on either human resources or material resources. Only a few works study both resources types, but separately. The context of those studies does not meet the real hospital context (where human resources are combined with material resources). Therefore, the provided results are not very reliable. In this paper, an approach by human and material resources combination is proposed to increase inpatient unit care capacity. Indeed, this approach consists of developing inpatient unit service rate formula in terms of human and material resources numbers.