Improving Interhospital Medical Patient Transportation in Morocco: A Forecasting Collaborative Approach

Improving Interhospital Medical Patient Transportation in Morocco: A Forecasting Collaborative Approach

Youness Frichi, Abderrahmane Ben Kacem, Fouad Jawab, Oualid Kamach, Samir Chafik
DOI: 10.4018/978-1-7998-0268-6.ch008
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Abstract

Healthcare facilities are nowadays facing several challenges in terms of quality of care, costs, and performance. Collaboration with stakeholders is a promising way to overcome these challenges. In Morocco, healthcare access and continuity of care remain difficult due, among others, to the various stakeholders involved and the lack of ambulances for extra-hospital and interhospital medical patient transportation (MPT). In this chapter, the aim was to explore collaboration in healthcare supply chain to improve the availability of ambulances for interhospital MPT (transfers). For this purpose, an overview of the MPT system in Morocco was presented while highlighting its main issues. Then, a case study of three hospitals in Casablanca City was analyzed employing a collaborative approach. It consisted in forecasting transfer requests for next periods based on past data, and redistributing the ambulances of the three hospitals according to the forecasts. Findings attest to the variability in demand in the three hospitals and therefore the need for a dynamic allocation of ambulances.
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Introduction

In Morocco, healthcare infrastructure, including the number of healthcare facilities and physicians, has undergone significant changes over the years, enabling the health system to make significant progress. According to statistics from the Ministry of Health, life expectancy increased from 64.6 in 1990 to 75.8 years in 2015, infant and child mortality fell from 76‰ in 1990 to 27.6‰ in 2011, and maternal mortality declined from 317 per 100,000 live births in 1990 to 72.6 in 2015 (Ministry of health, 2016). However, access to healthcare remains difficult due to many deficiencies, including distance to healthcare facilities, unbalanced distribution of healthcare provision, insufficient number of health professionals, cost of care, and inadequacy of the medical transportation system, (Yaakoubd, 2010). All these difficulties have been highlighted in several scientific publications that have mentioned the shortcomings of the health system in terms of governance (Dehbi, 2017), medical coverage, resource allocation (Alami, 2017; Legros & Chaoui, 2012), etc. However, medical transportation issues have not been the subject of any specific research work. Little is known about its organization and functioning as well as solutions for its improvement.

The interest in medical transportation is derived from its crucial role in achieving the Sustainable Development Goals (SDG), which the United Nations member states have committed to achieve by 2030, in particular SDG 3 on improving health and well-being that aims to promote access to healthcare (United Nations, 2015).

Organizations, including healthcare facilities, can rely on collaboration to effectively manage their activities and enhance their performance (Singh, Garg, & Sachdeva, 2018). It is recognized that collaboration among health system stakeholders leads to improved health outcomes (Morley & Cashell, 2017). The current study objective is to discuss collaboration between healthcare facilities to face medical transportation challenges. Among the existing collaborative approaches, the one considered for the purpose of this chapter is collaborative forecasting. The basic idea is that hospitals can forecast transportation demands based on past performed transportation, and then redistribute their ambulances according to the established forecasts.

The remainder of this chapter is organized as follow. The second section presents backgrounds about medical patient transportation, healthcare supply chain collaboration and demand forecasting methods. Third section takes stock of the current situation of the medical patient transportation in Morocco, while highlighting its main difficulties and challenges. Fourth section details time series forecasting models. Fifth section develops ARIMA forecasting model to predict interhospital transportation demand for three hospitals in Casablanca city. Sixth section discusses results and provides insights into options for improving medical transportation.

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