Impact of Medical Logistics on the Quality of Life of Health Care Users

Buntak, Krešimir; Kovačić, Matija; Martinčević, Ivana

doi:10.24874/pes01.02.109

Cited by 5 publications

(2 citation statements)

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“…According to Landry and Philippe (2004), optimizing and reengineering healthcare logistics could bring new sources of effectiveness and efficiencies both in logistical and clinical terms. HL aims at ensuring smooth physical, information and financial flows, and reducing the waiting time by a timely provision of needed resources to meet demands of healthcare users as well as the healthcare system as a whole (Buntak, Kovačić & Martinčević, 2019;Landry & Philippe, 2004). For instance, the unavailability of supplies may postpone a planned surgical intervention and negatively impact the patient's health.…”

Section: Hospital Logistics and Its Interaction With Qc And Psmentioning

confidence: 99%

Modeling the impact of hospital logistics on quality of care and patient satisfaction: Results of a survey in three public healthcare facilities in Fez, Morocco

Frichi¹,

Jawab²,

Boutahari³

2020

JIEM

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Purpose: The objective of this research is to examine the role of hospital logistics in improving quality of care and patient satisfaction. Thus, the paper focuses on modeling and evaluating the relationship between these three constructs.Design/methodology/approach: In the present study, hospital logistics was specified as a second-order construct composed of five first-order constructs: physical accessibility, waiting time, consultation time, hospital hotel services, and administrative procedures. A questionnaire was developed and administered face-to-face to 384 hospitalized patients in three public healthcare facilities in Fez-Morocco. Collected data were processed and analyzed deploying the PLS-SEM method and using SmartPLS3 software. Data analysis was carried out by considering two types of patient circuits according to the admission modes in the healthcare facility: Urgent Patients Circuit and Scheduled Patients Circuit. Thus, two PLS-SEM models were evaluated and validated.Findings: Results highlighted the significant impact of hospital logistics on quality and satisfaction. In particular, the results of the two models showed that the most preponderant hospital logistics component is physical accessibility which consists of the availability and accessibility of ambulances, medical and nursing staff, support and guidance staff, technical facilities and equipment, etc. Thus, hospital managers and health system stakeholders should pay particular attention to hospital logistics activities in general and specifically to the physical accessibility to improve the quality of care and patient satisfaction.Research limitations/implications: This study only included patients from three public healthcare facilities in Fez-Morocco. Also, the model variables of hospital logistics construct are restricted and were applied in a specific context. Besides, the sample size was relatively reduced. Thus, results generalization might be limited. Further studies including more patients from other territories and including other logistics components are needed for large-scale validation of the proposed model. Originality/value: The results of this study contribute to the scientific literature on hospital logistics and its role as a lever for quality of care and patient satisfaction.

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Section: Hospital Logistics and Its Interaction With Qc And Psmentioning

confidence: 99%

Modeling the impact of hospital logistics on quality of care and patient satisfaction: Results of a survey in three public healthcare facilities in Fez, Morocco

Frichi¹,

Jawab²,

Boutahari³

2020

JIEM

View full text Add to dashboard Cite

show abstract

“…The UK's National Health Service (NHS) is targeting net-zero emissions by 2040 and has highlighted the need to change transport behaviours in order to achieve this goal [2]. Despite emissions from transport being a globally recognised problem, effective logistics strategies have been highlighted as critical to the success of healthcare supply chains and patient care outcomes, and any alterations to existing NHS transport systems to mitigate emissions must not affect the overall effectiveness of operations [3,4]. As part of this endeavour, new logistics systems are being explored using innovative freight modes such as autonomous (driverless) vehicles [2,5,6].…”

Section: Introductionmentioning

confidence: 99%

Enabling Safe and Sustainable Medical Deliveries by Connected Autonomous Freight Vehicles Operating within Dangerous Goods Regulations

et al. 2022

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Health service providers in developed nations are responsible for 5% of their national carbon emissions, much of which originate from transport and supply chains. Connected autonomous freight vehicles (CAV-Fs) offer the potential to reduce this impact and enable lower cost operations, with trials being explored across the world. Transportation and carriage regulations, particularly in relation to the movement of dangerous goods (DG) such as medicines and diagnostic specimens, have not been developed for and applied to this new transport mode, particularly where loads are unaccompanied. Through an audit of current legislation and practice, this paper evaluates current DGs regulations applied to the transportation of medical products and medicines by autonomous road vehicles. Where existing regulations are not appropriate for CAV-Fs, recommendations and adaptations have been proposed to support safe and practical application. Remote monitoring and tracking of vehicles are critical for ensuring load security, quick and effective incident response, and management of documents and communications between parties. Loading/unloading procedures are significantly more important than in crewed vehicles, with load segregation and recording of all activity being of key importance. Other recommendations relate to training provision, vehicle specifications, and product health monitoring.

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A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic

Ghaithan

Alarfaj

Mohammed

et al. 2022

Neural Comput & Applic

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Since COVID-19 was declared as a pandemic by World Health Organization in March 2020, 169,682,828 cases have been reported worldwide, with 151,416,570 recovered, and 3,526,647 deaths by May 28, 2021. Oxygen gas cylinders demand is booming globally due to its need for COVID-19’s for intensive care. Thus, it is critical for hospitals to know exactly the time of receiving oxygen gas cylinders since this will help in minimizing the fatality rate. In this regards, this paper proposes a Multilayer Perceptron Neural Network-based model to predict the delivery time of oxygen gas cylinders for a real-life logistics data from a company that delivers oxygen gas cylinders to all cities around Saudi Arabia. Besides, Multilayer Perceptron Neural Network is benchmarked to supported vector machine and multiple linear regression. Although all the considered models have the ability to provide accurate prediction results, the findings indicate that the proposed supported vector machine and Multilayer Perceptron Neural Network model provide better prediction results. The analysis was achieved through a methodology to identify factors with the highest impact and build a neural network model. The model was further optimized to identify the best order and select the best subset of input variables. The analysis showed that the neural network model can be used effectively to estimate the delivery time of oxygen gas cylinders. The model illustrated high accuracy of prediction by comparing the predicted values to the actual values.

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Impact of Medical Logistics on the Quality of Life of Health Care Users

Cited by 5 publications

References 0 publications

Modeling the impact of hospital logistics on quality of care and patient satisfaction: Results of a survey in three public healthcare facilities in Fez, Morocco

Modeling the impact of hospital logistics on quality of care and patient satisfaction: Results of a survey in three public healthcare facilities in Fez, Morocco

Enabling Safe and Sustainable Medical Deliveries by Connected Autonomous Freight Vehicles Operating within Dangerous Goods Regulations

A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic

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