Ricciardi Carlo, Ponsiglione Alfonso Maria, Converso Giuseppe, Santalucia Ida, Triassi Maria, Improta Giovanni
Department of Advanced Biomedical Sciences, School of Medicine and Surgery, University of Naples "Federico II", Naples, Italy.
Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
Math Biosci Eng. 2020 Nov 27;18(1):253-273. doi: 10.3934/mbe.2021013.
In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a "behavioral" Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital "A. Cardarelli" of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states.
在文献中,已经提出了几种组织解决方案来确定患者从急诊科自愿出院的概率。在此,通过基于马尔可夫理论的建模来分析自动出院问题,这是近年来在医疗保健领域广泛应用的一种创新方法。这项工作的目的是通过定义一个通用模型来描述急救过程,使用“行为”马尔可夫链模型提出一种计算自愿出院率的新方法,这是一种考虑患者满意度的新方法。然后将所提出的模型在MATLAB中实现,并通过那不勒斯“A. Cardarelli”医院的实际案例研究进行验证。结果发现,大多数自动出院风险发生在患者就诊前的等待时间和等待最终报告的时间内;通常,一旦请求进行分析,患者虽然不太满意,但愿意等待更长时间以获取结果。该模型允许从患者的角度描述急救过程。所提出的模型是通用的,只需改变状态之间的转移概率就可以适应每个医院设施。