Simulation methods in the healthcare systems

International audienceHealthcare systems can be considered as large-scale complex systems. They need to be well managed in order to create the desired values for its stakeholders as the patients, the medical staff and the industrials working for healthcare. Many simulation methods coming from other sectors have already proved their added value for healthcare. However, based on our experience in the French heath sector (Jean et al. 2012), we found these methods are not widely used in comparison with other areas as manufacturing and logistic. This paper presents a literature review of the healthcare issue and major simulations methods used to address them. This work is design to suggest how more systematic creation of solutions may be performed using complementary methods to resolve a common issue. We believe that this first work can help to better understand the simulation approaches used for health workers, deciders or researchers of any responsibility level

La utilización de la investigación de operaciones como soporte a la toma de decisiones en el sector salud: Un estado del arte

The contributions of Operations Research (OR) in the healthcare field have been extensively studied in the scientific literature since the 1960s, covering decision support tools with operational, tactical, and strategic approaches. The aim of this article is to analyze the historical development of the application of OR models in healthcare. The application trends for optimization, planning, and decision- making models are studied through a descriptive literature review and a bibliometric analysis of scientific papers published between 1952 and 2016. An upward trend in the usage of operational models is observed with the predominance of resource optimization approaches and strategic decision-making for public health.Los aportes de la Investigación de Operaciones (IO) en el campo de la salud han sido ampliamente estudiados en la literatura científica desde la década de 1960, abarcando herramientas para el soporte a la decisión en enfoques operacionales, tácticos y estratégicos. El objetivo de este artículo es analizar el avance y el desarrollo histórico del uso de modelos operativos en el campo de la salud. A través de una revisión bibliográfica descriptiva y un análisis bibliométrico de artículos científicos publicados durante el periodo 1952-2016, se estudia el comportamiento de las tendencias en la aplicación de modelos operativos para la optimización, la planificación y la toma de decisiones en el sector salud. Se evidencia una tendencia creciente en el uso de modelos de IO durante el periodo estudiado, predominando las aplicaciones orientadas a la optimización de recursos y decisiones estratégicas de salud pública

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

Flexible nurse staffing based on hourly bed census predictions

Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years

Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning

In this chapter, we provide an overview of inventory management within the pharmaceutical industry and how to model and optimize it. Inventory management is a highly relevant topic, as it causes high costs such as holding, shortage, and reordering costs. Especially the event of a stock-out can cause damage that goes beyond monetary damage in the form of lost sales. To minimize those costs is the task of an optimized reorder policy. A reorder policy is optimal when it minimizes the accumulated cost in every situation. However, finding an optimal policy is not trivial. First, the problem is highly stochastic as we need to consider variable demands and lead times. Second, the supply chain consists of several warehouses incl. the factory, global distribution warehouses, and local affiliate warehouses, whereby the reorder policy of each warehouse has an impact on the optimal reorder policy of related warehouses. In this context, we discuss the concept of multi-echelon inventory optimization and a methodology that is capable of capturing both, the stochastic behavior of the environment and how it is impacted by the reorder policy: Markov decision processes (MDPs). On this basis, we introduce the concept, its related benefits and weaknesses of a methodology named Reinforcement Learning (RL). RL is capable of finding (near-) optimal (reorder) policies for MDPs. Furthermore, some simulation-based results and current research directions are presented

IoT-based Asset Management System for Healthcare-related Industries

The healthcare industry has been focusing efforts on optimizing inventory management procedures through the incorporation of Information and Communication Technology, in the form of tracking devices and data mining, to establish ideal inventory models. In this paper, a roadmap is developed towards a technological assessment of the Internet of Things (IoT) in the healthcare industry, 2010–2020. According to the roadmap, an IoT-based healthcare asset management system (IoT-HAMS) is proposed and developed based on Artificial Neural Network (ANN) and Fuzzy Logic (FL), incorporating IoT technologies for asset management to optimize the supply of resources

Operations research as a decision-making tool in the health sector: A state of the art

The contributions of Operations Research (OR) in the healthcare field have been extensively studied in the scientific literature since the 1960s, covering decision support tools with operational, tactical, and strategic approaches. The aim of this article is to analyze the historical development of the application of OR models in healthcare. The application trends for optimization, planning, and decision- making models are studied through a descriptive literature review and a bibliometric analysis of scientific papers published between 1952 and 2016. An upward trend in the usage of operational models is observed with the predominance of resource optimization approaches and strategic decision-making for public health.Los aportes de la Investigación de Operaciones (IO) en el campo de la salud han sido ampliamente estudiados en la literatura científica desde la década de los 60, abarcando el soporte a la decisión en enfoques operacionales, tácticos y estratégicos. Se presenta un resumen del desarrollo histórico de la IO en el campo de la salud y se listan los principales modelos aplicados en los últimos años, identificando el principal enfoque utilizado, y el potencial aporte a la toma de decisiones en el campo de la salud