Simulation Modelling in Healthcare: Challenges and Trends

AbstractIn this paper, we describe simulation models in healthcare that have been developed in the past two decades. Simulation systems, ranging from simulation of patient flow in emergency rooms to simulation of populations with a specific chronic diseases, are reviewed. Simulation types included discrete event simulation (DES) and agent based simulation (ABS). A trend of variability and scalability were identified, and discussed in terms of platform used to develop the model, data sources, and computational power needed to run the simulation. In the synthesis of simulation models, programming languages and products emerged as clusters. Design models and systems engineering development processes are examined with a focus on requirements discovery, models and scenarios of simulation. Graphic user interfaces in the simulation tools in healthcare are reviewed in terms of visual design and human factors. Furthermore, interaction modes and trends of information visualization techniques used for the simulations are reported. Agent-based simulation models in particular were reviewed, and findings suggest agent characteristics varied across literature researched in aspects such as socio-demographic design considerations

Improving surgeon utilization in an orthopedic department using simulation modeling

Purpose: Worldwide more than two billion people lack appropriate access to surgical services due to mismatch between existing human resource and patient demands. Improving utilization of existing workforce capacity can reduce the existing gap between surgical demand and available workforce capacity. In this paper, the authors use discrete event simulation to explore the care process at an orthopedic department. Our main focus is improving utilization of surgeons while minimizing patient wait time. Methods: The authors collaborated with orthopedic department personnel to map the current operations of orthopedic care process in order to identify factors that influence poor surgeons utilization and high patient waiting time. The authors used an observational approach to collect data. The developed model was validated by comparing the simulation output with the actual patient data that were collected from the studied orthopedic care process. The authors developed a proposal scenario to show how to improve surgeon utilization. Results: The simulation results showed that if ancillary services could be performed before the start of clinic examination services, the orthopedic care process could be highly improved. That is, improved surgeon utilization and reduced patient waiting time. Simulation results demonstrate that with improved surgeon utilizations, up to 55% increase of future demand can be accommodated without patients reaching current waiting time at this clinic, thus, improving patient access to health care services. Conclusion: This study shows how simulation modeling can be used to improve health care processes. This study was limited to a single care process; however the findings can be applied to improve other orthopedic care process with similar operational characteristics. Keywords: waiting time, patient, health care processpublishedVersio

Simulation Analysis for in-Line Sorting-and-Washing of Reusable Pallets: A Case Study

In this study, a system constituting an integral part of a continuous sequence of operations or machines in a line is presented as a form of "in-line" system. Due to the sequential nature of the production line, the throughput rate of the line depends on the slowest process. This paper presents one example of an in-line system that is used for pallet re-use by sorting, washing, and drying in a continuous processing line. Basically, a well-structured in-line system provides high throughput because of the non-stop flow of materials in the system. However, there is a hidden loss in the system efficiency. The object of this paper is to evaluate some of the possible alternatives to solve these hidden inefficiency problems and to improve throughputs through simulated models. The three outcomes from this simulation indicate that exchanging the robotic arm with a sorter and adding additional spin-drying machines could reduce overhead costs and the average waiting time for the spin-drying machine and improve the utilization of the resources in the washing process

Simulation analysis of resource flexibility on healthcare processes

Purpose: This paper uses discrete event simulation to explore the best resource flexibility scenario and examine the effect of implementing resource flexibility on different stages of patient treatment process. Specifically we investigate the effect of resource flexibility on patient waiting time and throughput in an orthopedic care process. We further seek to explore on how implementation of resource flexibility on patient treatment processes affects patient access to healthcare services. We focus on two resources, namely, orthopedic surgeon and operating room. Methods: The observational approach was used to collect process data. The developed model was validated by comparing the simulation output with actual patient data collected from the studied orthopedic care process. We developed different scenarios to identify the best resource flexibility scenario and explore the effect of resource flexibility on patient waiting time, throughput, and future changes in demand. The developed scenarios focused on creating flexibility on service capacity of this care process by altering the amount of additional human resource capacity at different stages of patient care process and extending the use of operating room capacity. Results: The study found that resource flexibility can improve responsiveness to patient demand in the treatment process. Testing different scenarios showed that the introduction of resource flexibility reduces patient waiting time and improves throughput. The simulation results show that patient access to health services can be improved by implementing resource flexibility at different stages of the patient treatment process. Conclusion: This study contributes to the current health care literature by explaining how implementing resource flexibility at different stages of patient care processes can improve ability to respond to increasing patients demands. This study was limited to a single patient process; studies focusing on additional processes are recommended. Keywords: agile strategy, waiting time, throughput, patient access, responsivenesspublishedVersio

Integração da simulação a Eventos Discretos e mapeamento do fluxo de valor para melhoria do sistema de distribuição de medicamentos em um hospital.

Este trabalho utilizou a Simulação a Eventos Discretos e o Mapeamento do Fluxo de Valor (MFV) para avaliar o sistema de distribuição de medicamentos de um hospital, devido a constantes atrasos na entrega de medicamentos aos pacientes. Desta forma, o trabalho tem como objetivo integrar simulação a eventos discretos e o mapeamento do fluxo de valor para melhoria de processos em uma farmácia hospitalar. A presente pesquisa aplicou a metodologia modelagem e simulação para analisar o sistema, em que foi divida em fase de concepção, fase de implementação, e fase de análise, na qual foi realizado um diagnóstico por meio do Mapa do Estado Atual para identificar os erros e desperdícios do processo e posteriormente, foram discutidas alterações através do Mapa do Estado Futuro. O Mapa do Estado futuro elaborado foi utilizado como projeto experimental da simulação. As alterações propostas foram o balanceamento da chegada das prescrições, a abertura de um novo posto de trabalho e a eliminação das paradas não planejadas dos funcionários. Analisando os relatórios obtidos com as replicações foi possível compreender e quantificar o impacto dessas alterações sobre o processo. Por fim, com utilização Delineamento de Experimentos, notou-se que com todos os fatores no estado de melhoria é possível atender todos os pacientes de acordo com a prescrição médica

Proposta de um framework para a condução de projetos de simulação a eventos discretos integrando Lean Six Sigma

There are many Modeling and Simulation methods available in the literature, which are commonly used in Discrete Event Simulation (DES) projects. However, most of these methods focus on constructing computational models, which results in opportunities for improvement in the problem definition and results analysis stages, as they are often overlooked. One way to address this gap is by incorporating alternative methods to DES. In this regard, the integration of the Lean Six Sigma (LSS) method, based on the Define, Measure, Analyze, Improve, and Control (DMAIC) framework, with the DES method has shown positive results in various application areas. However, the scientific literature provides few studies addressing this integration. Given this context, the present work aims to propose a framework that allows the integration of a robust and systematic problem-solving method (LSS) with a modeling and simulation tool (DES), aiming for more accurate and effective execution of discrete event simulation projects. To achieve this objective, a systematic literature review (SLR) was conducted to gather information regarding the state of the art on the topics addressed, to identify the focus of the works (DES or LSS), their structure, usage, and the main tools involved in the process, to determine in which phases of DMAIC the DES models were applied, and finally, the main elements found in works that presented some kind of framework. Among the analyzed works, only seven proposed an integrated framework, all of which were evaluated to identify past and future trends in the main practices presented. Based on these analyses, associations were established between the concepts of the two methods to relate common objectives within the same stage. Thus, the initial version of the framework was developed and subsequently subjected to evaluation through an online questionnaire, answered by experts in the areas of DES and LSS, with the purpose of being statistically validated. After considerations and tests, the final version of the framework was proposed, representing the outcome of this work.Existem muitos métodos de Modelagem e Simulação disponíveis na literatura, que são comumente utilizados em projetos de Simulação a Eventos Discretos (SED). No entanto, a maioria destes métodos tem seu foco na construção de modelos computacionais. Isso faz com que as etapas de definição do problema e análise de resultados apresentem oportunidades de melhoria, uma vez que são pouco exploradas. Uma das formas de suprir tal lacuna é incorporar métodos alternativos a SED. Nesse sentido, a integração do método Lean Six Sigma (LSS), baseado no formato Define, Measure, Analyze, Improve e Control (DMAIC), juntamente com o método de SED, tem demonstrado resultados positivos em diversas áreas de aplicação. No entanto, a literatura científica apresenta poucos estudos abordando essa integração. Diante desse contexto, o presente trabalho tem como objetivo propor um framework que permita a integração de um método robusto e sistemático de resolução de problemas (LSS) com uma ferramenta de modelagem e simulação (SED), visando a execução mais assertiva e eficaz de projetos de simulação a eventos discretos. Para atingir esse objetivo, foi realizada uma revisão sistemática de literatura (RSL) com o intuito de reunir informações no sentido de entender o estado da arte a respeito dos temas abordados, descobrir o foco nos trabalhos (SED ou LSS), a maneira como estavam estruturados, a forma como eram utilizados e quais as principais ferramentas envolvidas nesse processo, em quais fases do DMAIC os modelos de SED eram aplicados e por fim, os principais elementos encontrados em trabalhos que apresentavam algum tipo de framework. Dos trabalhos analisados, apenas sete propuseram um framework integrado, sendo todos eles avaliados para identificar tendências passadas e futuras das principais práticas apresentadas. Com base nessas análises, foram estabelecidas associações entre os conceitos dos dois métodos de forma a relacionar os objetivos comuns em uma mesma etapa. Assim, o framework em sua versão inicial foi desenvolvido e posteriormente submetido a uma avaliação por meio de um questionário online, respondido por especialistas nas áreas de SED e LSS, com o propósito de ser validado estatisticamente. Após considerações e testes, a versão final do framework foi proposta, representando o produto deste trabalho