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Applying Discrete Event Simulation on Patient Flow Scenarios with Health Monitoring Systems
Abstract
Despite technological advances, today there are still many treatments that have not been addressed by remote monitoring due to the absence of reliable monitoring devices and/or Health Monitoring Systems (HMS). In addition, there are situations where the deficiency of data due to the lack of a real scenario or device makes it impossible to use artificial intelligence (AI) techniques. However, this problem could be solved with simulation, being a fundamental mechanism for predicting and forecasting not-existing scenarios. In this paper, we proposed the use of Discrete-Event Simulation (DES) to model complex HMS scenarios. We have integrated a simulation module based on Matlab Simulink, into the MoSTHealth framework, so that the digital twins (DTs) modelled by the framework are elements of the DES scenario that the medical expert can easily parameterize through a mobile interface. A case study has been defined on the use of a wearable device (under development) that collects relevant hormone levels in real time, during infertility treatment. The DES simulation demonstrates an increase in the number of patients seen by one physician by 88,8%. In addition, the average waiting time for consultation decreased by 36.5%.KeywordsWeb of things (WoT)MATLABSimulinkSimEventsinfertility treatmentsimulationbiosensorModel Driven Engineering (MDE)Digital TwinDiscrete-Event Simulation (DES)
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According to doctors and researchers, fertility problems are becoming epidemic proportions. Meanwhile, the demand for infertility treatment is increasing by 5–10% per year. To support the growing demand, physicians need to define personalized remote monitoring treatments supported by devices that send real-time information on hormones levels, heart-rate, temperature, etc. To this end, Healthcare Monitoring Systems (HMS) have recently appeared, based on increasingly advanced devices that help to manage this task. However, current solutions are expensive and not very customizable by physicians themselves. In this paper, we propose a framework called MoSTHealth, based on digital twins and Model-Driven Engineering (MDE), allows healthcare experts to model a personalized Web of Things (WoT) HMS scenario per treatment and per patient. Thanks to MDE, the simulated scenario allows us to generate a Service-Oriented enterprise cloud architecture that integrates a prediction module based on machine learning and data analysis. In this paper, a WoT HMS scenario for infertility treatment is presented as a case study. In this scenario, a specific care plan is defined, associated with a set of devices, including the use of a biosensing device that sends hormones levels in real-time.KeywordsInfertility treatmentWeb of Things (WoT)Internet of Things (IoT) devicesHealthcare Monitoring System (HMS)SimulatorDigital twin
In Fertility Centers, quality should be measured by how well the organization complies with pre-defined requirements , and by how quality policies are implemented and quality objectives achieved. Having a quality management system (QMS) is a mandatory requirement for IVF centers established in most countries with regulatory guidelines, including Brazil. Nevertheless, none of the regulatory directives specify what a QMS must have in detail or how it should be implemented and/or maintained. ISO 9001 is the most important and widespread international requirement for quality management. ISO 9001 standards are generic and applicable to all organizations in any economic sector, including IVF centers. In this review, we discuss how we implemented QMS according to ISO 9001 and what we achieved 5 years later. In brief, with ISO we defined our structure, policies, procedures, processes and resources needed to implement quality management. In addition, we determined the quality orientation of our center and the quality objectives and indicators used to guarantee that a high-quality service is provided. Once measuring progress became part of our daily routine, quantifying and evaluating the organization's success and how much improvement has been achieved was an inevitable result of our well-established QMS. Several lessons were learned throughout our quality journey, but foremost among them was the creation of an internal environment with unity of purpose and direction; this has in fact been the key to achieving the organization's goals.
Background:
Hospital emergencies have an essential role in health care systems. In the last decade, developed countries have paid great attention to overcrowding crisis in emergency departments. Simulation analysis of complex models for which conditions will change over time is much more effective than analytical solutions and emergency department (ED) is one of the most complex models for analysis. This study aimed to determine the number of patients who are waiting and waiting time in emergency department services in an Iranian hospital ED and to propose scenarios to reduce its queue and waiting time.
Methods:
This is a cross-sectional study in which simulation software (Arena, version 14) was used. The input information was extracted from the hospital database as well as through sampling. The objective was to evaluate the response variables of waiting time, number waiting and utilization of each server and test the three scenarios to improve them.
Results:
Running the models for 30 days revealed that a total of 4088 patients left the ED after being served and 1238 patients waited in the queue for admission in the ED bed area at end of the run (actually these patients received services out of their defined capacity). The first scenario result in the number of beds had to be increased from 81 to179 in order that the number waiting of the "bed area" server become almost zero. The second scenario which attempted to limit hospitalization time in the ED bed area to the third quartile of the serving time distribution could decrease the number waiting to 586 patients.
Conclusion:
Doubling the bed capacity in the emergency department and consequently other resources and capacity appropriately can solve the problem. This includes bed capacity requirement for both critically ill and less critically ill patients. Classification of ED internal sections based on severity of illness instead of medical specialty is another solution.
The authors discuss the current state of the art and future trends in discrete event simulations in light of industrial application and future software development.
The advent of rich Internet applications (RIAs) has evolved into an authentic technological revolution, providing Web information systems with advanced requirements similar to desktop applications. At the same time, RIAs have multiplied the possible architectural and technological options, complicating development and increasing risks. The real challenge is selecting the right alternatives among the existing RIA variability, thus creating an optimal solution to satisfy most user requirements. To face this challenge, the authors' extended the OOH4RIA approach to generative RIA development, which introduces architectural and technological aspects at the design phase and provides a closer match between the modeled system and the final implementation.
Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990's, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This paper reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the paper calls for development of a new generation of discrete event simulation software.
Patient queues are prevalent in healthcare and wait time is one measure of access to care. We illustrate Queueing Theory-an analytical tool that has provided many insights to service providers when designing new service systems and managing existing ones. This established theory helps us to quantify the appropriate service capacity to meet the patient demand, balancing system utilization and the patient's wait time. It considers four key factors that affect the patient's wait time: average patient demand, average service rate and the variation in both. We illustrate four basic insights that will be useful for managers and doctors who manage healthcare delivery systems, at hospital or department level. Two examples from local hospitals are shown where we have used queueing models to estimate the service capacity and analyze the impact of capacity configurations, while considering the inherent variation in healthcare.
The healthcare ecosystem is now in a state of flux. Social, economic pressures beside demographic changes disrupt the balance of the health facilities. According to the Organization for Cooperation and Economic Development (OECD, 2004), “the last thirty years have been a period of change and of expansion for health systems”. Currently, the major problem remains about controlling the ever-increasing health expenditures. Thus, Hospitals are faced with a triple constraint: cost, time and quality. The current ecosystem must simultaneously integrate these constraints in order to offer the best possible service to patient in a minimum time and at the optimal patient’s situation. We focus our interest on patients suffering from heart diseases, but still the global approach of the proposed model valid for other contexts. The proposed model is based on an extreme danger situation consisting of heart attacks. In this paper, we aim to establish an embedded connectivity between heart diseases patients and their physicians. Real time monitoring plays an important role to establish that kind of connectivity. In fact, the proposed simulation model is a Dynamic Stochastic Discrete model realized using ARENA software. All the results presented here are based on random data and can be replaced by real time data extracted and preprocessed using IoT sensors recording, and are referenced to a bi-objective function we are going to present in the following sections.
Operational research embodies a wide range of techniques that can improve the way we plan and organize health services. Operation research (O.R) focuses on the application of analytical methods to facilitate better decision-making. This paper is an attempt to analyze the theory (Queuing) and instances of use of queuing theory in health care organizations around the world and benefits acquired from the same.
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