Optimization of Healthcare Emergency Departments by Agent-Based Simulation

Process Modeling of ABCDE Primary Survey in Trauma Resuscitations: A Crucial First Step for Agent-Based Simulation Modeling of Complex Team-Based Clinical Processes

INTRODUCTION

Trauma teams are ad hoc, multidisciplinary teams that perform complex patient care and medical decision making under dynamic conditions. The ability to measure and thus understand trauma team processes is still limited. Agent-based simulation modeling (ABSM) can be used to investigate complex relationships and performance within a trauma team. However, the foundational work to support such efforts is lacking. The goal of this work is to develop a comprehensive process model for the primary survey in trauma that can support ABSM.

METHODS

A process model for the primary survey of patients with blunt traumatic injuries was developed using Advanced Trauma Life Support guidelines and peer-reviewed publications. This model was then validated using video recordings of 25 trauma resuscitations in a level 1 trauma center. The assessment and treatment pathway followed in each video were mapped against the defined pathway in the process model. Deviations were noted when resuscitations performance did not follow the defined pathway.

RESULTS

Overall the process model contains 106 tasks and 78 decision points across all domains, with the largest number appearing in the circulation domain, followed by airway and breathing. A total of 34 deviations were observed across all 25 videos, and a maximum of 3 deviations were observed per video.

CONCLUSIONS

Overall, our data offered validity support for the blunt trauma primary survey process model. This process model was an important first step for the use of ABSM for the support of trauma care operations and team-based processes.

Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

The awareness of the occurrence of a new disease involves much uncertainty and the search for answers and also appropriate questions. In this paper we focus on the perspective of public health decision-makers. Typically, they would have a standard set of questions and supporting metrics that have been found in previous disease outbreaks to be useful in assessing the effectiveness of various 'solution' methods on the trajectory of the disease. There may be other relevant questions with which such public health domain experts may not be familiar and/or for which they are familiar but are not aware of methods for addressing such questions when there is limited data. Decision Support Systems (DSS) can be used to facilitate the exploration of established questions and some other relevant questions. Given an initial set of questions, the DSS designer should consider which sets of data analytic methods have the capabilities to adequately address. Some of these data analytic methods may also have the capability of addressing questions that could be of interest to the public health decision makers including researchers. In this paper we present a conceptual design for a relevant easy-to-construct DSS and an example of a multi-method DSS that is based on this conceptual design. Using publicly available data on the CoViD-19 pandemic, we illustrate benefits of the multi-method DSS in action.

Resources management impact on neonatal services performance in the United Kingdom: A system dynamics modelling approach

Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results' implications and SD's modelling usefulness to guide decision making in complex health settings are discussed.

Distributed Simulation Using Agents for the Internet of Things and the Factory of the Future

The adoption of the Internet of Things (IoT) and its related technologies has transformed the manufacturing industry and has significantly changed the traditional linear manufacturing supply chains into dynamic and interconnected systems. However, the lack of an approach to assess the economic feasibility and return uncertainties of an IoT system implementation, is blamed as the culprit for hindering its adoption rate. Using two distinctive case studies, this research investigates the use of distributed simulation of agent-based model (ABM) to address such gap in the literature. The first involves the economic feasibility of an IoT implementation in a very large retail warehouse facility, while the second case study proposes a framework able to generate and assess ideal or near-ideal manufacturing configurations and capabilities, and in establishing appropriate information messaging protocols between the various system components by using ABM in distributed simulation.

Analysis of Patient Satisfaction with Emergency Medical Services

Background

This study analyses the degree of patient satisfaction regarding the Emergency Medical Services (EMS) by taking into account the waiting time which is considered to be associated with the success of the EMS organizational model.

Methodology

Between 1 Jan 2016 and 31 March 2016 a cross-sectional research study among visitors of the EMS clinics in the EMS of the Primary Health Services of Gorenjska was performed. The EUROPEP survey was used for rating the degree of patient satisfaction. Statistical methods were utilized to determine the differences among the studied variables, namely the t test, one way ANOVA, as well as post-hoc multiple comparisons, were used.

Results

Nearly all questions associated with the patient survey scored higher than 4.0, indicating patients were generally very satisfied with EMS treatment. Patients were least satisfied with the length of time spent waiting for an examination. The results showed that the waiting time is a statistically significant factor concerning all four dimensions of patient satisfaction: medical staff, clinical facilities, clinical equipment and organization of services (p < 0.05).

Conclusions

Research results have confirmed that the effectiveness of the EMS organizational model impacts on the degree of patient satisfaction. The research also revealed a deficiency in the current EMS organizational services at the prehospital level, given that triage frequently failed to be carried out upon a patient’s arrival at the EMS clinics.

Using the Integration of Discrete Event and Agent-Based Simulation to Enhance Outpatient Service Quality in an Orthopedic Department

Many hospitals are currently paying more attention to patient satisfaction since it is an important service quality index. Many Asian countries' healthcare systems have a mixed-type registration, accepting both walk-in patients and scheduled patients. This complex registration system causes a long patient waiting time in outpatient clinics. Different approaches have been proposed to reduce the waiting time. This study uses the integration of discrete event simulation (DES) and agent-based simulation (ABS) to improve patient waiting time and is the first attempt to apply this approach to solve this key problem faced by orthopedic departments. From the data collected, patient behaviors are modeled and incorporated into a massive agent-based simulation. The proposed approach is an aid for analyzing and modifying orthopedic department processes, allows us to consider far more details, and provides more reliable results. After applying the proposed approach, the total waiting time of the orthopedic department fell from 1246.39 minutes to 847.21 minutes. Thus, using the correct simulation model significantly reduces patient waiting time in an orthopedic department.

Understanding Emergency Care Delivery Through Computer Simulation Modeling

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges.

A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency Department

In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.

Scheduling prioritized patients in emergency department laboratories

This research focuses on scheduling patients in emergency department laboratories according to the priority of patients' treatments, determined by the triage factor. The objective is to minimize the total waiting time of patients in the emergency department laboratories with emphasis on patients with severe conditions. The problem is formulated as a flexible open shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem. Then, the response surface methodology is applied for tuning the GA parameters. The algorithm is tested on a set of real data from an emergency department. Simulation results show that the proposed algorithm can significantly improve the efficiency of the emergency department by reducing the total waiting time of prioritized patients.