Use of an agent-based simulation model to evaluate a mobile-based system for supporting emergency evacuation decision making

Developing Public Health Emergency Response Leaders in Incident Management: A Scoping Review of Educational Interventions

During emergency responses, public health leaders frequently serve in incident management roles that differ from their routine job functions. Leaders' familiarity with incident management principles and functions can influence response outcomes. Therefore, training and exercises in incident management are often required for public health leaders. To describe existing methods of incident management training and exercises in the literature, we queried 6 English language databases and found 786 relevant articles. Five themes emerged: (1) experiential learning as an established approach to foster engaging and interactive learning environments and optimize training design; (2) technology-aided decision support tools are increasingly common for crisis decision-making; (3) integration of leadership training in the education continuum is needed for developing public health response leaders; (4) equal emphasis on competency and character is needed for developing capable and adaptable leaders; and (5) consistent evaluation methodologies and metrics are needed to assess the effectiveness of educational interventions.These findings offer important strategic and practical considerations for improving the design and delivery of educational interventions to develop public health emergency response leaders. This review and ongoing real-world events could facilitate further exploration of current practices, emerging trends, and challenges for continuous improvements in developing public health emergency response leaders.

Promotion of prehospital emergency care through clinical decision support systems: opportunities and challenges

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included "clinical decision support system," "decision support system," "decision support tools," "prehospital care," and "emergency medical services." Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.

The Use of Smartphone-Based Triage to Reduce the Rate of Outpatient Error Registration: Cross-Sectional Study

BACKGROUND

In many clinics, patients now have the option to make Web-based appointments but doing so according to their own judgment may lead to wrong registration and delayed medical services. We hypothesized that smartphone-based triage in outpatient services is superior to Web-based self-appointment registration guided by the medical staff.

OBJECTIVE

This study aimed to investigate smartphone-based triage in outpatient services compared with Web-based self-appointment registration and to provide a reference for improving outpatient care under appointment registration.

METHODS

The following parameters in Guangzhou Women and Children's Medical Center were analyzed: wrong registration rate, the degree of patient satisfaction, outpatient visits 6 months before and after smartphone-based triage, queries after smartphone-based triage, number of successful registrations, inquiry content, and top 10 recommended diseases and top 10 recommended departments after queries.

RESULTS

Smartphone-based triage showed significant effects on average daily queries, which accounted for 16.15% (1956/12,112) to 29.46% (3643/12,366) of daily outpatient visits. The average daily successful registration after queries accounted for 56.14% (1101/1961) to 60.92% (1437/2359) of daily queries and 9.33% (1130/12,112) to 16.83% (2081/12,366) of daily outpatient visits. The wrong registration rate after smartphone-based triage was reduced from 0.68% (12,810/1,895,829) to 0.12% (2379/2,017,921) (P<.001), and the degree of patient satisfaction was improved. Monthly outpatient visits were increased by 0.98% (3192/325,710) to 13.09% (42,939/328,032) compared with the same period the preceding year (P=.02).

CONCLUSIONS

Smartphone-based triage significantly reduces the wrong registration rate caused by patient Web-based appointment registration and improves the degree of patient satisfaction. Thus, it is worth promoting.

A systematic literature review of criteria and models for casualty distribution in trauma related mass casualty incidents

INTRODUCTION

Mass casualty incidents impose a large burden on the emergency medical systems, hospitals and community infrastructures. The pre-hospital and hospital capacities are usually bear the burden of casualties large numbers. One of the challenging issues in mass casualty incidents is the distribution of casualties among the suitable health care facilities.

OBJECTIVE

To review models and criteria affecting the distribution of casualties during the trauma-related mass causality incidents.

MATERIALS AND METHODS

A systematic literature search in the scientific databases which included: PubMed, Scopus and Web of Science was conducted. Relevant literature which was published before August 2017 was searched. Neither the publication date nor language limitations were considered in the literature search. All the trauma-related mass casualty incidents are included in this study. Two independent reviewers conducted the data extraction and quality assessment of the documents was considered using a checklist developed by the researchers.

RESULTS

Literature search yielded 4540 documents of which 493 were duplicated and removed. After reviewing the titles and abstracts of the remaining documents (4047), only 73 documents were considered relevant. Finally, the inclusion and exclusion criteria were applied and only 30 documents were considered for data extraction and quality assessment. The study found 491 criteria to be affecting the distribution of casualties following trauma-related mass casualty incidents. These are categorized as pre-hospital (triage, treatment and transport); hospital (space, staff, stuff, system / structure); incidents' characteristics and others. The criteria which were extracted from the models are termed as "model extracted" while the other labeled as "author suggested".

CONCLUSION

To the best of our knowledge, this is the first systematic literature review on criteria affecting distribution of casualties following trauma-related mass casualty incidents based on the pre-hospital and hospital capacities.

SYSTEMATIC REVIEW REGISTRATION NUMBER

This review was registered in international prospective register of systematic reviews (PROSPERO) with registration number CRD42016049115.

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.

SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents

It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. No experimental studies regarding the effects of DMR interventions on the health outcomes of disaster survivors have been carried out. Traditional analytic methods cannot fully capture the flow of disaster victims through a complex disaster medical response system (DMRS). Computer modelling and simulation enable to study and test operational assumptions in a virtual but controlled experimental environment. The SIMEDIS (Simulation for the assessment and optimization of medical disaster management) simulation model consists of 3 interacting components: the victim creation model, the victim monitoring model where the health state of each victim is monitored and adapted to the evolving clinical conditions of the victims, and the medical response model, where the victims interact with the environment and the resources at the disposal of the healthcare responders. Since the main aim of the DMR is to minimize as much as possible the mortality and morbidity of the survivors, we designed a victim-centred model in which the casualties pass through the different components and processes of a DMRS. The specificity of the SIMEDIS simulation model is the fact that the victim entities evolve in parallel through both the victim monitoring model and the medical response model. The interaction between both models is ensured through a time or medical intervention trigger. At each service point, a triage is performed together with a decision on the disposition of the victims regarding treatment and/or evacuation based on a priority code assigned to the victim and on the availability of resources at the service point. The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.

Design and development of a medical big data processing system based on Hadoop

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics--Volume, Variety, Velocity and Value (the 4 Vs)--that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations.