Ebook: Accident and Emergency Informatics
(A&EI) as a novel subfield became obvious. As in all areas of Biomedical Informatics, A&EI must deal with issues such as relevant data collection, the management of data extracted from accident sites, health records or sensors, wearables and apps, and appropriate data processing, with the dual purpose of preventing harm and decision support.
This book is an introduction to the research and application domain of A&EI, and is the product of three years’ work by the Working Group in A&EI of the International Medical Informatics Association (IMIA). The book presents ten chapters organized in four sections. The first section explores the framework for achieving an emergency-informatics health information infrastructure; the second focuses on the gathering of critical clinical data related to the building up of a smart environment for A&EI; the third introduces state-of-the-art technologies for integration into virtual emergency registries; and the final part considers the delicate issues of patient safety raised by the introduction of surveillance technologies into clinical care, along with other issues presenting challenges to the domain of A&EI for the future.
The book is an important contribution to the field of A&EI, and will be of interest to healthcare professionals, informaticians, and all those who want a better understanding of the domain of Accident and Emergency Informatics.
Preparing and publishing a first book on Accident and Emergency Informatics (A&EI) is not only an important task, but also an enormous accomplishment with a profound impact for the scientific and healthcare community. The International Medical Informatics Association, with its Working Group in A&EI, has worked in a productive and innovative way in this field for the last three years to introduce this research and application domain as a field of great interest; the Informatics in Emergency Medicine and Clinical Care.
This book is the outcome of many discussions, meetings, workshops, and scientific exchanges in the domain, as viewed and experienced by the distinguished editorial team and the expert and renowned authors. The ten chapters in the book are organised in four sections.
The first section is mostly dedicated to the framework for achieving an emergency-informatics health information infrastructure. The second part is focused on the sensory information gathering of critical clinical data related to the building up of a smart environment for A&EI. The third part introduces the state-of-the-art technologies of Virtual technology and the Internet-of-Things for integration into Virtual Emergency Registries. The final part considers the delicate issues of patient safety raised by the introduction of surveillance technologies into clinical care, along with other issues being tackled by the domain of A&EI in facing the challenges of the future. In addition, the final part discusses the principles and methods for evaluating A&EI applications; a critical challenge for gaining the acceptance of healthcare professionals.
It is clear that such a book, prepared as a concise edited volume and introducing a new domain such as A&EI for the first time faces a unique challenge to be up to date and to the point, to define principles, to formalise specific methodologies, to describe applications, to provide methods for evaluation and to see and foresee future challenges and suggest solutions. All these aspects have been considered and tackled in an excellent and masterful way in this book, which is an important contribution to the field of A&EI, making it very appropriate and suitable for recommendation to Healthcare Professionals and Informaticians wishing to understand and fathom the domain of Accident and Emergency Informatics.
Athens, 25 Jan 2022
Emergency care is one of the cornerstone parts of the world health organization’s action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these requirements through automated emergency technology. Machine learning (ML) is one of the main parts of some of these proposed technologies. There are various ML algorithms and techniques which are potentially applicable for different purposes of emergency care. AI-based approaches using classification and clustering algorithms, natural language processing, and text mining are some of the possible techniques that could prove useful for investigating models of emergency prevention and management and proposing improved procedures for handling such critical situations. ML is known as a field of AI which attempts to automatically learn from data and applies that learning to make better decisions. Decision-support tools can apply the results of either supervised or various semi-supervised or unsupervised learning methods to tackle the how decisions about emergency situations are typically handled by the best professionals at the scene of an emergency, in the pre-hospital, and in healthcare facility settings. Enhanced and rapid communication at the moment of emergency, with the most effective decision making for triaging to estimate the acute nature of injuries and possible complications, how to keep a patient stable on the way to the care facility, and also avoiding adverse drug reactions, are some of the possible directions for exploring how ML can help to gather the data and to make emergency management more efficient and effective. The wide range of scenarios present in emergency situations and the complexity of different legal and ethical constraints on what responding personnel are allowed to perform on an injured subject before reaching a hospital makes for a most challenging set of problems for investigating the components of “intelligent” decision support that could help in these highly interactive and humanly tragic situations.
The 21st century has seen an enormous growth in emergency medical services (EMS) information technology systems, with corresponding accumulation of large volumes of data. Despite this growth, integration efforts between EMS-based systems and electronic health records, and public-sector databases have been limited due to inconsistent data structure, data missingness, and policy and regulatory obstacles. Efforts to integrate EMS systems have benefited from the evolving science of entity resolution and record linkage. In this chapter, we present the history and fundamentals of record linkage techniques, an overview of past uses of this technology in EMS, and a look into the future of record linkage techniques for integrating EMS data systems including the use of machine learning-based techniques.
A significant number of problems in emergency care are caused by a lack of provider access to pre-existing patient information at the point of care. Medical Emergency Datasets (MEDs) are brief summarizations of an individual’s medical history, providing vital patient information to emergency medical providers. The German MED was validated by German physicians and – based on an international research project – also by Canadian physicians. Physicians in both countries considered the content very useful. The MED is currently being introduced in Germany as part of the Telematic Infrastructure. At the same time, the COVID pandemic forced healthcare professionals around the work to optimize the digital information exchange among different healthcare providers. While the exchange of data is important, additional personal expert advice is sometimes vital. Real time virtual support systems (RTVS) were introduced in Germany and Canada to support team-based healthcare delivery, independent of the actual location. Such systems have been implemented for intensive care, emergency medicine, primary care and several other medical specialties. These systems serve as a safety net, a funnel (appropriate utilization; linking patients back to primary care networks – thus reducing fragmented or disrupted services) and a medical network by building interprofessional relationships.
The rapid development of elderly population is changing demographics in Europe and North America and imposes barriers to healthcare systems that may reduce the quality of service. Telemedicine is a potential solution supporting the real-time and remote monitoring of subjects as well as bidirectional communication with medical personnel for care delivery at the point of perception. Smart homes are private spaces where young or elderly, healthy or diseased-suffering, or disabled individuals spend the majority of their time. Hence, turning smart homes into diagnostic spaces for continuous, real-time, and unobtrusive health monitoring allows disease prediction and prevention before the subject perceives any symptoms. According to the World Health Organization, health, well-being, and quality of life assessment require the monitoring of interwoven domains such as environmental, behavioral, physiological, and psychological. In this work, we give an overview on sensing devices and technologies utilized in smart homes, which can turn the home into a diagnostic space. We consider the integration of sensing devices from all four WHO domains with respect to raw and processed data, transmission, and synchronization. We apply the bus-based scalable intelligent system to construct a hybrid topology for hierarchical multi-layer data fusion. This enables event detection and alerting for short-time as well as prediction and prevention for long-time monitoring.
Chemical, analytical and biological laboratories use a variety of different solvents and gases. Many of these compounds are harmful or even toxic to laboratory personnel. Permanent monitoring of the air quality is therefore of great importance regarding the greatest possible occupational safety and the detection of dangerous situations in the work process. An increasing need exists for the development and application of small and portable sensor solutions that enable personal monitoring and that can be flexibly adapted to different environments and situations. Different sensor principles are available for the detection of gases and solvent vapors, which differ in terms of their selectivity and sensitivity. Besides simple sensing elements, integrated sensors and smart sensors are increasingly available, which, depending on their scope of functions, require a distinct effort in integration. This chapter gives an overview of available sensors and their integration options, and describes ready-to-use sensor systems for personal monitoring in life science laboratories.
Early Warning Scores (EWSs) systems support the timely detection of patient deterioration and rapid response of the care team. Due to the mobility nature of healthcare settings, there has been a growing tendency to use mobile-based devices in these settings. This chapter aimed to design a mobile-based EWS application (app). This was a descriptive study to design the architecture of the proposed EWS app. The design of architecture was done using the Unified Modeling Language diagrams including a class diagram, use-case diagram, and activity diagram. We evaluated the architecture using the ARID scenario-based evaluation method. The proposed EWS application (app) was the integration of three EWSs, including NEWS2, PEWS, and MEOWS. The workflow of these EWSs systems was designed and integrated into a single app. Also, the static structure of the proposed EWS app was designed by class diagram and the behavioral structure was depicted by use-case and activity diagrams. The class diagram showed the system components and their relationships. However, the use-case diagram displayed the app’s interaction with its environment, and the activity diagram illustrated how the EWS app processes were carried out. Evaluation results showed the possibility of designing the architecture for the proposed EWS app. In our app, the EWSs were designed in the clinician’s workflow, and it was integrated with the patient’s Electronic Health Record (EHR). These factors may lead to more use of EWSs. Considering the frequency of alerts represented to clinicians and the user-friendly design of the app, some suggestions can be considered by EWS systems developers in the future.
Social Media and the Internet of Things are nowadays full and strong components of day-to-day life worldwide. Both allow communicating with others 24 hours a day, 7 days a week without distance limitations. During the last decade, on-site citizens have shared disaster-related first reports on social media. Official institutions are using the same framework for delivering up-to-date and follow-up directives. Moreover, monitoring health risks, patients, and systems behavior in real-time over the Internet-of-Things allows detecting different levels of anomalies that might lead to critical events that need to be managed as an emergency. Emergency and disaster medicines deal with broad and complex medical, surgical, mental health, epidemiological, managerial, and communicational issues. Social Media platforms and the Internet of Things are technologies that increase cyber-physical interactions between individuals, machines, and their environment. The generated data over time are massive and are supporting the emergency or disaster mitigation process. This chapter deals with, in the first section, the social media platforms, and the Internet of Things. Then, at a second one, the concepts of emergency, disaster medicine and management are discussed. In the following two sections, we discuss applications and usages of social media and IoT technologies for improving the management (preparedness, response, recovery, mitigation) of emergencies and disasters as fundamental keys and pillars for efficiently handling the managerial information flow in emergency and disaster contexts.
The prediction of the demography of Spain shows that Spain will experience an aging population soon. Aging is a condition of chronic disease resulting in overcrowding Emergency Department. Despite chronic diseases, Covid-19 became a serious issue for emergency Department staff and health care providers. All of these matters emphasized the importance of the Virtual Emergency Department which can provide faster and more affordable medical services while everyone can keep the social distance as much as possible. In this chapter, we investigated the role of IT in the healthcare system and the possible suggested solutions. We have studied the existing telemedicine, e-health, machine learning algorithms and in the end, their combination to built an integrated virtual emergency department to cover all the aspects. We have proposed a model for this integrated model and studied the possibility of success in each step including admission, triage, diagnoses, and clinical advice based on literature.
The World Health Organization (WHO) announced the first-ever World Patient Safety Day on September 17, 2019, which remarks a global campaign to create an awareness of patient safety and urges people to show their commitment to making healthcare safer. Reporting medical incidents or patient safety events (PSE) has been recommended as an effective approach for the detection of patterns, discovery of underlying factors, and generation of solutions. It is believed that PSE reporting systems (e-reporting) could be a good resource to share and to learn from the reporting if the event data are collected in a properly structured format. Unfortunately, the prevalence of underreporting and low quality of the reports have become barriers to ultimately achieve the goal of preventing and reducing medical incidents. This chapter describes the efforts that have been made to improve e-reporting through informatics approaches, including a review of PSE taxonomies and conceptual frameworks, studies of medication events, patient falls, and PSE involved in health information technologies, as well as discussions of design requirements for future e-reporting systems.
Accident and emergency informatics has become a new approach to accident research in the era of digitization, where it has become realistic to integrate data recorded at accident sites with data in electronic health records of patients. This chapter deals with the question on whether the existing and well-established evaluation methodologies used in accident-centered research as well as in patient-centered research within clinical medicine are sufficient and should also be used for such integrated data or whether they have to be modified or extended. Based on the Gaus-Muche-Nomenclature on studies in clinical medicine, it will be outlined which types of studies are appropriate here. In addition to observational studies and registers, controlled trials using randomization are also be regarded as an important approach for gaining new knowledge. In order to appropriately access data from health records and from accidents, standards for representing and communicating data for such studies will be of importance. Another criterion is referential integrity. Here and with respect to accidents the International Standard Accident Number (ISAN) may be of importance.