Chair (2021-2024)

Thomas M. Deserno, Ph.D.
Professor, Department of Computer Science, TU Braunschweig
Adjunct Faculty, Hannover Medical School
Braunschweig, Germany

Co-Chair (2022-2025)

Dr. Mostafa Haghi
Senior researcher, Ubiquitous Computing Lab,
HTWG Konstanz,Department of Computer Science,
Konstanz, Germany

IMIA A&EI Book Published

Studies in Health Technology and Informatics

291, 2022
Thomas M. Deserno, Mostafa Haghi, Najeeb Al-Shorbaji
978-1-64368-274-7 (print) | 978-1-64368-275-4 (online)

Description

(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.

https://ebooks.iospress.nl/ISBN/978-1-64368-275-4

IMIA A&EI WG General Aim

Accident & emergency informatics (A&EI) is a novel and trans-disciplinary science of systematic collecting and managing medical data (e.g., electronic health records) as well as sensor data from the human environment (e.g., event data recorder such as acceleration sensors in the vehicle), their syntactic and semantic integration and their analytics, in order to forecast, prevent, or lower the impact of such events on the subject.

 Therefore, the core mission of A&EI is saving lives – on the one hand by combining and jointly analyzing medical and non-medical data, and on the other hand by involving decision-makers, actors, and stakeholders from politics, infrastructure and health management, and industry.

Focus of Research

A&EI research is focused on the conception (syntactic and semantic interoperability), implementation (at least on the prototype level), and operation (at least as a field experiment) of sensor-enriched medical information systems. Our research is addressing the following tasks:

  • Data collection: Collecting sensor-based event data that is related to accidents and medical emergencies must be captured in real-time. Relevant data can be recorded with fixed sensors in the living space, in a vehicle, or with sensors worn on or even inside the human body. Today’s vehicles are already equipped with a variety of sensors (e.g., temperature, rain, global position, seat belt status, airbags, breaks, and other assistance systems) but other living spaces (e.g., homes) need enrichment with according sensors.
  • Data management: Accident and emergency data have to be stored in appropriate registries. The design and implementation of such data repositories providing syntactic and semantic interoperability as well as satisfying data privacy and security requirements are important requirements and considered as the key expertise of PLRI.
  • Data integration: Linking event-related data to medical data will yield increased knowledge of accidents and emergencies. Location and time-based identifiers are needed to bridge the different registries. Such automatically generated identifiers need appropriate hashing and integration in existing data systems.
  • Data analytics: In order to predict accident and emergency situations, to take a specific action, or to lower their impact on humans, the merged data have to be analyzed. Therefore, A&EI involves investigating accidents and emergencies through state-of-the-art methods of data science and analytics targeting towards automatic alarming systems that actually save lives.

 

Further information please see: https://aei.plri.de/

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