TAKAI Team‐Arbeit‐Kontext‐Analyse Inventar

Das Team‐Arbeit‐Kontext‐Analyse Inventar nach Hagemann et al. dient zur Erhebung des Arbeitskontextes von High-Responsibility-Teams.The Team Work Context Analysis Inventory (Hagemann et al.) is designed to assess the work context of high responsibility teams

Towards Human-AI Interaction in Medical Emergency Call Handling

Call-takers in emergency medical dispatch centers typically rely on decision-support systems that help to structure emergency call dialogues and propose appropriate responses. Current research investigates whether such systems should follow a hybrid intelligent approach, which requires their extension with interfaces and mechanisms to enable an interaction between call-takers and artificial intelligence (AI). Yet unclear is how these interfaces and mechanisms should be designed to foster call handling performances while making efficient use of call-taker's often strained mental capacities. This paper moves towards closing this gap by 1) deriving required artifacts for human-AI interaction and 2) proposing an iterative procedure for their design and evaluation. For 1), we apply the guidelines for human-AI interaction and conduct workshops with domain experts. For 2), we argue that performing a full evaluation of the artifacts is too extensive at earlier iterations of the design process, and therefore propose to enact use-case-driven lightweight evaluations instead

Technology commitment of high responsibility teams

The aim of the present study is to survey the technology commitment of dispatchers in fire and emergency centers, physicians and EMS professionals, and to develop a short scale for specific Technology Commitment with regard to intelligent systems, as they are to be developed and implemented in the SPELL project.The study was conducted as part of the SPELL project.Questionnaire, data sheet, analysis (code

Multi-professional evaluation of the training of paramedic trainees and participants of the advanced training in emergency care in extended reality

The research data refer to a formative project evaluation in the BMBF-funded project ViTAWiN.In addition to socio-demographic data from 41 participants, presence (Igroup Presence Questionnaire, Schubert et al.), simulator sickness (Sim. Sickn. Quest., Kennedy), situational motivation (Situational Motivation Scale, Guay) and usability (System Usability Scale, Brook) are recorded.It is a raw data file in CSV format that can be opened by the common evaluation programmes

Psychometrische und soziodemografische Rohdaten der Trainingsevaluation von Notfallsanitäter:innen in Ausbildung mit Extended Reality im Classroom-Setting innerhalb des Projekts ViTAWiN

The raw data includes socio-demographic information about the participants as well as results of the measurement of situational motivation, usability, virtual reality sickness, and presence.The ViTAWiN project is funded by the German Federal Ministry of Education and Research (BMBF).The CSV file can be opened with all statistics programs or with a spreadsheet program

Exposé of the field study in the research project SPELL

Um Transparenz und Reproduzierbarkeit zu ermöglichen, wird der Forschungsplan zur Feldforschung im Projekt veröffentlicht. Die Feldforschung adressiert Aspekte der Aufmerksamkeitsverteilung und des Mental Workloads während der Prozesse "Notrufannahme" und "Einsatzdisposition".PDF-Fil

Interprofessional evaluation of the training of paramedic trainees and participants of the advanced training in emergency care in extended reality

The research data refer to a formative project evaluation in the BMBF-funded project ViTAWiN. In addition to socio-demographic data, presence (Igroup Presence Questionnaire, Schubert et al.), simulator sickness (Sim. Sickn. Quest. Kennedy), task and cognitive load (NASA TLX, Hart & Staveland), situational motivation (Situational Motivation Scale), Training Evaluation (Training Evaluation Inventory, Ritzmann et al.) and usability (System Usability Scale according to Brooke) are recorded.Included are two files in CSV format that can be opened by the usual evaluation programmes

Attitude of Emergency Dispatchers Towards Artificial Intelligence – A Black Box of Expectations

Introduction: AI is transforming various industries, especially healthcare and emergency services. For example, AI helps with clinical decision support, detects cardiac arrest and stroke during calls, and manages text-to-speech translation. On the human-centered side, the societal and personal impacts of AI and other technologies are significant but under-researched. Therefore, this study examines the belief systems of emergency dispatchers regarding AI applications. Methods: From September 2021 to September 2023, eight extensive interviews were conducted with a total of 31 individuals, lasting over 619 minutes. Following grounded theory, the interview guide was iteratively adapted to support theory development. Results: The interviews revealed a high level of commitment to their profession and a strong appreciation and interest in research. While many issues within public safety and answering points (PSAPs) and the healthcare system were identified, few concrete ideas for AI-based solutions were mentioned. In addition to the common assumption of high mental workload in emergency call centers and the need for AI systems to be understandable, there are notable differences in the belief systems of dispatchers and other experts. These differences often lead to a more negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications. However, the ability to reflect can mitigate these limitations. AI can support dispatchers who have to handle complex tasks under time pressure, information deficits and uncertainty. Conclusion: In addition to the assumption of high mental workload and the need for understandable AI systems, dispatchers and other experts have different belief systems. These can lead to a negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications, although reflection can help to mitigate this. AI can support dispatchers to handle complex tasks under pressure, information deficits and uncertainty. To prevent rejection of AI and raise awareness of its opportunities and risks, a comprehensive package of measures such as the one we have introduced is needed

The reasons for calls in medical emergencies: Development of a structured semantic model based on a randomised sample of medical assistance calls from an integrated rescue coordination centre

Rettungsleitstellen sehen sich mit steigenden Herausforderungen durch kontinuierlich steigende Notrufzahlen konfrontiert. Zur besseren Strukturierung und Priorisierung der Notrufgespräche werden vielerorts standardisierte Abfragesysteme implementiert. Aktuelle Entwicklungen im Bereich der künstlichen Intelligenz eröffnen neue Möglichkeiten der Entscheidungsunterstützung von Disponierenden. Voraussetzung hierfür ist ein prozesshaftes Modell des Notrufdialogs. Anbei finden sich das Zusatzmaterial zur Forschung.Die Daten wurden im Rahmen des öffentlich geförderten Projekts SPELL erhoben