Konnichiwa PsyErgo (CHI25, Yokohama)

Im Rahmen der Konferenz präsentierten Tobias Grundgeiger, Stephan Huber und Kilian Bahnsen in vier Vorträgen, zwei Postern und zwei Workshopbeiträgen aktuelle Forschungsarbeiten der PsyErgo, von denen zwei als Honorable Mention ausgezeichnet wurden.

AR Cue Reliability for Interrupted Task Resumption Affects Users' Resumption Strategies and Performance

Reliable augmented reality (AR) cues can support the resumption of interrupted tasks. We investigated how sub-optimal AR cue reliability (100%, 86%, 64%, or no cue) affected users’ resumption performance and strategies. In a between-subjects experiment, 120 participants conducted a physical sorting task including interruptions, and we manipulated AR cue reliability (i.e., the AR cue was present or absent at the end of interruptions). In trials with AR cue, performance with 86% and 64% reliable AR cues was as well as with 100% reliable cues. In trials without AR cue, performance with sub-optimal AR cue reliability declined but was still better than with no cue. Cue reliability affected task resumption strategies of the 86% (slow but no increase in errors) and the 64% (fast but increase in errors) reliability groups differently. Our results extend reliability research to interruptions and the observed efficiency-thoroughness trade-offs in resumption strategies provide insight for design.

Kilian L Bahnsen, Emma Dischinger, and Tobias Grundgeiger. 2025. AR Cue Reliability for Interrupted Task Resumption Affects Users' Resumption Strategies and Performance. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 1224, 1–12. https://doi.org/10.1145/3706598.3713685 [Honorable Mention]

Patient Handover in the Emergency Department Is Not Just a Point Event: Insights for Designing Information Support Tools

Effective information support tools are challenging to design for fast-paced, information rich, and difficult to predict circumstances, particularly when information is fragmented and sources are dispersed. To explore, we conducted a field study on handover and the associated information work, which included 40 visits and 75 hours of observation and interviews with doctors in a metropolitan emergency department (ED). Beyond information exchange, we found that handovers highlight doctors’ proactive approach by anticipating information needs, managing uncertainties arising from dynamic information, and developing patient care plans through multiple contingencies. Expanding on the idea of handover as a multifaceted process rather than a single event, we reinforce existing calls for greater flexibility emphasising that the ascertainment of pertinent information is an ongoing, adaptive process. This work demonstrates that deciding what constitutes relevant information is a priori indeterminate when designing information systems and support tools in environments such as EDs. We propose the preservation of specific ‘relativities’ of information—such as uncertainty, particularity, incompleteness, and temporality—in designing information support tools for dynamic, critical and multi-disciplinary work environments.

Aloha Hufana Ambe, Isaac Salisbury, Tobias Grundgeiger, Daniel Bodnar, Sean Rothwell, Nathan Brown, . . . Ben Matthews. 2025. Patient Handover in the Emergency Department Is Not Just a Point Event: Insights for Designing Information Support Tools. In the Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’25), Yokohama, Japan. https://doi.org/10.1145/3706598.3713756

How a Clinical Decision Support System Changed the Diagnosis Process: Insights from an Experimental Mixed-Method Study in a Full-Scale Anesthesiology Simulation

Recent advancements in artificial intelligence have sparked discussions on how clinical decision-making can be supported. New clinical decision support systems (CDSSs) have been developed and evaluated through workshops and interviews. However, limited research exists on how CDSSs affect decision-making as it unfolds, particularly in settings such as acute care, where decisions are made collaboratively under time pressure and uncertainty. Using a mixed-method study, we explored the impact of a CDSS on decision-making in anesthetic teams during simulated operating room crises. Fourteen anesthetic teams participated in high-fidelity simulations, half using a CDSS prototype for comparative analysis. Qualitative findings from conversation analysis and quantitative results on decision-making efficiency and workload revealed that the CDSS changed team structure, communication, and diagnostic processes. It homogenized decision-making, empowered nursing staff, and introduced friction between analytical and intuitive thinking. We discuss whether these changes are beneficial or detrimental and offer insights to guide future CDSS design.

Sara Wolf, Tobias Grundgeiger, Raphael Zähringer, Lora Shishkova, Franzisca Maas, Christina Dilling, and Oliver Happel. 2025. How a Clinical Decision Support System Changed the Diagnosis Process: Insights from an Experimental Mixed-Method Study in a Full-Scale Anesthesiology Simulation. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 257, 1–23. https://doi.org/10.1145/3706598.3713372 [Honorable Mention]

Participation User Experience: A Call to Better Manage the Most Important Resource in User-Centered Design

Participating users are the foundation of user-centered design. However, there is a limited understanding of their motivation, engagement, and experience participating in research. In this work, we propose the concept of Participation User Experience (PUX), which addresses participants’ experiences in user-centered design. To set a scope for PUX, we conducted a reflexive thematic analysis on workshop data involving 20 experienced user-centered design practitioners and researchers. The analysis yielded five themes, making explicit aspects of PUX that have been implicitly considered and how their consideration could be improved. Great potential lies in addressing intrinsic motivations over extrinsic incentives and developing more structured approaches to planning and measuring PUX to mitigate various sources of bias related to incentives or Experimenter Effects. We contribute to a first understanding of PUX, point to persisting research gaps, and present practical implications for improving participants’ experiences in user-centered design.

Melina Joline Heinisch, Sara Wolf, Franzisca Maas, and Stephan Huber. 2025. Participation User Experience: A Call to Better Manage the Most Important Resource in User-Centered Design. Paper presented at the Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3706599.3719918

Evaluating an AI Documentation Assistant for Anesthesiology Teams

In addition to ensuring patient safety during anesthetic inductions, anesthesiologists must document clinical interventions and administer drugs. This is a time-consuming and low priority task, which harms the documentation quality of anesthetic protocols. In this case study, we demonstrate how speech-based artificial intelligence (AI) assistants that leverage closed-loop communication can increase documentation quality. An evaluation in 40 scenarios in a medical high-fidelity simulator indicated that the AI documentation assistant facilitated earlier data entry and increased documentation precision. However, despite the objective advantages for data quality and patient safety, anesthesiologists experienced a higher temporal demand with the system. With this study, we contribute qualitative insights of how the AI documentation assistant benefited anesthesiologists’ work style and affected their interactions within the team. Future research should aim to design AI assistants that enforce communication clarity while considering their impact on team dynamics.

Stephan Huber, Ronja Fricke, Caroline Pütz, Lennart Baumeister, Christina Dilling, Oliver Happel, Simon Ottenhaus, Anja Nagel, Matthias Dunkelberg, and Tobias Grundgeiger. 2025. Evaluating an AI Documentation Assistant for Anesthesiology Teams. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). Association for Computing Machinery, New York, NY, USA, Article 672, 1–8. https://doi.org/10.1145/3706599.3706658

Team Roles of Artificial Intelligence in Anesthesiology – A Scoping Review

When referring to the role of newly proposed clinical applications of artificial intelligence (AI), recent work inflationary uses the term Human-AI Team. However, the roles foreseen for AI systems within teams remain unclear. We systematically reviewed the literature on AI deployment in anesthesiology and found that most AI system papers only describe algorithms. We identified 57 interactive systems and assigned six roles based on described behavior, tasks, and interactions. While the most prevalent role was task completer, some AI systems also served their team as problem solvers, evaluators, task motivators, or even teamwork support and team leaders. We contribute (1) a classification system for team roles, behaviors, tasks, and interactions of AI team members and (2) an overview of AI systems’ team roles in anesthesiology. We conclude that (3) AI systems’ intended social roles within teams need to be more consciously reflected, shaped and clearly communicated to meet healthcare standards.

Stephan Huber, Lea Weppert, Lennart Baumeister, Oliver Happel, and Tobias Grundgeiger. 2025. Team Roles of Artificial Intelligence in Anesthesiology – A Scoping Review. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). Association for Computing Machinery, New York, NY, USA, Article 508, 1–13. https://doi.org/10.1145/3706599.3720186

Die PsyErgo Workshopbeiträge zum Workshop „ Envisioning the Future of Interactive Health, CHI 2025“ finden Sie hier.