Publications @ CHI 2025

@inproceedings{Rosbach2025WhenTwo,

title = {"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology},

author = {Emely Rosbach (Technische Hochschule Ingolstadt), Jonas Ammeling (Technische Hochschule Ingolstadt), Sebastian Krügel (University of Hohenheim), Angelika Kießig (Katholische Universität Eichstätt), Alexis Fritz (Albert-Ludwigs-Universität Freiburg), Jonathan Ganz (Technische Hochschule Ingolstadt), Chloé Puget (Freie Universität Berlin), Taryn Donovan (Animal Medical Center), Andrea Klang (University of Veterinary Medicine Vienna), Maximilian C. Köller (Medical University of Vienna), Pompei Bolfa (Ross University School of Veterinary Medicine), Marco Tecilla (University of Milan), Daniela Denk (Ludwig-Maximilians-University of Munich), Matti Kiupel (Michigan State University), Georgios Paraschou (Ross University School of Veterinary Medicine), Mun Keong Kok (Faculty of Veterinary Medicine Universiti Putra Malaysia), Alexander F. H. Haake (Freie Universität Berlin), Ronald R. de Krijger (UMC Utrecht, Princess Maxima Center for Pediatric Oncology), Andreas F.-P. Sonnen (UMC Utrecht), Tanit Kasantikul (Michigan State University), Gerry M. Dorrestein (NOIVBD), Rebecca C. Smedley (Michigan State University), Nikolas Stathonikos (UMC Utrecht), Matthias Uhl (University of Hohenheim), Christof A. Bertram (University of Veterinary Medicine Vienna), Andreas Riener (Technische Hochschule Ingolstadt), Marc Aubreville (Flensburg University of Applied Sciences)},

url = {https://hcig.thi.de/, website 

linkedin.com/in/emely-rosbach-0492b1178, linkedin},

year  = {2025},

date = {2025-04-26},

urldate = {2025-04-26},

abstract = {Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, such as confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may worsen when time pressure, ubiquitously present in routine pathology, strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration may fuel confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI use in healthcare and aim to support the safe integration of clinical decision support systems.},

keywords = {Full Paper, Healthcare Assistance, Honorable Mention},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wolf2025HowClinical,

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

author = {Sara Wolf (Julius-Maximilians-Universität Würzburg, Würzburg, Germany), Tobias Grundgeiger (Julius-Maximilians-Universität Würzburg, Würzburg, Germany), Raphael Zähringer (Julius-Maximilians-Universität Würzburg, Würzburg, Germany), Lora Shishkova (Julius-Maximilians-Universität Würzburg, Würzburg, Germany), Franzisca Maas (Julius-Maximilians-Universität Würzburg, Würzburg, Germany), Christina Dilling (Universitätsklinikum Würzburg Würzburg, Germany), and Oliver Happel (Universitätsklinikum Würzburg Würzburg, Germany)},

url = {https://www.mcm.uni-wuerzburg.de/psyergo/, website},

doi = {10.1145/3706598.3713372},

year  = {2025},

date = {2025-04-26},

urldate = {2025-04-26},

abstract = {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 decisionmaking 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.},

keywords = {Full Paper, Healthcare Assistance, Honorable Mention},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ambe2025PatientHandover,

title = {Patient Handover in the Emergency Department Is Not Just a Point Event: Insights for Designing Information Support Tools},

author = {Aloha Hufana Ambe (The University of Queensland Brisbane, Australia), Isaac Salisbury (The University of Queensland Brisbane, Australia), Tobias Grundgeiger (Julius-Maximilians Universität Würzburg Würzburg, Germany), Daniel Bodnar (Royal Brisbane and Women’s Hospital Brisbane, Australia), Sean Rothwell (Royal Brisbane and Women’s Hospital Brisbane, Australia), Nathan Brown (Royal Brisbane and Women’s Hospital Brisbane, Australia), Penelope Sanderson (The University of Queensland Brisbane, Australia), and Ben Matthews (The University of Queensland Brisbane, Australia)},

url = {https://www.mcm.uni-wuerzburg.de/psyergo/, website},

doi = {10.1145/3706598.3713756},

year  = {2025},

date = {2025-04-26},

urldate = {2025-04-26},

abstract = {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.},

keywords = {Full Paper, Healthcare Assistance},

pubstate = {published},

tppubtype = {inproceedings}

}