Integration of artificial intelligence in clinical practice – Insights from a modified Delphi study with human factors experts and healthcare professionals
4 affiliations
- 1Institute for Patient Safety, University Hospital Bonn, Germany; Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany
- 2Institute for Patient Safety, University Hospital Bonn, Germany
- 3Institute for Patient Safety, University Hospital Bonn, Germany; Corresponding author at: Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1 Building A 02, 53127 Bonn, Germany.
- 4Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, US
Abstract
Human Factors and Ergonomics (HF/E) offers essential expertise for understanding and optimizing interactions between humans, technology, and clinical work systems. Despite the growing utilization of Artificial Intelligence (AI) in healthcare, there is a lack of research on its real-world implementation and impact on patient safety, as well as a gap in the inclusion of HF/E considerations in the development, regulation, and implementation of AI. This study aimed to explore HF/E considerations for AI integration in healthcare as well as key risks and opportunities for patient safety related to AI use. This exploratory study used a modified Delphi approach, including semi-structured interviews with 10 HF/E experts across different nationalities, followed up with an online survey involving 37 participants, encompassing HF/E and healthcare professionals. This process resulted in a collection of 22 recommendations, emphasizing human-centered design, early and continuous stakeholder involvement, and ongoing system monitoring. In addition, the study identifies risks and opportunities for patient safety associated with AI implementation, highlighting areas that warrant further research and careful attention in future applications. As the study examined AI broadly to identify cross-cutting principles, this scope may have limited the depth of technology-specific insights. Nevertheless, the findings reinforce the importance of HF/E principles and demonstrate their relevance for guiding the integration of emerging AI technologies in healthcare. As AI capabilities continue to advance, integrating HF/E throughout the entire AI lifecycle will remain essential to ensuring its safe, effective, and sustainable use in healthcare.
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BibTeX
@article{wenderott2026integration,
title = {{Integration of artificial intelligence in clinical practice – Insights from a modified Delphi study with human factors experts and healthcare professionals}},
author = {Katharina Wenderott and Jim Krups and Matthias Weigl and Abigail R. Wooldridge},
journal = {Human Factors in Healthcare},
year = {2026},
month = oct,
doi = {10.1016/j.hfh.2026.100161},
url = {https://doi.org/10.1016/j.hfh.2026.100161},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integration of artificial intelligence in clinical practice – Insights from a modified Delphi study with human factors experts and healthcare professionals
AU - Katharina Wenderott
AU - Jim Krups
AU - Matthias Weigl
AU - Abigail R. Wooldridge
PY - 2026
DA - 2026/10/30
JO - Human Factors in Healthcare
DO - 10.1016/j.hfh.2026.100161
UR - https://doi.org/10.1016/j.hfh.2026.100161
AB - Human Factors and Ergonomics (HF/E) offers essential expertise for understanding and optimizing interactions between humans, technology, and clinical work systems. Despite the growing utilization of Artificial Intelligence (AI) in healthcare, there is a lack of research on its real-world implementation and impact on patient safety, as well as a gap in the inclusion of HF/E considerations in the development, regulation, and implementation of AI. This study aimed to explore HF/E considerations for AI integration in healthcare as well as key risks and opportunities for patient safety related to AI use. This exploratory study used a modified Delphi approach, including semi-structured interviews with 10 HF/E experts across different nationalities, followed up with an online survey involving 37 participants, encompassing HF/E and healthcare professionals. This process resulted in a collection of 22 recommendations, emphasizing human-centered design, early and continuous stakeholder involvement, and ongoing system monitoring. In addition, the study identifies risks and opportunities for patient safety associated with AI implementation, highlighting areas that warrant further research and careful attention in future applications. As the study examined AI broadly to identify cross-cutting principles, this scope may have limited the depth of technology-specific insights. Nevertheless, the findings reinforce the importance of HF/E principles and demonstrate their relevance for guiding the integration of emerging AI technologies in healthcare. As AI capabilities continue to advance, integrating HF/E throughout the entire AI lifecycle will remain essential to ensuring its safe, effective, and sustainable use in healthcare.
SN - 2772-5014
ER -