Engaging Multidisciplinary clinical users in the design of an artificial intelligence-powered graphical user interface for intensive care unit instability decision support
Open accessAbstract
Background Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. Objectives Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. Methods Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. Results Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. Conclusion Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.
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Health professionals described XAI systems as useful for supporting clinical decision making.
“Health professionals also described XAI systems as useful for supporting clinical decision making [29,31,33,35–37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Participants commonly reported that XAI tools support task completion.
“Across these studies participants commonly reported that the XAI tools support task completion [23,26,28,29,31,33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Eight studies linked perceived usefulness to the tool's impact on workload.
“Eight studies linked perceived usefulness to the tool’s impact on workload [26,29–31,33,35,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
The theme perceived usefulness was identified in 13 papers.
“The theme perceived usefulness was identified in 13 papers, where participants reported the how the systems impacted their work and associated tasks [23,24,26,28–33,35–38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Integration with electronic health records was common, particularly in more mature tools.
“Integration with electronic health records (EHRs) was common, particularly in more mature tools [23,24,26–34,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Education and training was a theme that arose in 9 studies.
“Education and training was a theme that arose in 9 studies, where participants described their need for, or experience of, learning how to use the XAI system [23,25,26,28–30,32,35,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Perceptions on the model's performance were reported in 13 studies.
“Perceptions on the model’s performance was reported in 13 studies [23–26,29–37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Perceptions regarding efficiency or saving time were reported in six studies.
“Perceptions regarding efficiency or saving time were reported in six studies [23,28–30,32,33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Helman et al. reported it took a significant amount of time to explain how to use their XAI tool, which was viewed as a barrier to use in practice.
“Helman et al. [29] reported it took a significant amount of time to explain how to use their XAI tool, which was viewed as a barrier to use in practice.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Participants had concerns with performance regarding unique patients believed to be outliers.
“Participants also had concerns with performance regarding unique patients believed to be outliers [25,26,29,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Five studies included perceptions related to health professionals' autonomy.
“Five studies included perceptions related to health professionals’ autonomy [26,29,30,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Participants in included studies raised concerns that XAI systems could lead to over-reliance and reduce critical thinking skills.
“Participants also raised concerns that the XAI system could lead to over-reliance and reduce critical thinking skills [29,30,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Several studies reported trust and acceptance of model outputs were facilitated by providing evidence for the model effectiveness on patients and/or evidence of the output.
“Several studies reported trust and acceptance of model outputs were facilitated by providing evidence for the model effectiveness on patients and/or evidence of the output [23,29,30,33,35,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
XAI model predictions were reportedly more likely to be accepted when they aligned with health professionals' knowledge or with the patient context.
“XAI model predictions were reportedly more likely to be accepted when they aligned with health professionals’ knowledge, or with the patient context [23,25,27–30,32–36]”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Perceptions of the XAI model's data quality impacted participants' trust and use of tools, and trust in outputs was reduced when XAI systems used hospital data that participants knew was unreliable.
“Similarly, perceptions of the XAI model’s data quality impacted participants trust and use of tools and trust in outputs were reduced when XAI system’s used hospital data that participants knew was unreliable [23,25,26,29,32,33,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
Health professionals' perceptions involving design and usability were found in 14 papers.
“Health professionals’ perceptions involving design and usability were found in 14 papers [23–34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [29]
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Van Dort, B. A., Engelsma, T., Sneekes, P., Peute, L., & Medlock, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001417 · Cited by 9 · see citing works
- Authors
- Helman, S., Terry, M. A., Pellathy, T., Hravnak, M., George, E., Al-Zaiti, S., & Clermont, G.
- Affiliations
- University of Pittsburgh (US)
- Center for Healthcare Evaluation, Research, and Promotion (US)
- UPMC Presbyterian (US)
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- ORCID
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2023-08
- Volume
- 14
- Issue
- 04
- Pages
- 789-802
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC10550364
- OpenAlex
- W4387347612
- Cited by
- 20
- References
- 6
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 4
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Clinical Informatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health
- National Institute of Nursing Research (F31NR019725)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- clinical decision support, artificial intelligence, graphical user interface design, intensive care unit, nurses' perceptions, physician attitudes, physician assistants, nurse practitioners, interpretability, clinical workflow, trust in AI, display location
- MeSH terms
- Artificial Intelligence, Decision Making, Humans, Intensive Care Units, Focus Groups, Decision Support Systems, Clinical
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