Cited by 20Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsHealthcare Technology and Patient Monitoring

Engaging Multidisciplinary clinical users in the design of an artificial intelligence-powered graphical user interface for intensive care unit instability decision support

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Helman, Stephanie, Terry, Martha Ann, Pellathy, Tiffany, Hravnak, Marilyn, George, Elisabeth, Al-Zaiti, Salah, Clermont, Gilles·2023·Applied Clinical Informatics

Abstract

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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Registry

Crossref · 10.1055/s-0043-1775565

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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
Venue
Applied Clinical Informatics
Published
2023-08
Volume
14
Issue
04
Pages
789-802
ISSN
1869-0327 (ISSN-L)
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
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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