ArticleInternational journal of medical informatics2022
Engaging clinicians early during the development of a graphical user display of an intelligent alerting system at the bedside.
Article in International journal of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Nurses' Experiences Using AI in Clinical Practice: Systematic Review.JMIR nursing · 2026Pooled it
- Participatory Design of AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements.Mayo Clinic proceedings. Digital health · 2025Article
- Perspectives of family medicine residents on artificial intelligence for survival estimation in patients with serious illness.PLOS digital health · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- "Be Really Careful about That": Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection.Applied clinical informatics · 2025Article
- Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.Applied clinical informatics · 2023Article
- Increasing acceptance of medical AI: The role of medical staff participation in AI development.International journal of medical informatics · 2023Article
- Article
- Must-have Qualities of Clinical Research on Artificial Intelligence and Machine LearningBalkan medical journal · 2023Review
- A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML).European heart journal. Digital health · 2022Article
- Intelligent Clinical Decision Support.Sensors (Basel, Switzerland) · 2022Article
- Implementation of KIDMATCH: A Clinical Decision Support Tool for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundArtificial Intelligence (AI) is increasingly used to support bedside clinical decisions, but information must be presented in usable ways within workflow. Graphical User Interfaces (GUI) are front-facing presentations for communicating AI outputs, but clinicians are not routinely invited to participate in their design, hindering AI solution potential. PURPOSE: To inform early user-engaged design of a GUI prototype aimed at predicting future Cardiorespiratory Insufficiency (CRI) by exploring clinician methods for identifying at-risk patients, previous experience with implementing new technologies into clinical workflow, and user perspectives on GUI screen changes.
methodsWe conducted a qualitative focus group study to elicit iterative design feedback from clinical end-users on an early GUI prototype display. Five online focus group sessions were held, each moderated by an expert focus group methodologist. Iterative design changes were made sequentially, and the updated GUI display was presented to the next group of participants.
results23 clinicians were recruited (14 nurses, 4 nurse practitioners, 5 physicians; median participant age ∼35 years; 60% female; median clinical experience 8 years). Five themes emerged from thematic content analysis: trend evolution, context (risk evolution relative to vital signs and interventions), evaluation/interpretation/explanation (sub theme: continuity of evaluation), clinician intuition, and clinical operations. Based on these themes, GUI display changes were made. For example, color and scale adjustments, integration of clinical information, and threshold personalization.
conclusionsEarly user-engaged design was useful in adjusting GUI presentation of AI output. Next steps involve clinical testing and further design modification of the AI output to optimally facilitate clinician surveillance and decisions. Clinicians should be involved early and often in clinical decision support design to optimize efficacy of AI tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.