ArticleHuman factors in healthcare2026
Human-Centered design requirements for clinical decision support in cardiac surgery perfusion.
Article in Human factors in healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Background: Perfusionists manage critical physiological parameters of cardiac surgery patients during cardiopulmonary bypass (CPB), requiring rapid, high-stakes decision-making under uncertainty. While artificial intelligence (AI) has the potential to enhance perfusionists' decision-making and improve patient safety, there is limited understanding of how future AI-enabled clinical decision support systems (CDSS) should be designed to align with perfusionists' workflows and cognitive demands. Objective: This study aims to identify human-centered design requirements for CDSS in cardiac perfusion and to conduct a formative evaluation of a prototype interface presenting simulated decision-support recommendations. Methods: A three-phase, multiple-method qualitative study was conducted with eight practicing perfusionists. Phase 1 involved semi-structured interviews to characterize workflow challenges and design requirements. Phase 2 engaged participants in structured prototype-feedback sessions to refine an interface concept for future AI-enabled decision support. Phase 3 assessed interface usability using scenario-based tasks with simulated AI recommendations designed by clinical experts and measured task success and exploratory self-reported perceptions. Results: Overall, perfusionists expressed positive perceptions of future AI-enabled CDSS concepts but emphasized the need to retain human control and receive contextually relevant explanations. Among three explanation formats evaluated, decision trees emerged as the most preferred explanation format across the study participants. In usability testing, participants achieved an 84% task success rate, reported positive perceptions of the prototype and perceived compatibility with perfusion workflows. Conclusion: This work contributes empirically grounded design requirements for future AI-enabled CDSS in safety-critical intraoperative settings. Key design considerations include: (1) prioritizing interpretable explanations (such as decision trees), (2) ensuring system recommendations align with perfusionists' reported monitoring strategies, and (3) preserving clinician oversight through human-in-the-loop design frameworks.
Indexed as
Identifiers
What OpenQuestion holds
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.