The HEART Interface: Visualizing Risk Score Uncertainty in the Cardiothoracic ICU

@XXXXX{Nourani2026HEARTInterfaceVisualizing,
  author    = {Mahsan Nourani, Lien Nguyen, Carey Barry, Qingchu Jin, Melanie Tory},
  VENUE     = {IUI '26: Proceedings of the 31st International Conference on Intelligent User Interfaces},
  title     = {The HEART Interface: Visualizing Risk Score Uncertainty in the Cardiothoracic ICU},
  year      = {2026},
  note      = {XXX YYYY. Preprint at \url{}. Supplemental material at \url{[https://osf.io/akqx8/}.},
](https://osf.io/akqx8/%7D.%7D,/n)  number    = {},
  pages     = {1019 - 1036},
  volume    = {},
  abstract  = {Artificial Intelligence (AI) holds significant potential for supporting clinical decision-making, particularly in high-pressure environments, such as Cardiothoracic Intensive Care Units (CT-ICU). Care teams in these settings face challenges such as alarm fatigue, rapid staff turnover, time-sensitive decisions, and an overwhelming amount of data. AI-driven Clinical Decision-Support Systems (AI-CDSS) can support care teams in overcoming some of these challenges by providing solutions like detecting and reporting risk scores for adverse events that may lead to increased fatalities or re-admissions, enabling timely intervention. One key challenge with risk scores is missing data, which can create considerable uncertainty in risk score values. AI-CDSSs rarely convey the risk score uncertainty, which is important in the effectiveness and reliability of clinical decision-making. In this paper, we describe the interface design process for HEART, an AI-powered system developed collaboratively with clinical and AI experts over a 16-month iterative design process for a hospital’s CT-ICU. The HEART interactive interface integrates understandable visualizations of risk scores and their uncertainty within both a holistic view of all patients in the unit and detailed patient-specific views. We reflect on the user-centered design process, report findings from an expert walkthrough study, and discuss lessons learned as well as broader implications. This work contributes valuable insights into uncertainty visualization design for AI-derived risk scores in a critical care application. Beyond these specific insights, our work illustrates the kind of comprehensive, human-centered design process necessary for responsible AI adoption in critical environments.},
  articleno = {},
  doi       = {[https://doi.org/10.1145/3742413.3789109},
](https://doi.org/10.1145/3742413.3789109%7D,/n)  series   = {},
}