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An Explanation User Interface for Artificial Intelligence-Supported Mechanical Ventilation Optimization for

Ian-C Jung1, Maria Zerlik1, Katharina Schuler1

  • 1Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

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Summary

Designing explainable AI (XAI) interfaces for clinical decision support systems (CDSSs) requires understanding user needs. This study found that ICU nurses and physicians have different preferences for AI explanations in mechanical ventilation support.

Keywords:
CDSSICUXAIXUIclinical decision support systemexplainable artificial intelligenceexplanation user interfaceformative evaluationintensive care unitusability

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Area of Science:

  • Artificial Intelligence in Medicine
  • Human-Computer Interaction
  • Clinical Decision Support

Background:

  • Artificial intelligence (AI) integration in clinical decision support systems (CDSSs) for mechanical ventilation offers potential but faces adoption barriers due to lack of transparency.
  • Explainable AI (XAI) and explanation user interfaces (XUIs) are crucial for enhancing trust and usability in high-stakes environments like intensive care units (ICUs).
  • Limited understanding exists regarding clinician interaction with XUIs in ICUs, necessitating research for seamless integration of AI into clinical workflows.

Purpose of the Study:

  • To evaluate the initial design and user perception of an XUI for an AI-based CDSS optimizing mechanical ventilation in the ICU.
  • To explore how different clinical user groups (ICU nurses and physicians) perceive and prioritize explanation concepts within the XUI.
  • To gather empirical data for refining the XUI design based on user feedback and role-specific needs.

Main Methods:

  • Developed a midfidelity XUI prototype using Justinmind, adhering to user-centered design (UCD) principles (ISO 9241-210).
  • Conducted formative evaluations through two usability walkthroughs with resident physicians and ICU nurses.
  • Collected qualitative and quantitative feedback, including guided discussions and Likert-scale assessments on explanation understandability, suitability, and visual appeal.

Main Results:

  • A two-level XUI was designed: Level 1 provided high-level explanations (outlier warning, output certainty), and Level 2 offered detailed insights (input, feature importance, rule-based explanations).
  • Physicians found the detailed second level useful, while ICU nurses preferred the concise first level, indicating differing role-specific needs.
  • The layered design effectively balanced transparency and information overload, confirming the need for role-dependent explanation strategies.

Conclusions:

  • User-centered design (UCD) is vital for developing effective explanation user interfaces (XUIs) for clinical decision support systems (CDSSs).
  • Recognizing and addressing the distinct information requirements of physicians and ICU nurses is crucial for XUI development.
  • Findings offer practical guidance for designing layered, role-sensitive XUIs in critical care, paving the way for future impact studies on trust and decision-making.