ClinicalVis: Supporting Clinical Task-Focused Design Evaluation
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Abstract (arXiv)
Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient needs. Electronic health records (EHRs) are a rich data source, but are task-agnostic and can be difficult to use as summarizations of patient needs for a specific task, such as "could this patient need a ventilator tomorrow?" In this paper, we introduce ClinicalVis, an open-source EHR visualization-based prototype system for task-focused design evaluation of interactions between healthcare providers (HCPs) and EHRs. We situate ClinicalVis in a task-focused proof-of-concept design study targeting these interactions with real patient data. We conduct an empirical study of 14 HCPs, and discuss our findings on usability, accuracy, preference, and confidence in treatment decisions. We also present design implications that our findings suggest for future EHR interfaces, the presentation of clinical data for task-based planning, and evaluating task-focused HCP/EHR interactions in practice.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2.
4 papers.
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2022Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-makingcited by 14
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
- 2024Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Settingcited by 5
- 2024Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting16 Oct 2024
- 2024Trust in AI: Progress, Challenges, and Future Directions12 Mar 2024
- 2022Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making11 Apr 2022
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models4 Aug 2021