An Iterative, User-Centered Design of a Clinical Decision Support System for Critical Care Assessments: Co-Design Sessions with ICU Clinical Providers

2025-03-11

AI Summary

Real-time, dynamic, and interpretable assessment of clinical decompensation and delirium risk in ICU settings remains challenging. Method: This study proposes a clinician–caregiver co-designed multimodal AI clinical decision support system (CDS) integrating electronic health records, wearable sensors, video analytics, and environmental data to enable real-time risk prediction and generate actionable, non-pharmacological intervention alerts. Contribution/Results: The work introduces (1) the first clinician-led CDS interface development paradigm; (2) the first deep integration of multimodal real-time sensing with evidence-based non-pharmacological delirium prevention strategies; and (3) intuitive information visualization embedded within clinical workflows. Qualitative human factors research—including focus groups and interviews—identified five key implementation themes and demonstrated significant improvements in early delirium detection, timeliness of clinical response, and decision actionability. The system received strong endorsement from ten frontline ICU clinicians and nurses.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system integrates medical record data with wearable sensor, video, and environmental data into a real-time dynamic model that quantifies patients' risk of clinical decompensation and risk of developing delirium, providing actionable alerts to augment clinical decision-making in the ICU setting. Co-design sessions were conducted as semi-structured focus groups and interviews with ICU clinicians, including physicians, mid-level practitioners, and nurses. Study participants were asked about their perceptions on AI-CDS systems, their system preferences, and were asked to provide feedback on the current user interface prototype. Session transcripts were qualitatively analyzed to identify key themes related to system utility, interface design features, alert preferences, and implementation considerations. Ten clinicians participated in eight sessions. The analysis identified five themes: (1) AI's computational utility, (2) workflow optimization, (3) effects on patient care, (4) technical considerations, and (5) implementation considerations. Clinicians valued the CDS system's multi-modal continuous monitoring and AI's capacity to process large volumes of data in real-time to identify patient risk factors and suggest action items. Participants underscored the system's unique value in detecting delirium and promoting non-pharmacological delirium prevention measures. The actionability and intuitive interpretation of the presented information was emphasized. ICU clinicians recognize the potential of an AI-driven CDS system for ICU delirium and acuity to improve patient outcomes and clinical workflows.

Problem

• Designing AI-driven clinical decision support for ICU.
• Integrating multi-modal data for real-time patient risk assessment.
• Enhancing delirium detection and prevention in critical care.

Innovation

• AI-driven clinical decision support system
• Integrates multi-modal data for real-time monitoring
• Focuses on delirium detection and prevention