Clinicians’ perspectives on the design of early warning systems presenting AI-based multiple outputs for clinical deterioration: A qualitative analysis

Background AI-enabled clinical decision support systems (CDSS) can help detect patient deterioration, but their value depends on outputs that fit workflows, support reasoning, and avoid alert fatigue. Objective. Little is known about clinician perspectives on multi-output Early Warning Systems (MO-EWS), which provide simultaneous predictions (e.g., overall deterioration, organ- or diagnosis-specific). This study explores clinician views on future MO-EWS and offers user-centred recommendations to improve design, usability, and adoption. Methods Reflexive thematic analysis was used to examine clinicians’ perceptions of existing early warning systems (EWS) and envisioned AI-based tools for predicting clinical deterioration. Between January and June 2025, 22 clinicians from Australian hospitals with experience in managing clinical deterioration were interviewed using a semi-structured questionnaire. Resulting themes were mapped to system components (user interface [UI], AI model, clinical workflow) to inform practical design recommendations which were then assessed for their feasibility. Results Participants found existing EWS distracting and sometimes unsafe. They envisioned AI-based systems that provide multiple, contextualised outputs (e.g., causes of deterioration, organ dysfunction, clinical severity) as layered, adaptable tools to support reasoning, prioritisation, and communication. While many UI improvements are readily achievable, model development is more complex, and workflow integration will depend on infrastructure, policies, and clinician preferences. Conclusion Clinicians perceive potential benefits in an AI-enabled system that provides more insightful information when predicting clinical deterioration in individual patients, but various design and implementation challenges remain.