Clinical Evaluation of an AI Risk Prediction System (AI-TRiPS)

This project evaluates a bespoke risk prediction system developed by trauma surgeons, pre-hospital clinicians, and computer scientists. The device aims to enhance the situational awareness of hospital trauma teams via a digital display, located in the resuscitation suite, depicting pre-hospital patient status and individualised risk predictions.

Evidence Base and Prior Work

The AI-TRiPS Device builds on an extensive, multi-phase programme of research led by the Centre for Trauma Sciences at Queen Mary University of London, funded by the US Department of Defense, UK Ministry of Defence, and Rosetrees Trust. This programme has:

* Investigated trauma clinical decision-making, demonstrating that situational awareness is often impaired by uncertainty and cognitive load, and highlighting the need for decision support during early trauma resuscitation. * Developed clinically relevant, explainable Bayesian network models using hybrid data- and knowledge-driven methods, with internal and external validation across large civilian and military trauma datasets. * Designed and iteratively refined a web-based clinical decision support system (CDSS) to deliver model outputs through an interface tailored to trauma resuscitation workflows, incorporating end-user feedback. * Conducted simulation and operational studies demonstrating improved clinician performance with the CDSS compared to unaided judgement. * Contributed methodological work to support the safe and effective translation of prediction algorithms into usable and trustworthy clinical tools, including published frameworks for usability testing, implementation evaluation, and explainability in clinical decision support.

The current stage of development is consistent with early-stage clinical evaluation of a Software as a Medical Device (SaMD) under UK MDR 2002 and ISO 14155.

This trial is designed to evaluate clinical performance and safety in real-world conditions, with a primary focus on effects on clinician behaviour and decision-making. While patient outcomes will be collected, the study is not powered to assess downstream impact on clinical outcomes.

Primary Objective To evaluate the impact of the AI-TRiPS device on clinician behaviour during the initial management of trauma patients in a real-world clinical setting, specifically situational awareness (clinician perception of individual patient risk), associated confidence, and cognitive load, compared with standard unassisted clinician performance.

Hypothesis The investigators hypothesise that delivering accurate, real-time risk estimates to trauma clinicians during the initial phase of trauma care will improve situational awareness - in particular, clinicians' perception of individual patient risks - along with increased confidence and reduced cognitive effort, compared with standard unassisted clinician performance.

Null Hypothesis There is no difference in clinician situational awareness (including perception of risk), confidence, or cognitive load between AI-assisted and unassisted clinician performance during initial trauma care.

Secondary objective(s)

Secondary Objectives

• Evaluate impact on Clinician Decision-Making: To assess the effect of the AI-TRiPS device on clinician decision-making, as a potential downstream effect of changes in clinician risk perception (situational awareness).

• Evaluate impact on Clinical Processes: To assess the effect of the AI-TRiPS device on early trauma care processes, including time to critical interventions and length of stay

• Evaluate Patient Impact: To examine patient outcomes associated with clinician exposure to the AI-TRiPS device, recognising these as indirect effects mediated by altered clinical decision-making.

• Evaluate Real-World System Performance: To assess the real-world performance of the AI-TRiPS device, including prediction calibration and the identification of system errors or underperformance that may affect clinical decision-making.

• Evaluate usability and acceptability (Integrated Process Evaluation): To explore the acceptability, usability, and contextual factors that influence the implementation and adoption of the AI-TRiPS device in real-world clinical settings.

• Clinician Decision-Making Behaviour - Decision Making [Time frame: From discharge through to study completion, an average of 1 year.]
• Clinician Decision-Making Behaviour - Appropriateness of Decision Making [Time frame: From Discharge through to study completion, an average of 1 year.]
• Clinician Decision-Making Behaviour - Clinician Confidence [Time frame: Baseline to 24 hours - Immediately following initial clinician decision making]
• Clinician Decision-Making Behaviour - Clinician Cognitive Effort [Time frame: Baseline to 24 Hours - immediately following risk predictions]
• Clinician Decision-Making Behaviour - Time Pressure [Time frame: Baseline to 24 Hours - immediately following decision making]
• Clinical Process Measures - Time to Major Haemorrhage Protocol(MHP) Activation [Time frame: Baseline - 12 Hours]
• Clinical Process Measures - Time to Haemorrhage Control [Time frame: Baseline - 12 Hours]
• Clinical Process Measures - Length of Hospital Stay [Time frame: Discharge through to study completion, an average of 1 year]
• Clinical Process Measures - Intensive Care Unit (ICU) length of stay [Time frame: Discharge through to study completion, an average of 1 year]
• Patient Outcome Measure - In Hospital Mortality [Time frame: From Baseline to Discharge/Death]
• Patient Outcome Measure - Trauma Induced Coagulopathy [Time frame: Baseline]
• Patient Outcome Measure - Blood Transfusion Volume [Time frame: Baseline to 24 hours]