The goal of this clinical study is to evaluate a software device and its impact on clinician behaviour during the initial management of trauma patients in a real-world clinical setting. Known as the AI-TRiPS Device this software uses real-time prehospital data and machine learning-based risk predictions which are displayed digitally for hospital trauma teams prior patient arrival. The investigators will use a Stepped Wedge Cluster Randomised Controlled study design with an integrated process evaluation. The Device will be deployed across the London Major Trauma System where the Major Trauma Centres will be the clusters. Each cluster will transition from control (standard care) to intervention at a pre-specified time (time of transition is randomised). Primary Outcome: Clinician behaviour, assessed via the accuracy of risk prediction and clinician confidence. Secondary Outcome: Clinician acceptability, care process metrics, patient outcomes, and safety endpoints. Primary study population: Hospital trauma clinicians, following initial resuscitation of each eligible trauma patient, who will complete electronic questionnaires. Secondary study population: Adult trauma patients, data will be collected for the duration of their index admission to hospital, to assess outcomes and enable comparison with clinician risk predictions.
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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Enrollment
1,200
This is Software as a Medical Device designed to function as an aid to inform clinical situational awareness by presenting predictions of patient trajectory (probability of death, probability of trauma induced coagulopathy, probability of red cell transfusion, probability of acute kidney injury).
Clinician Risk Prediction - Mortality, Trauma Induced Coagulopathy, and Acute Kidney Injury
Clinician participants will make probability estimates (0-100%) on index admission in each of the 3 domains.
Time frame: Baseline
Clinician Risk Prediction - Estimation of Blood Transfusion Volume
Clinicians will estimate the number(n) of packed red blood cell (pRBC) units required for transfusion in the first 24 hours. The estimation will take place immediately after initial resuscitation.
Time frame: Baseline
Clinician Confidence
Clinician Participants will self-report their confidence in their predictions using the Post-Task Confidence Scale (PTCS), a Likert scale from 1-7, where the higher the score the higher the level confidence.
Time frame: Baseline to 24 Hours - Immediately following initial clinician predictions
Clinician Cognitive Effort
Clinician participants self-report the mental effort required to make each prediction using the Paas Mental Effort Scale ( Likert Scale 1-9) where a lower score corresponds to low mental effort.
Time frame: Baseline to 24 Hours - immediately following risk predictions
Risk Prediction Accuracy
For each of the 4 domains in which predictions have been made, accuracy of these predictions will be determined with a comparison to patient outcomes. This will be done using the Brier score, however other metrics of predictive performance may also be used to perform comparisons, including measures of discrimination, calibration, and accuracy (Brier skill Score, Mean Absolute Error)
Time frame: From Discharge through to study completion, an average of 1 year.
Clinician Decision-Making Behaviour - Decision Making
Measurement of whether a decision was made (Decision in this case refers to activation of the major haemorrhage protocol, or proceeding directly to definitive haemorrhage control). This data will be extracted from the National Major Trauma Registry and/or patient clinical records. Binary (yes/no) based on whether the outcome was performed.
Time frame: From discharge through to study completion, an average of 1 year.
Clinician Decision-Making Behaviour - Appropriateness of Decision Making
Expert panel review of decision making with regards to activation of major haemorrhage protocol/proceeding directly to definitive haemorrhage control. Expert review of extracted patient data from National Major Trauma Registry and/or Patient clinical records. Binary (Appropriate/Inappropriate)
Time frame: From Discharge through to study completion, an average of 1 year.
Clinician Decision-Making Behaviour - Clinician Confidence
Clinician Participants will self-report their confidence in their predictions using the Post-Task Confidence Scale (PTCS), a Likert scale from 1-7, where the higher the score the higher the level confidence.
Time frame: Baseline to 24 hours - Immediately following initial clinician decision making
Clinician Decision-Making Behaviour - Clinician Cognitive Effort
Clinician participants self-report the mental effort required to make each prediction using the Paas Mental Effort Scale ( Likert Scale 1-9) where a lower score corresponds to low mental effort.
Time frame: Baseline to 24 Hours - immediately following risk predictions
Clinician Decision-Making Behaviour - Time Pressure
Clinicians self report time pressure using the NASA Task Load Index Temporal Demand Subscale (Likert Scale 1-10). This is measured immediately after each decision.
Time frame: Baseline to 24 Hours - immediately following decision making
Clinical Process Measures - Time to Major Haemorrhage Protocol(MHP) Activation
Time to MHP activation in minutes(continuous) from arrival to activation of major haemorrhage protocol. Data collected from National Major Trauma Registry and/or patient hospital records.
Time frame: Baseline - 12 Hours
Clinical Process Measures - Time to Haemorrhage Control
Time to Haemorrhage control in minutes(continuous) from arrival to start of first definitive haemorrhage control intervention. Data collected from National Major Trauma Registry and/or patient hospital records.
Time frame: Baseline - 12 Hours
Clinical Process Measures - Length of Hospital Stay
Total number of inpatient hospital days (continuous), measured from index admission to discharge. Data obtained from National Major Trauma Registry and/or patient clinical records following discharge.
Time frame: Discharge through to study completion, an average of 1 year
Clinical Process Measures - Intensive Care Unit (ICU) length of stay
Total number of intensive care unit days (continuous), measured from index admission to discharge. Data obtained from National Major Trauma Registry and/or patient clinical records following discharge.
Time frame: Discharge through to study completion, an average of 1 year
Patient Outcome Measure - In Hospital Mortality
Patient death during index hospital admission, Binary (yes/No).
Time frame: From Baseline to Discharge/Death
Patient Outcome Measure - Trauma Induced Coagulopathy
Trauma-induced coagulopathy will be assessed using the admission prothrombin time ratio (PTr). This variable will be recorded as binary (yes/no), with trauma-induced coagulopathy defined as a PTr \> 1.2
Time frame: Baseline
Patient Outcome Measure - Blood Transfusion Volume
The total number (N) of units of packed red blood cells (pRBC) transfused to the patient within the first 24 hours post injury.
Time frame: Baseline to 24 hours
Patient Outcome Measure - Acute Kidney Injury
The degree of acute kidney injury will be recorded using the Kidney Disease Improving Global Outcomes(KDIGO) stage 1-3, over the first 72 hours post injury.
Time frame: Baseline to 72 hours
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