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.
Age range
16 Years
Sex
ALL
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Clinician Risk Prediction - Mortality, Trauma Induced Coagulopathy, and Acute Kidney Injury
Timeframe: Baseline
Clinician Risk Prediction - Estimation of Blood Transfusion Volume
Timeframe: Baseline
Clinician Confidence
Timeframe: Baseline to 24 Hours - Immediately following initial clinician predictions
Clinician Cognitive Effort
Timeframe: Baseline to 24 Hours - immediately following risk predictions
Risk Prediction Accuracy
Timeframe: From Discharge through to study completion, an average of 1 year.