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

Summary

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.

Conditions

• Trauma
• Injury
• Decision Support Systems, Clinical

Interventions

DEVICE

AI-TRiPS Device

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).

Sponsors & Collaborators

• Congressionally Directed Medical Research Programs

  collaborator FED

• University of Aberdeen

  collaborator OTHER

• Barts & The London NHS Trust

  collaborator OTHER

• St George's University Hospitals NHS Foundation Trust

  collaborator OTHER

• Imperial College Healthcare NHS Trust

  collaborator OTHER

• King's College Hospital NHS Trust

  collaborator OTHER

• London Ambulance Service NHS Trust

  collaborator OTHER

• London's Air Ambulance Charity

  collaborator OTHER

• Queen Mary University of London

  lead OTHER

Principal Investigators

• Prof. N Tai · Queen Mary University London

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

NONE

Model

SEQUENTIAL

Eligibility

Min Age

16 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-06-01

Primary Completion

2027-06-01

Completion

2027-12-01