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Not yet recruitingNCT07634185AI-TRiPSUpdated Jun 8, 2026

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

An Early Phase 1 interventional study of AI-TRiPS Device in Trauma, Injury and Decision Support Systems, Clinical, sponsored by Queen Mary University of London. Not yet recruiting. Open to participants aged 16 Years and older. Per ClinicalTrials.gov, last updated 2026-06-08.

Sponsored by Queen Mary University of London · Early Phase 1, Interventional, and Other

Phase
Early Phase 1
Study type
Interventional
Enrollment
1,200
Allocation
Randomized
Ages
16 Years and older
Sex
All
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Study 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.

Read the detailed description

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.
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Conditions studied

  • Trauma
  • Injury
  • Decision Support Systems, Clinical

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Keywords

  • Device trial, prediction tool, trauma
  • clinical decision support
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In context

Wounds and Injuries

5,056 studies on the registry are indexed under Wounds and Injuries; 861 are open to participants now.

This study's planned enrollment of 1,200 is above the median of 52 across 3,239 interventional studies indexed under Wounds and Injuries.

Browse Wounds and Injuries studies →

Lead sponsor

Queen Mary University of London is the lead sponsor of 250 studies on the registry; 59 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
16 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Inclusion criteria

Clinician Participants

  • Senior clinical decision-maker involved in the initial trauma resuscitation (e.g. consultant or senior trainee in emergency medicine, anaesthesia, intensive care medicine, or surgery).
  • Based at one of the four participating Major Trauma Centres.
  • Able and willing to provide informed consent.
  • Completed the required study-specific training.

Trauma Patients

  • Aged 16 years and above.
  • Treated and transported to a participating Major Trauma Centre by London's Air Ambulance.
  • Managed by one or more participating trauma clinicians during the resuscitation.

Exclusion criteria

Exclusion Criteria:

Clinician Participants

● Decline or withdraw informed consent at any stage.

Trauma Patients

  • Aged under 16
  • Not treated by London's Air Ambulance.
  • Transported to a non-participating hospital.
  • Not managed by any participating clinicians.
  • Presenting with injuries resulting from burns, hangings, drownings, or isolated psychiatric emergencies.
  • Have registered a national NHS data opt-out or otherwise requested that their routine clinical data not be used for research.
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Study design

Phase
Early Phase 1
Primary purpose
Other
Allocation
Randomized
Intervention model
Sequential assignment
Masking
None (open label)
Enrollment
1,200 participants (estimated)

Study arms

  • Experimental
    AI TRIPS device intervention

    Patients who fit the eligibility criteria are triaged and treated at the participating trauma centre by trauma clinicians who have been exposed to the individualised risk predictions for that patient.

    Device: AI-TRiPS Device

  • No intervention
    Usual Standard Care

    Patients who fit the eligibility criteria are triaged and treated at the participating trauma centre by trauma clinicians under standard conditions.

Interventions

  • DeviceAI-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).

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What researchers measure

Primary outcomes

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

Secondary outcomes

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Patient Outcome Measure - In Hospital Mortality

    Patient death during index hospital admission, Binary (yes/No).

    Time frame: From Baseline to Discharge/Death

  11. 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

  12. 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

  13. 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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Study locations

No study locations are listed for this record.

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References and documents

Publications

  • Kyrimi E, Neves MR, McLachlan S, Neil M, Marsh W, Fenton N. Medical idioms for clinical Bayesian network development. J Biomed Inform. 2020 Aug;108:103495. doi: 10.1016/j.jbi.2020.103495. Epub 2020 Jun 30. PubMed 32619692 ↗
  • McLachlan S, Kyrimi E, Wohlgemut J, Perkins Z, Lagnado D, Marsh W. Explainable AI: Definition and characteristics of a good explanation for health AI. AI and Ethics. 2025:1.
  • Wohlgemut JM, Pisirir E, Stoner RS, Perkins ZB, Marsh W, Tai NRM, Kyrimi E. A scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems. BMC Med Inform Decis Mak. 2024 Nov 1;24(1):323. doi: 10.1186/s12911-024-02739-1. PubMed 39487462 ↗
  • Kyrimi E, McLachlan S, Wohlgemut JM, Perkins ZB, Lagnado DA, Marsh W. Explainable AI: definition and attributes of a good explanation for health AI. AI and Ethics. 2025:1-14.
  • Pisirir E, Wohlgemut JM, Kyrimi E, et al. A process for evaluating explanations for transparent and trustworthy ai prediction models. IEEE; 2023:388-397.
  • Kyrimi E, Stoner RS, Perkins ZB, Pisirir E, Wohlgemut JM, Marsh W, Tai NRM. Updating and recalibrating causal probabilistic models on a new target population. J Biomed Inform. 2024 Jan;149:104572. doi: 10.1016/j.jbi.2023.104572. Epub 2023 Dec 9. PubMed 38081566 ↗
  • Wohlgemut JM, Pisirir E, Kyrimi E, Stoner RS, Marsh W, Perkins ZB, Tai NRM. Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis. JAMIA Open. 2023 Jul 12;6(3):ooad051. doi: 10.1093/jamiaopen/ooad051. eCollection 2023 Oct. PubMed 37449057 ↗
  • Marsden MER, Perkins ZB, Pisirir E, Marsh W, Kyrimi E, Rossetto A, Lyon RL, Weaver A, Davenport R, Tai NR. Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting. Emerg Med J. 2025 Sep 24;42(10):654-661. doi: 10.1136/emermed-2024-214396. PubMed 40234019 ↗
  • Marsden M, Perkins Z, Marsh W, et al. Evaluation of an Artificial Intelligence (AI) system to augment clinical risk prediction of Trauma Induced Coagulopathy in the pre-hospital setting: a prospective observational study: 3. BMJ Military Health. 2022;168(5):e12.
  • Alptekin C, Wohlgemut JM, Perkins ZB, Marsh W, Tai NRM, Yet B. Presenting predictions and performance of probabilistic models for clinical decision support in trauma care. Int J Med Inform. 2025 Feb;194:105702. doi: 10.1016/j.ijmedinf.2024.105702. Epub 2024 Nov 14. PubMed 39579585 ↗
  • Wohlgemut JM, Kyrimi E, Stoner RS, Pisirir E, Marsh W, Perkins ZB, Tai NRM. The outcome of a prediction algorithm should be a true patient state rather than an available surrogate. J Vasc Surg. 2022 Apr;75(4):1495-1496. doi: 10.1016/j.jvs.2021.10.059. Epub 2021 Dec 16. No abstract available. PubMed 34921966 ↗
  • Tandle S, Wohlgemut JM, Marsden MER, Pisirir E, Kyrimi E, Stoner RS, Marsh W, Perkins ZB, Tai NRM. Enhancing the clinical relevance of haemorrhage prediction models in trauma. Mil Med Res. 2023 Sep 20;10(1):43. doi: 10.1186/s40779-023-00476-6. No abstract available. PubMed 37726859 ↗
  • Perkins ZB, Yet B, Sharrock A, Rickard R, Marsh W, Rasmussen TE, Tai NRM. Predicting the Outcome of Limb Revascularization in Patients With Lower-extremity Arterial Trauma: Development and External Validation of a Supervised Machine-learning Algorithm to Support Surgical Decisions. Ann Surg. 2020 Oct;272(4):564-572. doi: 10.1097/SLA.0000000000004132. PubMed 32657917 ↗
  • Kyrimi E, Mossadegh S, Tai N, Marsh W. An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making. Artif Intell Med. 2020 Mar;103:101812. doi: 10.1016/j.artmed.2020.101812. Epub 2020 Jan 31. PubMed 32143808 ↗
  • Yet B, Perkins ZB, Tai NR, Marsh DWR. Clinical evidence framework for Bayesian networks. Knowledge and Information Systems. 2017;50(1):117-143.
  • Yet B, Perkins ZB, Rasmussen TE, Tai NR, Marsh DW. Combining data and meta-analysis to build Bayesian networks for clinical decision support. J Biomed Inform. 2014 Dec;52:373-85. doi: 10.1016/j.jbi.2014.07.018. Epub 2014 Aug 9. PubMed 25111037 ↗
  • Yet B, Perkins Z, Fenton N, Tai N, Marsh W. Not just data: a method for improving prediction with knowledge. J Biomed Inform. 2014 Apr;48:28-37. doi: 10.1016/j.jbi.2013.10.012. Epub 2013 Nov 2. PubMed 24189161 ↗
  • Perkins ZB, Yet B, Marsden M, Glasgow S, Marsh W, Davenport R, Brohi K, Tai NRM. Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model. Ann Surg. 2021 Dec 1;274(6):e1119-e1128. doi: 10.1097/SLA.0000000000003771. PubMed 31972649 ↗
  • Durrands TH, Murphy M, Wohlgemut JM, De'Ath HD, Perkins ZB. Diagnostic accuracy of clinical examination for identification of life-threatening torsos injuries: a meta-analysis. Br J Surg. 2023 Nov 9;110(12):1885-1886. doi: 10.1093/bjs/znad285. No abstract available. PubMed 37847819 ↗
  • Wohlgemut JM, Pisirir E, Stoner RS, Kyrimi E, Christian M, Hurst T, Marsh W, Perkins ZB, Tai NRM. Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome. Trauma Surg Acute Care Open. 2024 Jan 12;9(1):e001214. doi: 10.1136/tsaco-2023-001214. eCollection 2024. PubMed 38274019 ↗
  • Marsden MER, Kellett S, Bagga R, Wohlgemut JM, Lyon RL, Perkins ZB, Gillies K, Tai NR. Understanding pre-hospital blood transfusion decision-making for injured patients: an interview study. Emerg Med J. 2023 Nov;40(11):777-784. doi: 10.1136/emermed-2023-213086. Epub 2023 Sep 13. PubMed 37704359 ↗
  • Wohlgemut JM, Marsden MER, Stoner RS, Pisirir E, Kyrimi E, Grier G, Christian M, Hurst T, Marsh W, Tai NRM, Perkins ZB. Diagnostic accuracy of clinical examination to identify life- and limb-threatening injuries in trauma patients. Scand J Trauma Resusc Emerg Med. 2023 Apr 7;31(1):18. doi: 10.1186/s13049-023-01083-z. PubMed 37029436 ↗

Individual participant data

Plan to share: No

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Jun 8, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT07634185
Lead sponsor
Queen Mary University of London
Collaborators
Congressionally Directed Medical Research Programs, University of Aberdeen, Barts & The London NHS Trust, St George's University Hospitals NHS Foundation Trust, Imperial College Healthcare NHS Trust, King's College Hospital NHS Trust, London Ambulance Service NHS Trust, London's Air Ambulance Charity
Responsible party
Sponsor
First posted
Jun 8, 2026
Start date
Jun 1, 2026 (estimated)
Primary completion
Jun 1, 2027 (estimated)
Completion
Dec 1, 2027 (estimated)
Last update
Jun 8, 2026

Study contacts

Dr Mays Jawad
Contact
research.governance@qmul.ac.uk
+4402078827275
Prof. N Tai
Contact
bartsheatlh.AITRIPS@nhs.net
+4402073777044
Prof. N Tai
principal investigator · Queen Mary University London

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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