TraumaEarly Phase 1AI-TRiPSNCT07634185
Clinical Evaluation of an AI Risk Prediction System (AI-TRiPS)
A Early Phase 1 study testing AI-TRiPS Device in people with Trauma and Injury, run by Queen Mary University of London.
Trial 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. Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Trauma, Injury, Decision Support Systems, Clinical
- What is being tested?
- AI-TRiPS Device
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,200 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2026to 1 Dec 2027
- Phase
- Early Phase 1
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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
Show all 9Show fewer
- 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.
Who cannot join
- 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.
Show all 9Show fewer
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients: a Stepped-wedge Cluster Randomised Trial
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.
Read more
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.
- Conditions
- Trauma, Injury, Decision Support Systems, Clinical
- Being tested
- AI-TRiPS Device
- Keywords
- Device trial, prediction tool, trauma, clinical decision support
- Medical subject headings
- Wounds and Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-TRiPS DeviceDevice
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).
| Study group | Role | What they receive |
|---|---|---|
| AI TRIPS device intervention | Experimental | 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. Receives: Device: AI-TRiPS Device |
| Usual Standard Care | No intervention | Patients who fit the eligibility criteria are triaged and treated at the participating trauma centre by trauma clinicians under standard conditions. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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)
Measured: From Discharge through to study completion, an average of 1 year.
Secondary outcomes (13)
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.
Measured: 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)
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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
Measured: 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.
Measured: 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.
Measured: Baseline to 72 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2026
- Study start1 Jun 2026 (estimated)
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (22)Papers the sponsor has linked to this study.
- Other trials for TraumaStudies of the same condition, open and under way.
- More trials run by Queen Mary University of LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Early Phase 1
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Sequential
- Model description
- Early-phase, formative, interventional study using a stepped-wedge cluster randomised design with an integrated process evaluation across an integrated regional trauma system. The intervention is a digital dashboard displaying real-time prehospital data and machine learning-based risk predictions to hospital trauma teams before patient arrival. Each site transitions from control standard care to intervention at a pre-specified time, dependent on randomisation. The primary outcome is clinician behaviour, assessed via the accuracy of risk estimation and clinician confidence. Secondary outcomes include clinician acceptability, care process metrics, patient outcomes, and safety endpoints
- Masking
- None (open label)
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Queen Mary University of London
- Sponsor type
- University, hospital or other
- 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
- Investigators
- Prof. N Tai, Principal investigator, Queen Mary University London
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07634185
- Sponsor's protocol ID
- 354225
- Other IDs
- 179821
- Acronym
- AI-TRiPS
- Status last verified
- Jun 2026
- First submitted
- 6 May 2026
- Last updated
- 8 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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 32619692McLachlan 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 39487462Kyrimi 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 38081566Wohlgemut 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 37449057Marsden 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 40234019Marsden 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 39579585Wohlgemut 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 34921966Tandle 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 37726859Perkins 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 32657917Kyrimi 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 32143808Yet B, Perkins ZB, Tai NR, Marsh DWR. Clinical evidence framework for Bayesian networks. Knowledge and Information Systems. 2017;50(1):117-143.
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients: a Stepped-wedge Cluster Randomised Trial
- ClinicalTrials.gov ID
- NCT07634185
- Acronym / sponsor ID
- AI-TRiPS / 354225
- Conditions
- Trauma, Injury, Decision Support Systems, Clinical
- Keywords
- Device trial, prediction tool, trauma, clinical decision support
- Phase
- Early Phase 1
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- Queen Mary University of LondonUniversity, hospital or other
- 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
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 8 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Early-phase, formative, interventional study using a stepped-wedge cluster randomised design with an integrated process evaluation across an integrated regional trauma system. The intervention is a digital dashboard displaying real-time prehospital data and machine learning-based risk predictions to hospital trauma teams before patient arrival. Each site transitions from control standard care to intervention at a pre-specified time, dependent on randomisation. The primary outcome is clinician behaviour, assessed via the accuracy of risk estimation and clinician confidence. Secondary outcomes include clinician acceptability, care process metrics, patient outcomes, and safety endpoints
| Arm | Type | Receives |
|---|---|---|
| AI TRIPS device intervention | Experimental | Device: AI-TRiPS DevicePatients 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. |
| Usual Standard Care | No intervention | –Patients who fit the eligibility criteria are triaged and treated at the participating trauma centre by trauma clinicians under standard conditions. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (9 listed)
- 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.
Key exclusion criteria (9 listed)
- Clinician Participants
- ● Decline or withdraw informed consent at any stage.
- Trauma Patients
- Aged under 16
- Not treated by London's Air Ambulance.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-TRiPS DeviceThis 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). | Device | AI TRIPS device intervention |
Primary outcome
Clinician Risk Prediction - Mortality, Trauma Induced Coagulopathy, and Acute Kidney Injury
Time frame: Baseline
Clinician participants will make probability estimates (0-100%) on index admission in each of the 3 domains.
Clinician Risk Prediction - Estimation of Blood Transfusion Volume
Time frame: Baseline
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.
Clinician Confidence
Time frame: Baseline to 24 Hours - Immediately following initial clinician predictions
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.
Clinician Cognitive Effort
Time frame: Baseline to 24 Hours - immediately following risk predictions
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.
Risk Prediction Accuracy
Time frame: From Discharge through to study completion, an average of 1 year.
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)
13 secondary outcomes
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
Patient Outcome Measure - Acute Kidney Injury
Time frame: Baseline to 72 hours
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jun 2026.
- Background: PubMed 32619692Kyrimi 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.
- BackgroundMcLachlan 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.
- Background: PubMed 39487462Wohlgemut 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.
- BackgroundKyrimi 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.
- BackgroundPisirir E, Wohlgemut JM, Kyrimi E, et al. A process for evaluating explanations for transparent and trustworthy ai prediction models. IEEE; 2023:388-397.
- Background: PubMed 38081566Kyrimi 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.
- Background: PubMed 37449057Wohlgemut 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.
- Background: PubMed 40234019Marsden 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.
- BackgroundMarsden 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.
- Background: PubMed 39579585Alptekin 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.
- Background: PubMed 34921966Wohlgemut 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.
- Background: PubMed 37726859Tandle 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Prof. N Tai principal investigator
Queen Mary University London
Questions about this trial
What is this clinical trial studying?
A Early Phase 1 study testing AI-TRiPS Device in people with Trauma and Injury, run by Queen Mary University of London.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 8 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Early Phase 1 (sometimes called Phase 0) studies give a very small amount of a treatment to a few people, mainly to see how the body handles it.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Dr Mays Jawad (+4402078827275, research.governance@qmul.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinician Risk Prediction - Mortality, Trauma Induced Coagulopathy, and Acute Kidney Injury (time frame: Baseline). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07634185, which also lists 22 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jun 2026.