CancerSEARCH-EDNCT07391280
Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department
A study testing Harrison.ai Chest X-Ray Solution in people with Cancer and Cardiovascular, run by NHS Greater Glasgow and Clyde at 1 site in United Kingdom.
Trial summary
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital. The aim of the study is to find out if using a computer programme can help doctors diagnose heart and lung problems from chest x-rays. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cancer, Cardiovascular, Respiratory
- What is being tested?
- Harrison.ai Chest X-Ray Solution
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 17,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 25 May 2026to Jun 2027
- Phase
- Interventional
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Unconsented Use of Harrison CXR Algorithm in Emergency Department (ED):
- Frontal Chest X-Ray (CXR) (AP or PA) acquired in the Queen Elizabeth University Hospital (QEUH) ED
- Patients aged 18 or over
- Appropriate meta data (DICOM) to allow for Harrison CXR processing and secondary capture report provision.
- Patient Focus Groups:
- Aged 18 or over
Show all 15Show fewer
- Able to provide written, informed consent in English.
- Clinician Focus Groups:
- Aged 18 or over
- Able to provide written, informed consent in English.
- Working as a doctor, advanced nurse practitioner or advanced clinical practitioner in ED, radiology or downstream medical specialties
- For post-implementation focus groups only, must have at least 4 months experience of working with Harrison CXR algorithm.
- Diagnostic Clinic:
- Patients without terminal illness or advanced frailty
- Usual healthcare provider based in NHS GGC
Who cannot join
- Applies to use of unconsented CXRs:
- Patient has requested that they are removed from the study, or has objected to the use of AI in their routine clinical care and this has been subsequently upheld by the health board.
- Applies to invitation to combined diagnostic clinic:
- Patients not available to follow up, including patients i.e. whose the patient's usual care (or onward care following index admission) is out-with NHS GGC.
- Patients who have been referred to palliative care for end-stage disease, or patients with severe frailty (i.e. bedbound) will not be invited to the combined diagnostic clinic
- For Patient and Clinician Focus Groups:
- Unable to provide informed written consent in English
- Aged <18
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Dervla Carroll contact
United Kingdom1 site, 1 recruiting
- Recruiting
Queen Elizabeth University Hospital
Glasgow
Clea Du Toit, 0141 314 4328, clea.dutoit@nhs.scot
Dr Dervla Carroll, dervla.carroll@glasgow.ac.uk
About this study
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital. The aim of the study is to find out if using a computer programme can help doctors diagnose heart and lung problems from chest x-rays.
We want to compare how many people are diagnosed with heart or lung problems for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Read the detailed description
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital.
The aim of the study is to find out if using an artificial intelligence (AI) computer programme can help doctors diagnose heart and lung problems from chest x-rays. The computer programme is made by Harrison.ai. It is approved for use in the United Kingdom (UK), United States of America (US) and the European Union (EU). Studies have been carried out previously to make sure it is safe to use and that it can detect signs of heart and lung problems.
Many people who come to ED have a chest x-ray. Chest x-rays can show signs of heart or lung problems, which might be causing a patient's symptoms. All doctors can interpret chest x-rays. However, doctors who specialise in interpreting scans (radiologists) also provide an expert report for chest x-rays, describing what they have found. It can take a long time for chest x-ray reports to come back. Sometimes, doctors might miss signs of heart or lung problems.
We want to see if using a computer programme to help doctors interpret chest x-rays could lead to more patients getting an accurate diagnosis. We want to compare how many people are diagnosed with heart or lung problems (Chronic obstructive pulmonary disease [COPD], heart failure or lung cancer) for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Patients older than 18 who have a chest x-ray in ED will be included.
Patients with chest x-rays flagged by the computer programme for heart failure or COPD will be invited to an outpatient clinic for further assessment post-discharge, providing they have not been referred for testing or had testing previously.
All patients with chest x-rays flagged for lung cancer will be reviewed and acted on by the study radiologist.
The study consists of 1) a retrospective component; 2) a prospective live trial; 3) a qualitative evaluation of acceptability to patients and clinicians, and 4) a health economic analysis.
- Conditions
- Cancer, Cardiovascular, Respiratory
- Being tested
- Harrison.ai Chest X-Ray Solution
- Keywords
- Artificial Intelligence, Chest X-Ray, Lung Cancer, Heart Failure, COPD
- Medical subject headings
- Neoplasms, Lung Neoplasms, Heart Failure, Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Harrison.ai Chest X-Ray SolutionDevice
The Harrison.ai CXR module is an AI-driven clinical decision support tool that is designed to augment clinical interpretation of CXRs. It is a Class IIb CE-marked device which is able to detect up to 124 findings on a CXR.
| Study group | Role | What they receive |
|---|---|---|
| Harrison CXR 'ON' Block | Experimental | Chest X-ray AI results will be available to the treating clinician Receives: Device: Harrison.ai Chest X-Ray Solution |
| Harrison CXR 'OFF' Block | Active comparator | Chest X-ray AI results will not be available to the treating clinician Receives: Device: Harrison.ai Chest X-Ray Solution |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation.
Measured: 12 months
Secondary outcomes (11)
Duration of admission during index hospitalisation
Measured: 12 months
Time to initiation of guideline-based, long-term therapy for Chronic obstructive pulmonary disease (COPD) and Heart Failure.
For Chronic obstructive pulmonary disease (COPD), this will be defined as first prescription of combined long acting beta agonist (LABA)/long acting muscarinic antagonist (LAMA) inhaler or LABA/LAMA/inhaled corticosteroid (single or split) inhaler therapy. For Heart Failure , this will be defined as first prescription of either a) a renin-angiotensin system inhibitors, b) a beta blocker, or c) an SLGT2 inhibitor.
Measured: 12 months
Time to diagnostic testing for Heart Failure, COPD and lung cancer (echocardiography, spirometry, CT).
Measured: 12 months
Time to inpatient or outpatient specialist review and confirmation of lung cancer, COPD or Heart Failure
Measured: 12 months
Acceptability of AI-supported interpretation of Chest X-Ray for Emergency Department clinicians pre and post intervention using Theoretical Framework of Acceptability (TFA)
We will ask clinicians what they think of using AI for Chest X-Rays
Measured: Baseline and 12 months
Readmission rate within 90 days
Measured: 3 months
Proportion of patients with new diagnosis of lung cancer detected by an AI-Chest X-Ray algorithm
Measured: 12 months
Proportion of patients with new diagnosis of COPD detected by an AI-Chest X-Ray algorithm
Measured: 12 months
Proportion of patients with clinically-confirmed known diagnosis of lung cancer, Heart Failure and COPD detected by an AI-Chest X-Ray algorithm
Measured: 12 months
Percentage of Chest X-Rays not identified by an AI-CXR algorithm that have a subsequent diagnosis of Heart Failure, COPD or lung cancer within 6 months of index imaging (Emergency Department Chest X-Ray).
Measured: 6 months
Statistical analysis of model performance e.g. sensitivity, specificity, positive and negative predictive value
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Feb 2026
- Study start25 May 2026
- Primary completionJun 2027 (estimated)
- Study completionJun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- 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
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 17,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- NHS Greater Glasgow and Clyde
- Sponsor type
- University, hospital or other
- Collaborators
- University of Glasgow
- Responsible party
- Sponsor
- Investigators
- David J Lowe, Principal investigator, University of Glasgow
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07391280
- Sponsor's protocol ID
- INGN23AE079
- Other IDs
- 360783
- Acronym
- SEARCH-ED
- Status last verified
- May 2026
- First submitted
- 13 Jan 2026
- Last updated
- 1 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The dataset will be available in NHS GGC Safe Haven whereby pseudonymised access can be granted through NHS GGC LPAC approvals, as per local GGC policy.
Study at a glance
From the registry record, last updated by the sponsor on 1 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department
- ClinicalTrials.gov ID
- NCT07391280
- Acronym / sponsor ID
- SEARCH-ED / INGN23AE079
- Conditions
- Cancer, Cardiovascular, Respiratory
- Keywords
- Artificial Intelligence, Chest X-Ray, Lung Cancer, Heart Failure, COPD
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 17,000 participants (estimated)
- Lead sponsor
- NHS Greater Glasgow and ClydeUniversity, hospital or other
- Collaborators
- University of Glasgow
- Study start
- 25 May 2026 (actual)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Jun 2027 (estimated)
- First posted
- 5 Feb 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.
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital. The aim of the study is to find out if using a computer programme can help doctors diagnose heart and lung problems from chest x-rays.
We want to compare how many people are diagnosed with heart or lung problems for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Detailed description
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital.
The aim of the study is to find out if using an artificial intelligence (AI) computer programme can help doctors diagnose heart and lung problems from chest x-rays. The computer programme is made by Harrison.ai. It is approved for use in the United Kingdom (UK), United States of America (US) and the European Union (EU). Studies have been carried out previously to make sure it is safe to use and that it can detect signs of heart and lung problems.
Many people who come to ED have a chest x-ray. Chest x-rays can show signs of heart or lung problems, which might be causing a patient's symptoms. All doctors can interpret chest x-rays. However, doctors who specialise in interpreting scans (radiologists) also provide an expert report for chest x-rays, describing what they have found. It can take a long time for chest x-ray reports to come back. Sometimes, doctors might miss signs of heart or lung problems.
We want to see if using a computer programme to help doctors interpret chest x-rays could lead to more patients getting an accurate diagnosis. We want to compare how many people are diagnosed with heart or lung problems (Chronic obstructive pulmonary disease [COPD], heart failure or lung cancer) for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Patients older than 18 who have a chest x-ray in ED will be included.
Patients with chest x-rays flagged by the computer programme for heart failure or COPD will be invited to an outpatient clinic for further assessment post-discharge, providing they have not been referred for testing or had testing previously.
All patients with chest x-rays flagged for lung cancer will be reviewed and acted on by the study radiologist.
The study consists of 1) a retrospective component; 2) a prospective live trial; 3) a qualitative evaluation of acceptability to patients and clinicians, and 4) a health economic analysis.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (care provider)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Harrison CXR 'ON' Block | Experimental | Device: Harrison.ai Chest X-Ray SolutionChest X-ray AI results will be available to the treating clinician |
| Harrison CXR 'OFF' Block | Active comparator | Device: Harrison.ai Chest X-Ray SolutionChest X-ray AI results will not be available to the treating clinician |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (15 listed)
- Frontal Chest X-Ray (CXR) (AP or PA) acquired in the Queen Elizabeth University Hospital (QEUH) ED
- Patients aged 18 or over
- Appropriate meta data (DICOM) to allow for Harrison CXR processing and secondary capture report provision.
- Aged 18 or over
- Able to provide written, informed consent in English.
Key exclusion criteria (8 listed)
- Patient has requested that they are removed from the study, or has objected to the use of AI in their routine clinical care and this has been subsequently upheld by the health board.
- Patients not available to follow up, including patients i.e. whose the patient's usual care (or onward care following index admission) is out-with NHS GGC.
- Patients who have been referred to palliative care for end-stage disease, or patients with severe frailty (i.e. bedbound) will not be invited to the combined diagnostic clinic
- Unable to provide informed written consent in English
- Aged <18
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 |
|---|---|---|
| Harrison.ai Chest X-Ray SolutionThe Harrison.ai CXR module is an AI-driven clinical decision support tool that is designed to augment clinical interpretation of CXRs. It is a Class IIb CE-marked device which is able to detect up to 124 findings on a CXR. | Device | Harrison CXR 'OFF' Block, Harrison CXR 'ON' Block |
Primary outcome
Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation.
Time frame: 12 months
11 secondary outcomes
Duration of admission during index hospitalisation
Time frame: 12 months
Time to initiation of guideline-based, long-term therapy for Chronic obstructive pulmonary disease (COPD) and Heart Failure.
Time frame: 12 months
Time to diagnostic testing for Heart Failure, COPD and lung cancer (echocardiography, spirometry, CT).
Time frame: 12 months
Time to inpatient or outpatient specialist review and confirmation of lung cancer, COPD or Heart Failure
Time frame: 12 months
Acceptability of AI-supported interpretation of Chest X-Ray for Emergency Department clinicians pre and post intervention using Theoretical Framework of Acceptability (TFA)
Time frame: Baseline and 12 months
Readmission rate within 90 days
Time frame: 3 months
Proportion of patients with new diagnosis of lung cancer detected by an AI-Chest X-Ray algorithm
Time frame: 12 months
Proportion of patients with new diagnosis of COPD detected by an AI-Chest X-Ray algorithm
Time frame: 12 months
Proportion of patients with clinically-confirmed known diagnosis of lung cancer, Heart Failure and COPD detected by an AI-Chest X-Ray algorithm
Time frame: 12 months
Percentage of Chest X-Rays not identified by an AI-CXR algorithm that have a subsequent diagnosis of Heart Failure, COPD or lung cancer within 6 months of index imaging (Emergency Department Chest X-Ray).
Time frame: 6 months
Statistical analysis of model performance e.g. sensitivity, specificity, positive and negative predictive value
Time frame: 12 months
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 1 Jun 2026.
- Individual participant dataSharing planned: Yes. The dataset will be available in NHS GGC Safe Haven whereby pseudonymised access can be granted through NHS GGC LPAC approvals, as per local GGC policy.
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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.
Dervla Carroll contact
David J Lowe principal investigator
University of Glasgow
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Harrison.ai Chest X-Ray Solution in people with Cancer and Cardiovascular, run by NHS Greater Glasgow and Clyde at 1 site in United Kingdom.
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 15 inclusion and 8 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: looking for participants now, as of the sponsor's last update on 1 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
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.
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 Clea Du Toit (0141 314 4328, clea.dutoit@nhs.scot), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation. (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 1 Jun 2026.