Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department (SEARCH-ED)

May 28, 2026 updated by: NHS Greater Glasgow and Clyde

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Estimated)

17000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

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
  • 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

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Non-Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Harrison CXR 'ON' Block
Chest X-ray AI results will be available to the treating clinician
Device: Harrison.ai Chest X-Ray Solution
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.
Active Comparator: Harrison CXR 'OFF' Block
Chest X-ray AI results will not be available to the treating clinician
Device: Harrison.ai Chest X-Ray Solution
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
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
12 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Duration of admission during index hospitalisation
Time Frame: 12 months
12 months
Time to initiation of guideline-based, long-term therapy for Chronic obstructive pulmonary disease (COPD) and Heart Failure.
Time Frame: 12 months
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.
12 months
Time to diagnostic testing for Heart Failure, COPD and lung cancer (echocardiography, spirometry, CT).
Time Frame: 12 months
12 months
Time to inpatient or outpatient specialist review and confirmation of lung cancer, COPD or Heart Failure
Time Frame: 12 months
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
We will ask clinicians what they think of using AI for Chest X-Rays
Baseline and 12 months
Readmission rate within 90 days
Time Frame: 3 months
3 months
Proportion of patients with new diagnosis of lung cancer detected by an AI-Chest X-Ray algorithm
Time Frame: 12 months
12 months
Proportion of patients with new diagnosis of COPD detected by an AI-Chest X-Ray algorithm
Time Frame: 12 months
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
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
6 months
Statistical analysis of model performance e.g. sensitivity, specificity, positive and negative predictive value
Time Frame: 12 months
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

NHS Greater Glasgow and Clyde

Collaborators

University of Glasgow

Investigators

  • Principal Investigator: David J Lowe, University of Glasgow

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 25, 2026

Primary Completion (Estimated)

June 1, 2027

Study Completion (Estimated)

June 1, 2027

Study Registration Dates

First Submitted

January 13, 2026

First Submitted That Met QC Criteria

January 29, 2026

First Posted (Actual)

February 5, 2026

Study Record Updates

Last Update Posted (Actual)

June 1, 2026

Last Update Submitted That Met QC Criteria

May 28, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • INGN23AE079
  • 360783 (Other Identifier: IRAS)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

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.

IPD Sharing Time Frame

After publication of results

IPD Sharing Access Criteria

Bone fide collaboration request

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • CSR

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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