Spirometry Interpretation Performance of Primary Care Clinicians With/Without AI Software (SPIRO-AID)

Royal Brompton & Harefield NHS Foundation Trust

Status: Enrolling

Conditions: Lung Disease

Treatments: Other: Artificial Intelligence-powered Spirometry Interpretation Report

Study type: Interventional

Funder types: Other

Identifiers

NCT05933694

323361

Details and patient eligibility

About

To evaluate whether an artificial intelligence decision support software (ArtiQ.Spiro) improves the diagnostic accuracy of spirometry interpreted by primary care clinicians, as measured by Clinician Diagnostic Accuracy (vs Reference Standard).

Full description

This is a randomised controlled study to evaluate the effects of AI support software on the performance of primary care clinicians in the interpretation of spirometry. Clinicians will be provided with a clinical dataset of 50 entirely anonymous, previously recorded real-world spirometry records to interpret and will be asked to complete specific questions about diagnosis and quality assessment. The records will be randomly selected from a database comprising spirometry records from 1122 patients undergoing spirometry in primary care and community -based respiratory clinics in Hillingdon borough between 2015-2018.

Participating clinicians will be allocated at random to receive either spirometry records alone or spirometry records with the addition of an AI spirometry interpretation eport. The clinical spirometry records will be de-identified (name, date of birth, address, postcode, occupation, GP, medications data removed), by a member of the clinical care team.

Study participants (participating clinicians) will independently examine the same 50 spirometry records through an online platform. For each spirometry record, the primary care clinician participant will answer questions about technical quality, pattern interpretation, preferred diagnosis, differential diagnosis and self-rated confidence with these answers.

The study statistician will be blinded to treatment allocation up to completion of analysis and interpretation.

The reference standards for spirometry technical quality and pattern interpretation will be made by a senior experienced respiratory physiologist but without access to AI report.

The reference standard for diagnosis will be made by a panel of three respiratory specialists from the clinical care team with access to medical notes and results of relevant investigations but without access to AI report.

Enrollment: 228 estimated patients

Sex: All

Ages: 18 to 99 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

1. Clinicians working in primary care (for at least 50% of their job plan) in the UK, who refer for or perform spirometry (typically GP, practice nurse)
2. Able to access spirometry traces on study platform
3. Provide written informed consent via study platform

Exclusion criteria

1. Clinicians who have completed specialist training in respiratory medicine and recognised by the General Medical Council with a right to practise as a NHS consultant in respiratory medicine

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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228 participants in 2 patient groups

Control

No Intervention group

Description:

Participants to report 50 spirometry records alone

Intervention

Experimental group

Description:

Participants report the same 50 spirometry records provided in the control arm with an artificial intelligence-powered spirometry interpretation report

Treatment:

Other: Artificial Intelligence-powered Spirometry Interpretation Report