NCT05933694

Spirometry Interpretation Performance of Primary Care Clinicians With/Without AI Software

A Randomized Controlled Trial Comparing Performance of Primary Care Clinicians in the Interpretation of SPIROmetry With or Without Artificial Intelligence Decision Support Software

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 228 (estimated)
• Sponsor: Royal Brompton & Harefield NHS Foundation Trust · Academic / Other
• Sex: All
• Age: 18 Years – 99 Years
• Healthy volunteers: Accepted

Summary

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).

Detailed 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.

Conditions

• Lung Disease

Interventions

Type	Name	Description
OTHER	Artificial Intelligence-powered Spirometry Interpretation Report	A report generated by artificial intelligence powered software that assessed technical quality of spirometry and estimates the diagnostic probability of six categories: COPD/Asthma/ILD/ Normal/Other obstructive/Other Unidentified

Timeline

• Start date: 2023-06-27
• Primary completion: 2024-06-27
• Completion: 2024-09-30
• First posted: 2023-07-06
• Last updated: 2024-02-16

Locations

1 site across 1 country: United Kingdom