Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial
Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial
Introduction: spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation).
Methods and analysis: a parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups.
Ethics and dissemination: this study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders.
Trial registration number: NCT05933694.
Humans, Artificial Intelligence, Decision Support Systems, Clinical, Primary Health Care, Randomized Controlled Trials as Topic, Software, Spirometry/methods, United Kingdom
Doe, Gillian
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El-Emir, Ethaar
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Edwards, George D.
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Topalovic, Marko
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Evans, Rachael A.
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Russell, Richard
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Sylvester, Karl P.
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Van Orshoven, Karolien
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Sunjaya, Anthony P.
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Scott, David A.
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Prevost, A. Toby
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Harvey, Jennifer
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Taylor, Stephanie J.C.
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Hopkinson, Nicholas S.
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Kon, Samantha S.
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Jarrold, Ian
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Spain, Nannette
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Banya, Winston
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Man, William D.-C.
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1 July 2024
Doe, Gillian
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El-Emir, Ethaar
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Edwards, George D.
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Topalovic, Marko
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Evans, Rachael A.
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Russell, Richard
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Sylvester, Karl P.
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Van Orshoven, Karolien
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Sunjaya, Anthony P.
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Scott, David A.
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Prevost, A. Toby
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Harvey, Jennifer
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Taylor, Stephanie J.C.
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Hopkinson, Nicholas S.
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Kon, Samantha S.
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Jarrold, Ian
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Spain, Nannette
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Banya, Winston
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Man, William D.-C.
ef30a187-66e3-4781-b835-b034dbd1efda
Doe, Gillian, El-Emir, Ethaar, Edwards, George D., Topalovic, Marko, Evans, Rachael A., Russell, Richard, Sylvester, Karl P., Van Orshoven, Karolien, Sunjaya, Anthony P., Scott, David A., Prevost, A. Toby, Harvey, Jennifer, Taylor, Stephanie J.C., Hopkinson, Nicholas S., Kon, Samantha S., Jarrold, Ian, Spain, Nannette, Banya, Winston and Man, William D.-C.
(2024)
Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.
BMJ Open, 14 (6), [e086736].
(doi:10.1136/bmjopen-2024-086736).
Abstract
Introduction: spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation).
Methods and analysis: a parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups.
Ethics and dissemination: this study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders.
Trial registration number: NCT05933694.
Text
e086736.full
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More information
Accepted/In Press date: 12 June 2024
e-pub ahead of print date: 1 July 2024
Published date: 1 July 2024
Keywords:
Humans, Artificial Intelligence, Decision Support Systems, Clinical, Primary Health Care, Randomized Controlled Trials as Topic, Software, Spirometry/methods, United Kingdom
Identifiers
Local EPrints ID: 508475
URI: http://eprints.soton.ac.uk/id/eprint/508475
ISSN: 2044-6055
PURE UUID: 9a3992d6-bebe-436b-b4e2-4bd7d6659f26
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Date deposited: 23 Jan 2026 17:31
Last modified: 24 Jan 2026 03:08
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Contributors
Author:
Gillian Doe
Author:
Ethaar El-Emir
Author:
George D. Edwards
Author:
Marko Topalovic
Author:
Rachael A. Evans
Author:
Richard Russell
Author:
Karl P. Sylvester
Author:
Karolien Van Orshoven
Author:
Anthony P. Sunjaya
Author:
David A. Scott
Author:
A. Toby Prevost
Author:
Jennifer Harvey
Author:
Stephanie J.C. Taylor
Author:
Nicholas S. Hopkinson
Author:
Samantha S. Kon
Author:
Ian Jarrold
Author:
Nannette Spain
Author:
Winston Banya
Author:
William D.-C. Man
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