The landscape of spirometry services in England and the potential for Artificial Intelligence decision support software in primary care; a qualitative study.

15 May 2023
Introduction: Spirometry services to diagnose and monitor lung disease in primary care were severely affected by the pandemic. Services are slowly restarting in England, however, evidence regarding best practice is limited. We aimed to explore perspectives on spirometry provision in primary care, and the potential for Artificial Intelligence (AI) decision support software to aid quality and interpretation in future pathways. Methods: Semi-structured interviews were conducted with key stakeholders in spirometry services across England. Participants were recruited by snowball sampling. Interviews explored the pre-pandemic delivery of spirometry, restarting of services and perceptions of the role of AI. Transcripts were analysed using thematic analysis supported by NVivo software. Results: 28 participants (mean [SD], 21.6 [9.4, range 3-40] years’ clinical experience) were interviewed between April and June 2022. Participants included clinicians (n=25) and commissioners (n=3); eight held regional and/or national respiratory network advisory roles. Four themes were identified (Figure 1): 1) Historical challenges in spirometry provision; 2) Inequity in post-pandemic spirometry provision and challenges to restarting spirometry in primary care; 3) Future delivery closer to patients’ homes by appropriately trained staff; 4) The potential for AI to have supportive roles in spirometry. There was an overall sense of urgent need to improve services. Regardless of the details of a spirometry service model, all participants expressed the importance of spirometry being accessible for patients. Despite some hesitancy around AI, Family doctors in particular were keen to explore its potential. Discussion: Stakeholders highlighted historic challenges and the damaging effects of the pandemic contributing to inequity in provision of spirometry nationally. Overall stakeholders were positive about the potential of AI to support clinicians in quality assessment and interpretation of spirometry. However, it was evident that validation of the software must be sufficiently robust for clinicians and healthcare commissioners to have trust in the process.

Resource information

Respiratory conditions
  • COPD
Respiratory topics
  • Spirometry
Type of resource
Munich 2023
Gillian Doe1, Steph Taylor2, Marko Topalovic3, Richard Russell4, Rachael Evans1, Julie Maes3, Karolien Van Orshoven3, Anthony Sunjaya5, David Scott6, Toby Prevost7, Ethaar El-Emir8, Jennifer Harvey8, Nick Hopkinson9, Samantha Kon8, Suhani Patel8,9, Ian Jarrold11, Nannette Spain12, William Man8,9,10, Ann Hutchinson13 1Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom, 2Wolfson Institution of Population Health, Queen Mary University, London, United Kingdom, 3ARTIQ, Leuven, Belgium, 4Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom, 5The George Institute for Global Health, UNSW , Sydney, Australia, 6Southampton Health Technology Assessments Centre, University of Southampton, Southampton, United Kingdom, 7Kings College London, London, United Kingdom, 8Harefield Respiratory Research Group, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom, 9National Heart & Lung Institute, Imperial College London, London, United Kingdom, 10Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom, 11Asthma + Lung UK, London, United Kingdom, 12Patient and public involvement representative, London, United Kingdom, 13Wolfson Palliative Care Research Centre, Hull and York Medical School, University of Hull, Hull, United Kingdom