Introduction: Spirometry services to diagnose lung disease in primary care are slowly restarting in England post-pandemic; evidence regarding best practice is limited. 

Aim: Explore perspectives on spirometry provision and 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, 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.

Results: 28 participants (mean [SD], 21.6 [9.4] years? clinical experience) were interviewed April-June 2022. Participants included 25 clinicians and 3 commissioners; 8 held regional and/or national respiratory network roles. 

Four themes were identified: 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.

Conclusion: 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. Family doctors in particular were keen to explore its role in supporting clinicians in quality assessment and interpretation of spirometry. It was evident that validation of the software and trust in the process would be key for future implementation.