Abstract

RATIONALE: Respiratory disease is one of the leading causes of emergency hospital admissions and mortality in the UK. However, diagnostic pathways in respiratory care remain suboptimal. Currently, fewer than 6% of practice nurses in the UK are registered on the National Spirometry Register, with workforce limitations representing a major barrier to delivering effective spirometry services in primary care. Literature indicates that only 13.4% of spirometry performed in primary care settings meets international quality standards. ArtiQ.Spiro is an AI-based software solution designed to support primary care practitioners in conducting and interpreting spirometry. This study evaluates whether healthcare assistants using ArtiQ.Spiro can produce high-quality spirometry data, potentially expanding capacity for respiratory diagnostics in primary care. METHODS: A healthcare assistant (HCA) unregistered on the National Spirometry Register was provided with local training and competency assessment. The HCA conducted spirometry sessions using ArtiQ.Spiro as an enabler for high quality spirometry for a period of four months. For each session, the Forced Expiratory Volume in 1 second (FEV1) and Forced Vital Capacity (FVC) quality grades provided by ArtiQ.Spiro were recorded, along with the average number of trials required to achieve optimal quality. RESULTS: Spirometry was conducted on 19 patients, with bronchodilator response testing completed for 12 of them, totaling 31 sessions evaluated. The majority of sessions achieved high-quality ratings: FEV1 was rated as grade A in 29 sessions (94%), and FVC was rated as grade A in 22 sessions (71%) and grade B in 6 sessions (19%). A median of 3 trials per session was required to reach these quality standards. CONCLUSIONS: Our findings demonstrate that healthcare assistants, even without National Spirometry Register accreditation, can produce high-quality spirometry data when supported by AI tools like ArtiQ.Spiro. By enabling lower-band personnel to perform quality spirometry, AI-driven upskilling can expand workforce capacity and reduce access barriers to respiratory diagnostics in primary care.