Abstract

Context: County Durham faced increasing demand for accurate, accessible diagnostic spirometry across community services, alongside workforce challenges and delays in care. Therefore, the local community respiratory nursing service redesigned its model to increase capacity, workforce satisfaction, and maintain clinical quality through whole service consultation, revised skill mix and integrating artificial intelligence (AI).
Traditionally, spirometry was performed and interpreted by Band 6/7 Association for Respiratory Technology and Physiology (ARTP) certified respiratory nurse specialists, placing significant strain on the workforce. Staff resigned, sickness increased, service delayed, and waiting list increased.
A revised service model introduced Band 3, Respiratory Care and Support Workers (C&SW), performing ARTP standard spirometry, supported by AI-assisted interpretation and supervised by ARTP certified staff.
Strategy for change: C&SW received training through an endorsed integrated care board (ICB) framework as of 2024. Annual competency assessments supported quality assurance, with Band 6/7 ARTP certified nurse’s interpreting.
To further improve efficiency and enable task shifting, AI software (ArtiQ.Spiro, ArtiQ) support for executing and interpreting spirometry was introduced on 1st May 2025, ensuring consistent and high-quality diagnostic reporting.
This model promises to preserve clinical standards enabling specialist nurses to focus on complex care.
Effects of changes: By skill mixing and replacing human physiological interpretation with AI, the service instantly reduced appointment times from 60 to 45 minutes. This increased testing capacity from 300 to over 375 tests per month, freeing up 206 clinical hours at Band 6/7 and an additional 90 hours at Band 4. Patient waiting times and backlog volumes are declining, with full resolution anticipated within eight months. The service currently has zero vacancies, sickness absence has decreased by over 3 percentage points, and morale high.
Additional service evaluation data is being collected, 1st June – 31st August 2025. Qualitative comparisons between Band 3 and Band 6/7 performance and interpretation quality are underway. A survey, co-developed with the CDDFT patient experience team, is collecting feedback from primary care clinicians on the usability and quality of AI-supported spirometry. It is hypothesized that AI can safely and effectively support diagnostic delivery by lower-band staff, enabling sustainable and scalable respiratory care in the community.