To estimate condition-specific patient travel distances and associated carbon emissions across common chronic diseases in routine National Health Service (NHS) care, and to assess the potential carbon savings of modal shifts in transportation.

Retrospective population-based cohort study.

NHS Greater Glasgow and Clyde, Scotland.

6599 patients aged 50–55 years at diagnosis, including cardiovascular disease (n=1711), epilepsy (n=1044), cancer (n=716), rheumatoid arthritis (RA; n=172) and a matched control group based on age, sex and area-level deprivation (n=2956).

Annual home-to-clinic distances and associated carbon emissions modelled under four transport modes (petrol car, electric car, bus, train) across five time points: 2-year prediagnosis, diagnosis year and 2-year postdiagnosis.

Mean annual travel distances to hospital varied by condition and peaked at diagnosis. Patients with cancer had the highest travel distances (161 km/patient/year for men; 139 km/patient/year for women), followed by RA (approximately 78 km/patient/year). The matched control group travelled 2/patient/year to 8.0 kg CO₂/patient/year. Bus travel resulted in intermediate emissions, estimated between 10.5 and 8.0 kg CO₂/patient. When travel was modelled using electric vehicles, emissions dropped between 3.5 and 2.7 kg for all conditions. Train travel produced similarly low emissions. Reducing petrol car travel from 100% to 60% lowered emissions up to 6.6 kg CO₂/patient.

Condition-specific estimates of healthcare-related travel emissions provide baseline understanding of the opportunities and challenges for decarbonising healthcare. Emission reduction is most achievable through modal shift, yet such shifts depend on factors beyond NHS control—such as transport infrastructure, digital access and social equity. Multisectoral strategies, including targeted telemedicine and integrated transport and urban planning, are critical to achieving net-zero healthcare while maintaining equitable access to care.

Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports.

The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions.

Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.