In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models.

This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness.

This study has been reviewed and given a favourable opinion by the South Central—Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).

Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities.

NCT05389774.

Community Paramedicine (CP) is increasingly being used to provide chronic disease management for vulnerable populations in the community. CP@clinic took place in social housing buildings to support cardiovascular health and diabetes management for older adults. The purpose of this study was to examine participant perceptions of their experience with CP@clinic as well as potential ongoing programme benefits.

This descriptive qualitative study used focus groups to understand resident experiences of the CP@clinic programme. Groups were facilitated by experienced moderators using a semistructured guide. An inductive coding approach was used with at least two researchers taking part in each step of the analysis process.

Community-based social housing buildings in Ontario, Canada.

Forty-one participants from four CP@clinic sites took part in a focus group. Convenience sampling was used with anyone having taken part in a CP@clinic session being eligible to attend the focus group.

Analysis yielded six themes across two broad areas: timely access to health information and services, support to achieve personal health goals, better understanding of healthcare system (Personal Benefits); and sense of community, comfortable and familiar place to talk about health, facilitated communication between healthcare professionals (Programme Structure). Participants experienced discernible health changes that motivated their participation. CP@clinic was viewed as a programme that created connections within the building and outside of it. Participants were enthusiastic for the continuation of the programme and appreciated the consistent support to meet their health goals.

CP@clinic was successful in creating a supportive and friendly environment to facilitate health behavioural changes. Ongoing implementation of CP@clinic would allow residents to continue to build their chronic disease management knowledge and skills.

Trial registration number: NCT02152891, Clinicaltrials.gov.