Conectar
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.
Artificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS).
A qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out.
NHS and UK higher education institutes.
Thirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings.
Four core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff.
This study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability.
NCT05028179;
To generate, test and refine programme theories that emerged from a rapid realist review investigating practising UK Nurses' and Midwives' experiences of effective leadership strategies during the COVID-19 pandemic.
The realist review of literature generated six tentative theories of healthful leadership practices reflecting, working with people's beliefs and values; being facilitative; multiple means of communication and; practical support. The review yielded little insight into the actual impact of the leadership approaches advocated.
A realist study, informed by person-centredness using mixed-methods. Online survey (n = 328) and semi-structured interviews (n = 14) of nurses and midwives across the UK in different career positions/specialities. Quantitative data analysed using descriptive statistics and exploratory factor analysis. Framework analysis for qualitative data using context (C), mechanism (M), outcome (O) configurations of the tentative theories.
Three refined theories were identified concerning: Visibility and availability; embodying values and; knowing self. Healthful leadership practices are only achievable within organisational cultures that privilege well-being.
Leaders should intentionally adopt practices that promote well-being. ‘Knowing self’ as a leader, coaching and mentoring practice development is important for leadership development.
Nurses who feel valued, heard, cared for and safe are more likely to remain in clinical practice. Job satisfaction and being motivated to practice with confidence and competence will impact positively on patient outcomes.
The study addresses the role of leadership in developing healthful workplace cultures. The main findings were six leadership practices that promote healthful cultures. The research will have an impact on strategic and clinical leaders, nurses and midwives.
This study used EQUATOR checklist, RAMASES II as reporting standards for realist evaluations.
No patient or public contribution.
FreshRSS 