Atrial Fibrillation (AF) is the most common arrhythmia worldwide affecting an estimated 5% of people over the age of 65 and is a leading cause of stroke and heart failure. Identification of patients at risk allows preventative measures and treatment before these complications occur. Conventional risk prediction models are static, do not have flexibility to incorporate dynamic risk factors and possess only modest predictive value. Artificial intelligence and machine learning-powered health virtual twin technology offer transformative methods for risk prediction and guiding clinical decisions.

In this prospective observational study, 1200 patients will be recruited in two tertiary centres. Patients hospitalised with acute illnesses (sepsis, heart failure, respiratory failure, stroke or critical illness) and patients having undergone high-risk surgery (major vascular surgery, upper gastrointestinal surgery and emergency surgery) will be monitored with a patch-based remote wireless monitoring system for up to 14 days. Clinical and electrocardiographic data will be used for modelling the risk of new-onset AF. The primary outcome is episodes of AF >30 s and will be described as ratio of episodes/patient and as percentage of patients having episodes of AF. Secondary outcomes include 30-day and 90-day readmission rates and complications of AF.

The aim of this study is to generate data for the development and validation of health virtual twins predicting onset of AF in an at-risk population. The intelligent monitoring to predict atrial fibrillation (NOTE-AF) study is part of the TARGET project, a Horizon Europe funded programme which includes risk prediction, diagnosis and management of AF-related stroke (https://target-horizon.eu/).

The study has received approval by the Health Research Authority and the National Research Ethics Service (REC reference 24/NW/0170, IRAS project ID: 342528) in the UK and has been registered on clinicaltrials.gov (NCT06600620). Results will be disseminated as outlined in the TARGET protocol to communicate project ideas, activities and results to diverse audiences.

NCT06600620.

To explore and describe how healthcare professionals within the oncological outpatient setting perceive quality of care.

A qualitative, descriptive design with a phenomenographic approach was used, with focus group discussions as the means of data collection.

Primary care in oncological outpatient units in four hospitals in Sweden.

Through purposive sampling, 20 healthcare professionals entered and completed the study by participating in four focus groups, five participants in each group. Inclusion criteria were assistant nurses, nurses or physicians delivering treatment and care with radiation and/or anticancer drugs in oncological outpatient units. Excluded were healthcare professionals who had worked less than 3 months at the oncological outpatient unit.

Two descriptive categories emerged from the data: ‘The professional’s personal ability for good care’ and ‘The structural conditions for good care’. These categories consist of descriptions of quality of care being perceived as a good meeting with patients, patient participation, continuity, accessibility and care grounded in science.

According to the healthcare professionals, quality of care relies on organisational structures in combination with a professional and personal interaction between the patients and the healthcare professionals. Knowledge about what healthcare professionals believe constitutes quality of care should therefore be highly valuable to policymakers and hospital management.