Perceptions of healthcare professionals on the use of a risk prediction model to inform atrial fibrillation screening: qualitative interview study in English primary care
Year: 2025
Type: article
Abstract: OBJECTIVES: There is increasing interest in guiding atrial fibrillation (AF) screening by risk rather than age. The perceptions of healthcare professionals (HCPs) towards the implementation of risk prediction models to target AF screening are unknown. We aimed to explore HCP perceptions about using risk prediction models for this purpose, and how models could be implemented. DESIGN: Semistructured interviews with HCPs engaged in the Future Innovations in Novel Detection of AF (FIND-AF) study. Data were thematically analysed and synthesised to understand barriers and facilitators to AF screening and guiding screening using risk assessment. SETTING: Five primary care practices in England taking part in the FIND-AF study. PARTICIPANTS: 15 HCPs (doctors, nurses/nurse practitioners, healthcare assistants, receptionists and practice managers). RESULTS: Participants knew the health implications of AF and were supportive of the risk prediction models for AF screening. Four main themes developed: (1) health implications of AF, (2) positives and negatives of risk prediction in AF screening, (3) strategies to implement a risk prediction model and (4) barriers and facilitators to risk-guided AF screening. HCPs thought risk-guided AF screening would improve patient outcomes by reducing AF-related stroke, and this outweighed concerns over health anxiety and the impact on workload. Pop-up notifications and practice worklists were the main suggestions for risk-guided screening implementation and for this to be predominantly run by administrative staff. Many recommended the need for educating staff on AF and the prediction models to help aid the implementation of a clear protocol for longitudinal follow-up of high-risk patients and communication of risk. CONCLUSIONS: Overall, HCPs participating in the FIND-AF study were supportive of using risk prediction to guide AF screening and willing to take on extra workload to facilitate risk-guided AF screening. The best pathway design and the method of how risk is communicated to patients require further consideration. TRIAL REGISTRATION NUMBER: NCT05898165.
Source: BMJ Open
Authors: Ellen Hamilton, Lydia Shone, Catherine Reynolds, Jianhua Wu, Ramesh Nadarajah, Chris Gale
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,839
Awards: G1230673942, G1412811091, The BHF Bristol Myers Squibb Cardiovascular Catalyst Award: Future innovations in novel detection of atrial fibrillation (FIND-AF) – A proof-of-concept clinical implementation study of an artificial intelligence algorithm to increase detection rates of atrial fibrillation in the general population, Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF): Enabling regulatory approval for use of the FIND-AF machine learning algorithm in the NHS
OpenAlex ID: W4407150475