Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients.
Tomasz SzymanskiRachel AshtonSara SekeljBruno PetrungaroKevin G PollockBelinda SandlerSteven ListerNathan R HillUsman FarooquiPublished in: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology (2022)
Implementation of an AF risk prediction algorithm alongside standard opportunistic screening could close the AF detection gap and prevent strokes while substantially reducing NHS and PSS combined care costs.
Keyphrases
- machine learning
- atrial fibrillation
- primary care
- end stage renal disease
- healthcare
- deep learning
- ejection fraction
- chronic kidney disease
- quality improvement
- newly diagnosed
- artificial intelligence
- heart failure
- palliative care
- patient safety
- big data
- catheter ablation
- peritoneal dialysis
- left atrial
- left atrial appendage
- oral anticoagulants
- percutaneous coronary intervention
- left ventricular
- patient reported outcomes
- label free
- chronic pain
- health insurance
- real time pcr