Using Clinical Prediction Models to Improve Treatment for Patients With Chronic Obstructive Pulmonary Disease (COPD)

Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease of the lungs that affects more than 2.5 million Canadians. Patients with COPD experience episodes of lung attacks (or exacerbations). During these attacks, patients experience an intense increase in symptoms, such as breathlessness and cough. It is challenging to decide which patients should be put on treatments that would reduce the risk of such lung attacks. The digitization of health records in many clinics and hospitals means complex risk prediction algorithms can be used to predict the risk of lung attacks to enable personalized care. In this study, our team will implement a risk prediction tool (called ACCEPT) into the electronic health records in two teaching hospitals in Vancouver, British Columbia (BC), Canada. A clinical study will be conducted to evaluate if the use of this tool results in patients with COPD receiving better care with better outcomes, and if they are more satisfied with the care they are receiving.