Objectives To develop recommendations to inform development and integration of predictive digital health and artificial intelligence tools in primary care. Methods Recommendation development involved two

Objectives To develop and evaluate a machine learning (ML) model that predicts Crohn’s disease (CD) patients responsible for the top quartile of healthcare spending. Methods

Objectives Interoperability, the seamless exchange and use of data across digital health systems, is essential for integrated, efficient healthcare delivery. However, evidence on its adoption

Objectives Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potential, often outperforming traditional severity-of-illness scoring

Objectives Evaluate the technical integration and usability of an intraoperative predictive machine learning model for colorectal anastomotic leakage within the Epic electronic health records (EHRs)

Objectives Lung cancer is the leading cause of cancer-related mortality worldwide, with poor prognosis largely due to late-stage diagnosis. Current screening methods such as low-dose