Cost-effectively implementing an Artificial intelligence Clinical Decision Support for individualized holistic care of chronic RESpiratory patients: The AiRES-CDS project
20 April 2025
Main Applicant – Dr Chen Wenjia, Assistant Professor, Saw Swee Hock School of Public Health, National University of Singapore (NUS)
As Singapore’s population continues to age, healthcare spending is increasingly directed toward managing chronic diseases, particularly multimorbidity within individuals. Furthermore, with the rollout of a unified electronic health records (EHR) system, Singapore is transitioning from reactive to predictive healthcare.
To bend down the growing cost curves and leveraging MOH TRUST health administrative data, this study aims to develop an Artificial-Intelligence (AI)-based population surveillance tool (AiRES model) that accurately predicts hospitalisation risks for asthma and COPD patients.
Next, combining Bayesian reasoning with expert knowledge, we will identify key factors and pathways leading to hospitalizations. This will inform the development of a clinical decision support (CDS) system that delivers timely, personalised recommendations to guide patient-centred preventive care.