Applying artificial intelligence to support cardiovascular risk prediction in resource-constrained settings: qualitative insights from Indonesian doctors

Cardiovascular disease (CVD) risk assessment underpins preventive care. Although machine learning models often outperform traditional risk prediction methods, adoption of artificial intelligence (AI)-based clinical decision support (CDS) tools remains limited, especially in resource-constrained settings. This study explored the sociotechnical context of CVD risk assessment in resource-constrained settings and identified potential applications for AI-based CDS. Semi-structured interviews were conducted with 27 Indonesian doctors, including general practitioners and cardiologists. Interviews examined clinical decision-making processes, challenges associated with resource constraints, and potential use cases for AI-based CDS. Data were analyzed using a deductive thematic approach informed by the SEIPS (Systems Engineering Initiative for Patient Safety) framework. Doctors reported managing high patient volumes, relying on paper-based medical records, and making largely subjective decisions due to limited access to confirmatory investigations and specialist support. Acute coronary syndrome resulting from atherosclerosis was identified as the most common and important clinical problem. Participants highlighted atherosclerotic cardiovascular disease (ASCVD) risk assessment as a promising application for AI-based CDS because it uses widely available clinical information and can support preventive care decisions. Implementation of AI-based CDS should be guided by local clinical needs and sociotechnical context. Further research is needed to evaluate effects on clinical decision-making and patient outcomes.