JMIR Publications

ABSTRACT

Background:

Preventive strategies integrated with digital health and artificial intelligence (AI), have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored.

Objective:

We conducted a randomised controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention.

Methods:

In a three-way within-subject randomised design, doctors completed nine clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using either a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (e.g., random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and anti-hypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured.

Results:

102 doctors from all seven geographical regions of Indonesia participated. Most participants were 26–35 years (83%), 56% male, with a median of six years of clinical experience (IQR=4.75). AI-based CDS improved risk assessment by 27% (2(2, n=102) = 48.875, P<.001) compared to unassisted, or one additional correct risk classification for every 3.7 patients where doctors use AI (Number Needed to Treat NNT=3.7; 95% CI 2.9 to 5.2). The prescription of statins also improved by 29% (2(2, n=102)= 36.608, P<.001). In pairwise comparisons, doctors assisted with the AI-based CDS correctly assessed significantly more cases (z=-5.602, n=102, adjusted P<.001) and prescribed the appropriate statin more often (z=-4.936, adjusted P<.001, medium effect size r=.35) compared with the control. AI-assisted cases required less time (estimated marginal means 63.6 sec vs 72.8 sec, F(2, 772.8)=5.710, P=.003). However, improvements in the prescription of aspirin and anti-hypertensives did not reach statistical significance. No improvement was observed in referral decisions. Participants generally viewed AI-based CDS positively, with 79% agreeing or strongly agreeing that they would follow its recommendations and 82% indicating they would use it if given access. They believed CDS could enhance the efficiency of risk assessment, particularly in high-volume primary care settings, while noting the need to verify AI recommendations against clinical guidelines for each patient.

Conclusions:

Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of healthcare resources and doctors’ time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.