Improving Medication Safety in Chronic Kidney Disease Using Rule-Based and Artificial Intelligence–Based Clinical Decision Support Systems: A Systematic Review of Randomized Controlled Trials

Background: Optimization of renal drug dosing to avoid drug toxicity is essential in Chronic Kidney Disease (CKD), yet prescribing errors are common. CDSS with rule-based and AI/ML based tools are used to address this safety gap; however, their impact remains uncertain. Methods: We performed a PRISMA-guided systematic review and meta-analysis of RCTs comparing rule-based or AI/ML CDSS with usual care comparators among adults with CKD or at risk of CKD-related prescribing errors. The primary outcome was a medication safety endpoint aligned with the CDSS logic (appropriate renal dosing, potentially inappropriate prescribing, and medication errors). To address heterogeneity, we supplemented meta-analysis with a structured Best Evidence Synthesis and trial-level mapping by delivery mode and workflow stage. Results: Among 20 RCTs meeting inclusion criteria, 6 provided meta-analytic data. Pooled across four trials, CDSS improved appropriate renal dosing (RR 1.76; 95% CI 1.13–2.74), but heterogeneity was extreme (I 2 = 97%) and the 95% prediction interval (0.75–4.14) crossed the null; the pooled estimate is therefore a context-dependent average rather than a transportable effect, and benefit cannot be assured in a new setting. A consistent direction of effect favoring CDSS came instead from the Best Evidence Synthesis. Documentation of CKD in electronic health records improved consistently (RR 1.19; 95% CI 1.07–1.32; I 2 = 0%). Current evidence was predominantly interruptive order-entry interventions; clinician compliance ranged 17–74% owing to alert fatigue, time constraints, and unclear system function and override processes.