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.