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
In brief
Clinical decision support tools raise appropriate renal dosing by roughly 75% in CKD trials
A systematic review of 20 randomized trials found that rule-based or AI-driven decision support increased the rate of correctly dosed kidney-related medications (about 1.8-fold higher) compared with usual care, though results varied widely across settings. Documentation of CKD in electronic records also improved by about 20%, but alert fatigue limited clinician compliance, leaving real-world effectiveness uncertain.
- Journal
- F1000Research (Q2)
- Published
- 28 August 2026
- Study design
- Systematic review / meta-analysis of RCTs
- Evidence level
- Level 1, High (CEBM 1a)
- Authors
- Asrul Ismail, Rani Sauriasari, Arry Yanuar, Dodi Sudiana, Fonny Cokro
- PMID
- 42760898
- DOI
- 10.12688/f1000research.178438.2
Why clinicians should know about it
- Picked for Health Informatics (top studies of the week, 20 September 2026).
- Picked for Nephrology (top studies of the week, 20 September 2026).
- Picked for Pharmacology (medical) (top studies of the week, 20 September 2026).
Abstract
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.