Ai-enhanced clinical decision support reduces medication errors and adverse drug events in a multicenter teaching...

Medication errors remain a leading source of preventable harm in hospitalized patients, contributing to adverse drug events (ADEs), prolonged hospital stay, and avoidable healthcare costs. Although clinical decision support systems (CDSS) integrated with electronic health records (EHRs) have demonstrated potential to reduce prescribing errors, rigorous multicenter randomized evidence from low- and middle-income countries (LMICs), as classified by the World Bank income criteria, remains scarce.

To evaluate the effect of an AI-enhanced, EHR-integrated CDSS on medication error rates and ADE incidence in hospitalized adults, with additional assessment of alert performance, clinician adoption, and cost-effectiveness.

We conducted a prospective, parallel-arm, randomized controlled trial (RCT) across four tertiary-care teaching hospitals in Tashkent, Uzbekistan (January 2022 - August 2023). Adult inpatients were randomized 1:1 to CDSS-assisted care (MedGuard-UZ v1.3) or standard care. Primary outcomes were medication error rate per 1,000 patient-days and ADE incidence per 100 admissions. Analyses followed the intention-to-treat (ITT) principle.

Among 2,384 randomized patients, the CDSS group demonstrated a 49.2% reduction in medication error rates (3.47 vs. 6.83 per 1,000 patient-days; p < 0.001) and a 47.2% reduction in ADE incidence (4.7 vs. 8.9 per 100 admissions; p < 0.001). Overall alert acceptance was 73.6%, with allergy/contraindication alerts achieving 95.5%. Clinician adoption rose from 42.1% to 88.7% daily active users over 12 months. Length of stay was significantly shorter in the CDSS group (7.1 vs. 7.8 days; p = 0.002), as were 30-day readmissions (11.2% vs. 13.7%; p = 0.041). Estimated return on investment was 489% over 12 months.

AI-enhanced CDSS integration was associated with substantially improved medication safety and selected hospital outcomes in a multicenter LMIC tertiary-care setting. The MedGuard-UZ AI project materials are publicly available at https://github.com/Shakarbayev/MedGuard-UZ. For peer-review reproducibility, the repository state corresponding to this revision has been archived under the tagged release v1.0.0-ijmedi-rct (tag commit: f796c3d). MedGuard-UZ v1.3 denotes the internal trial system version, whereas v1.0.0-ijmedi-rct (tag commit: f796c3d) identifies the public revision archive supporting evaluation of the AI project component in resource-constrained health systems.