TY - JOUR
T1 - Medication Safety Analytics in Clinical Systems: Reconciliation Logic, Error Taxonomies, and Deployment Constraints
AU - Oliver Grant
AU - David Clark
AU - Sophia Nguyen
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2022
VL - 2
IS - 2
SP - 18
N2 - The integration of artificial intelligence (AI) into healthcare systems has transformed clinical analytics, particularly in improving medication safety through advanced reconciliation processes, structured error taxonomies, and careful deployment strategies. This narrative review examines how AI-driven analytics embedded within clinical infrastructures can reduce medication-related risks in hospital and ambulatory care settings. AI technologies such as natural language processing and machine learning enable automated detection of medication discrepancies by analyzing electronic health records and identifying inconsistencies that may be overlooked by manual review.AI systems also support the classification and prediction of medication errors, including prescribing mismatches and administration failures, allowing clinical decision support systems to identify high-risk prescriptions and support safer prescribing practices. In addition, AI contributes to closed-loop healthcare systems where analytics provide real-time decision support across the data lifecycle, from information ingestion to post-intervention feedback.Despite these benefits, several deployment constraints remain, including data quality limitations, interoperability challenges, and ethical concerns related to bias and governance. These factors highlight the importance of robust system design and transparent AI models to ensure safe and equitable implementation. Furthermore, AI can support standardized error taxonomies and pharmacovigilance through structured analytical frameworks that improve reporting and monitoring of adverse events.Overall, this review positions AI as a central component of adaptive clinical systems capable of strengthening medication safety. However, its effectiveness depends on addressing technical, operational, and regulatory barriers. Continued interdisciplinary collaboration will be essential to refine AI-enabled clinical analytics and support safer, more efficient healthcare systems.
UR - https://cirpublications.com/v886777840
ER -