Effectiveness of Clinical Decision Support Systems in Reducing Medication Errors across Multiple Specialties
Year: 2025
Type: article
Abstract: Background: Clinical Decision Support Systems (CDSS) have emerged as a promising technological intervention to reduce medication errors in healthcare settings. This study presents a comprehensive analysis of CDSS implementation across multiple medical specialties, examining their effectiveness in reducing medication errors. Aims and Objectives: The aim of this study is to evaluate the impact of CDSS on medication error reduction, clinical workflow integration, and patient outcomes. The specific objectives are to assess the overall reduction in medication errors, examine the effect of CDSS on clinical workflow, and identify challenges and factors influencing CDSS effectiveness across various specialties. Materials and Methods: This study is a systematic review of 87 studies spanning primary care, cardiology, pediatrics, geriatrics, and oncology. The studies were analyzed to evaluate the effectiveness of CDSS in reducing medication errors, with a focus on integration quality, user interface design, and alert fatigue mitigation. Results: CDSS implementation was associated with a significant overall reduction in medication errors (relative risks = 0.47, 95% confidence interval: 0.38–0.58, P < 0.001) across specialties. Specialty-specific analyses revealed the greatest effectiveness in geriatrics (65.3% error reduction) and oncology (61.7% error reduction), with moderate effectiveness in primary care (42.1%), cardiology (39.8%), and pediatrics (54.2%). Conclusion: This research provides evidence-based insights for healthcare organizations implementing CDSS and highlights the need for specialty-specific customization to maximize error reduction potential. Implementation challenges included workflow disruption, alert fatigue, and interoperability issues.
Authors: Abinash Biswaranjan Mohapatra, Aditya Sharma
Institution: Teerthanker Mahaveer University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411219634