The Role of AI-Based Clinical Decision Support Systems (AI-CDSS) in Modern Pharmacy Practice

Abstract

Artificial intelligence-based clinical decision support systems (AI-CDSS) are emerging tools leveraging machine learning and healthcare data to aid pharmacists in managing clinical complexity. They offer real-time insights for identifying high-risk prescriptions, preventing drug interactions, and aiming to improve patient outcomes. While the strength of AI-CDSS lies in data-driven support, human-centered design focusing on trust and usability is crucial for adoption. Applications of AI-CDSS extend to patient care (e.g., disease prediction, medication adherence) and pharmacy practice, including prioritizing prescription reviews (e.g., Lumio Medication) and early detection of cognitive impairment. Although AI-CDSS shows potential in optimizing pharmacy workflows and identifying drug-related issues, direct evidence of improved patient outcomes within the pharmacy remains limited. Implementation faces challenges like technical constraints and workflow misalignment. This paper synthesizes current research, highlighting the potential of AI-CDSS to transform pharmacy practice while acknowledging the need for further investigation into their impact on patient outcomes and the practical barriers to their widespread use.