Define and recall the meaning, purpose, and types of Clinical Decision Support Systems (CDSS) used in healthcare. Describe how Big Data integration enhances CDSS capabilities for real-time decision-making and personalized care. Examine how predictive analytics improves diagnostic accuracy, early disease detection, and resource optimization. Explain how CDSS contributes to better patient outcomes through evidence-based recommendations and workflow efficiency. Apply EHR data within CDSS tools to assist healthcare providers in making timely and accurate clinical decisions. Design a basic CDSS model or workflow demonstrating integration of data sources, inference engines, and user interfaces. Evaluate the impact of CDSS implementation on healthcare quality, patient safety, and operational effectiveness.

Foundational knowledge of healthcare data management and analytics. Basic understanding of EHR systems and health IT infrastructure. Awareness of data governance and healthcare regulations (HIPAA, GDPR)

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This unit is ideal for: Healthcare professionals and clinicians seeking to leverage data for decision-making. Health informatics and IT practitioners implementing CDSS within healthcare institutions. Clinical data scientists and analysts applying AI to improve care quality. Healthcare administrators and policy makers aiming to optimize clinical workflows and outcomes. Students and researchers in healthcare data analytics or biomedical informatics.