<![CDATA[Background: Nurses play a critical role in clinical decision-making, yet the process often faces challenges such as patient complexity, limited access to information, and high workload. The application of Decision Support System (DSS) or Clinical Decision Support System (CDSS) provides technological assistance to enhance patient safety and elevate the quality of nursing care. Objective: This literature review aims to analyze the implementation of DSS or CDSS in supporting clinical decisions, evaluating nurse performance, applying data mining techniques in nursing practice, and identifying challenges in their application. Methods: This study utilized a literature review approach using 15 relevant journal articles focusing on DSS or CDSS in nursing. Thematic analysis was conducted to categorize evidence into clinical decision support, performance management, data-driven decision making, and implementation barriers. Results: DSS or CDSS improves the accuracy of nursing diagnoses, enhances triage effectiveness, and reduces medication errors. Multi-criteria methods such as AHP, WP, and SAW have been used to provide objective and transparent nurse performance evaluations. Data mining techniques such as Apriori or ARM help explore patterns between interventions and clinical outcomes to support evidence-based nursing. Nevertheless, challenges remain regarding data quality, user adoption, and system integration with nursing workflows. Conclusion: DSS or CDSS has a strong potential to support the digital transformation of nursing services. However, successful implementation requires organizational support, user training, and the development of systems that are user-friendly and aligned with clinical needs.]]>
