E ISSN: 2583-049X
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International Journal of Advanced Multidisciplinary Research and Studies

Volume 4, Issue 6, 2024

Power BI-Based Clinical Decision Support System for Evidence-Based Nurse Decision-Making



Author(s): Akonasu Qudus Hungbo, Christiana Adeyemi, Opeoluwa Oluwanifemi Ajayi

DOI: https://doi.org/10.62225/2583049X.2024.4.6.4777

Abstract:

The integration of data analytics into healthcare has led to significant improvements in clinical decision-making, particularly for nursing professionals who require timely, evidence-based insights to enhance patient care. This study presents the development and implementation of a Power BI-based Clinical Decision Support System (CDSS) designed to support evidence-based nurse decision-making across diverse clinical settings. Leveraging Power BI's interactive visualization capabilities and real-time data processing, the system enables nurses to access patient records, diagnostic results, and treatment history through dynamic dashboards, fostering a data-driven approach to care delivery. The Power BI-based CDSS aggregates data from electronic health records (EHRs), laboratory systems, and nursing documentation, transforming them into actionable insights using customized key performance indicators (KPIs), risk stratification models, and clinical pathways. This approach ensures that nursing interventions align with current clinical guidelines, promoting consistency and quality in patient outcomes. Furthermore, the system provides predictive analytics that identify early warning signs of patient deterioration, enabling proactive intervention and reducing avoidable complications. A mixed-methods evaluation involving pilot deployment in surgical and medical wards demonstrated increased nurse confidence in clinical decisions, improved interprofessional communication, and reduced response times to patient needs. User feedback highlighted the intuitive design, adaptability, and ease of use as critical success factors. Challenges identified include the need for training in data interpretation and the importance of maintaining data quality and interoperability across systems. This research underscores the potential of Power BI-based tools to transform traditional nursing workflows into intelligent, evidence-informed decision models. By integrating clinical evidence with user-friendly analytics, the CDSS enhances critical thinking, supports real-time decision-making, and aligns nursing practice with organizational quality improvement goals. Future research should explore large-scale implementation, integration with artificial intelligence algorithms, and longitudinal impact on patient outcomes and cost-effectiveness.


Keywords: Power BI, Clinical Decision Support System (CDSS), Evidence-Based Practice, Nursing Informatics, Real-Time Analytics, Electronic Health Records (EHR), Data Visualization, Patient Outcomes, Predictive Analytics, Nursing Decision-Making

Pages: 2653-2668

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