Abstract
The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.
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