Big Data Analytics for Predictive Insights in Healthcare - Gates, John Doe; Yulianti, Yulianti; Pangilinan, Greian April (Pandawan Sejahtera Indonesia, 2024)
This study leverages the transformative power of big data analytics to enhance healthcare outcomes by integrating diverse data sources like electronic health records, medical imaging, and genomic data to refine predictive models that forecast disease progression and personalize treatment strategies. Employing rigorous data management and machine learning, our findings demonstrate effective risk factor identification and resource optimization, significantly reducing hospital readmissions and improving chronic disease management as evidenced by a case study at City Hospital. Despite challenges related to data security and integration, the research aligns with United Nations SDGs, particularly SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure), highlighting the role of analytics in promoting health equity and operational efficiency. The study advocates for the expanded use of big data to build a sustainable, resilient healthcare infrastructure responsive to diverse population needs, recommending that healthcare providers and policymakers utilize these insights to propel data-driven, patient-centric solutions, furthering progress towards global health goals and sustainable development. Future research should include emerging data streams like social determinants of health to enrich these models, ensuring ongoing advancements in healthcare analytics.