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In the face of an overwhelming influx of data, the healthcare sector is confronted with a critical question: How can it effectively leverage the capabilities of Big Data analytics (BDA) to attain a sustainable competitive advantage? This inquiry is not only timely but also crucial for an industry facing increasing demands amid constrained resources. To address this pivotal issue, the present study utilizes a composite of rigorous methodologies—including the best worst method, interpretive structural modeling, and interpretive structural—cross impact matrix multiplication—to assemble a hierarchical relationship among 4 main enablers and 12 corresponding subenablers. The study's findings reveal that “Technological” and “Organizational” enablers serve as key elements for successful BDA implementation, while “Socio-Cultural” and “Market and Customer” enablers play secondary yet important roles. This hierarchical structure serves as a foundational guide for policy formulation, enabling healthcare organizations to strategize with increased precision. The study strongly advocates for a strategic shift in policy: healthcare organizations should develop comprehensive frameworks that focus on these principal enablers and employ robust metrics for ongoing evaluation. By adopting this approach, organizations can more effectively harness BDA capabilities, thereby not only enhancing their competitive positioning but also improving operational efficiency and patient care outcomes. Through its rigorous methodological approach and actionable recommendations, this research contributes a significant academic reference to the rapidly expanding discourse on BDA-enabled healthcare.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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