Understanding and Optimizing PM-JAY: A Case Study of Balasore District, Odisha

The growing reliance on big data in healthcare presents significant opportunities for improving service delivery, especially in developing countries like India. However, challenges such as data heterogeneity, lack of integration, and limited access to suitable analytics tools hinder its effective utilization. This study proposes a conceptual framework for selecting appropriate analytics tools to enhance the usefulness of healthcare big data in the Indian context. A qualitative, interpretivist methodology was adopted, drawing from a decade of relevant literature. The framework comprises three key levels: influencing factors (technical and non-technical), data networks (sources and types), and analytics tools (predictive, prescriptive, descriptive, and diagnostic). These components are interlinked to guide healthcare institutions in making informed choices about analytics tools based on their data environment and operational needs. The study offers valuable insights into the alignment of healthcare and information systems, emphasizing the role of both human and technological factors. It contributes to the academic discourse on health informatics and provides a foundational model for policy formulation and empirical validation in developing nations. The framework is particularly relevant for improving clinical decision-making, optimizing healthcare operations, and enabling scalable, data-driven practices.