Identifying, ranking and analyzing obstacles to big data analytics implementation in the healthcare industry using an ISM approach

Big data analytics enhances patient outcomes, streamlines operations, and supports data-driven decision-making in healthcare. This study develops a framework to examine key obstacles to its implementation using Interpretive Structural Modelling (ISM) and MICMAC analysis. Through an extensive literature review, critical obstacles are identified and their interdependencies validated by nine experts, forming a self-structural interaction matrix. The analysis reveals hierarchical relationships and categorizes obstacles into dependent (e.g., data quality), linkage (e.g., interoperability across healthcare systems), and independent (e.g., data privacy and security) clusters, with no autonomous obstacles. Key findings highlight critical barriers, including data privacy, data quality, and IT infrastructure, along with MICMAC insights for prioritizing interventions. This framework may aid healthcare organizations in addressing challenges and optimizing big data analytics implementation.