Leveraging Cloud-Based Scalable Analytics for Healthcare Operational Insights: A Framework for Implementation

Abstract

Scalable analytics on the clouds provide disruptive opportunities to healthcare organizations that confront the challenge of growing exponentially because of electronic health records, medical imaging services, and monitoring devices. This framework analyzes the architectural elements that are required in executing secure, compliant, and elastic data pipelines that consolidate clinical and operational information streams. The layered architecture has exhaustive data ingestion, hybrid data storage, distributed-processing frameworks, machine learning integration, plus role-based visualization systems that meet the expectations of various stakeholders. Consideration of implementation touches upon the choice of platform, security controls, streaming analytics needs, and privacy-saving methods. A case study of a metropolitan healthcare system shows that, by improving the predictive models with the additional social determinants of health data, vulnerable populations could be subjected to targeted interventions that led to a massive reduction of readmissions. The framework concentrates on environmental sustainability by optimizing resources, economic benefits by use of consumption-based models, and social equity by identifying the risks of disadvantaged groups better, and emphasizing future directions such as federated learning and explainable AI to support clinical decisions.