"Bindu Madhavi Mangalampalli is a Business Intelligence & Data Engineering specialist with deep expertise in Healthcare Analytics. Her professional focus spans healthcare data warehousing, clinical analytics ETL, insurance claims intelligence, and medical data governance making her a versatile force in the healthcare data ecosystem. From 2021 to 2025, Bindu has pursued rigorous research across the full spectrum of healthcare BI, beginning with scalable data warehouse architecture and ETL optimization, and advancing into AI-driven anomaly detection, machine learning for medical billing fraud, and generative AI for data mart design. Her most recent work explores large language models for automated healthcare data dictionaries, federated learning for multi-organization collaboration, and conversational AI for self-service BI platforms reflecting her forward-thinking approach to intelligent, compliant, and patient-centered data solutions. Bindu exemplifies the convergence of engineering precision and healthcare domain expertise, driving smarter, more equitable analytics across insurance and clinical environments."

FHIR, Interoperability, Real-Time Data Exchange AbstractAgentic AI Systems for Autonomous Healthcare Data Pipeline Orchestration and Optimization Background: Data is undoubtedly the most relevant asset in the digital transformation of businesses. In healthcare, data pipelines enable the automated orchestration and optimization of data flow among diverse systems and stakeholders. Properly designed, they provide a mechanism to abstract and fulfil the resource-related needs of the processes they support, including ingestion, analysis, sharing, and publishing. Nevertheless, healthcare data pipelines are still implemented manually or with ad-hoc automation, which threatens their reliability, flexibility, and efficiency. Integrating Agentic AI (Artificial Intelligence) appears to be the solution for their automation and optimization. Agentic AI systems reproduce the autonomous, self-organizing, decision-making, and multi-role properties of human archaeogenic agentic behaviours. In the context of healthcare data pipelines, these properties enable automatic data ingestion and sharing whenever and wherever needed;participation as data providers, consumers, or orchestrators; dynamic adaptation to changes in resource-related needs, costs, or availability; and multi-agent collaboration with associated role allocation. Supporters of this approach claim that Agentic AI will favour businesses in their race towards becoming data-driven by allowing managers to focus on their core business and outsource resource-related problems to self-organizing control loops operating over business processes. Nevertheless, Agentic AI systems have yet to be implemented and tested. This gap deserves attention, especially in a domain as heterogeneous and complex as healthcare, where patients' lives depend on the accurate and reliable availability of data. The following presents an in-depth analysis of healthcare data pipelines and identifies how Agentic AI can support their automation and optimization.