(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2023)
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