Published Paper Details:

The sheer growth rate of the size and complexity of healthcare data, including structured data in the form of laboratory results, diagnostic codes, etc., in addition to unstructured data in the form of clinical notes, radiology reports, and more informal free-form narratives, presents major challenges when it comes to integration and analysis. The heterogeneity of these multimodal inputs, their scale, and velocity may exceed the capabilities of traditional data management frameworks, and thus, it may be difficult to leverage such underlying data management frameworks in clinical decision support, population health management, or research. Cloud-native data pipelines, the use of distributed computing frameworks, and continuous learning using modern natural language processing models have become a possible answer to consolidate disparate data feeds into a unified, queryable data source. These pipelines allow the construction of longitudinal patient records, real-time analytics, and secure cross-institutional collaboration, with adaptive orchestration and cost-aware optimization being included to stay operational. The architectures, operationalization, and research directions of such pipelines are discussed in this article in terms of resilience, compliance, and efficient management of large-scale clinical data environments.