Architecture of Interconnected Clinical Data Platforms

Healthcare intelligence graphs provide a blueprint for the integration of diverse health-related information into a common structure that supports graph analytics and navigational interfaces with interpretability at their core. The challenge is twofold: improving the quality and trustworthiness of the underlying data and enabling the discovery of new knowledge via methods that exploit the connectivity of a graph structure. Healthcare intelligence graphs realize a distinction between data gathering and data processing. Data come from clinical, administrative, genomic, and public health sources and are assembled in federated form to enable continuous updates. The data-processing layer allows data scientists or AI engineers to define the operations to be performed on a specific subset of the data and to control when and how they are executed. Analysts then provide the concise version of a dedicated task that can be interpreted, reused, and reasoned over by practitioners and decision-makers within the domain of interest.