Scalable Cloud Data Integration Models for Smart Healthcare Information Exchange
DOI:
https://doi.org/10.15662/IJEETR.2022.0406012Keywords:
Smart healthcare, cloud computing, cloud data integration, healthcare interoperability, big data, big data analytics, machine learning, artificial intelligence, cyber-physical systems, health information exchange, Internet of Things, health-related data exchange, security, privacy, economy, costs, performance, scalability, data governance, quality assuranceAbstract
Modern healthcare systems are evolving into smart and connected service networks, organized around people and enabled by information technology and data for improving lifestyle, health, and wellness. As the amount of data in smart healthcare applications keeps increasing, privacy, security, and legal concerns are becoming critical for sharing sensitive healthcare information across organizations, especially as organizations migrate their services to either hybrid or full cloud solutions. Cloud computing plays a major role in smart healthcare application systems by providing data infrastructures, architectures, and services with efficient data storage and processing capabilities. Integrating heterogeneous data to and from various cloud data services constitutes a significant challenge in cloud-based healthcare applications, particularly in healthcare information exchange.
This section presents scalable, evidence-based, and formally structured analysis of cloud data integration for smart healthcare data exchange. The core concepts and operating objectives of cloud data integration for smart healthcare information exchange are defined, the key architectural paradigms are identified, and the security, privacy, and legal considerations associated with the integration of sensitive healthcare information across organizations are addressed. Seeking cost-efficient storage and processing solutions, the discussion identifies suitable data integration models, including cloud-native data lakehouse architectures and event-driven, stream-processing data pipelines. The analysis also includes issues related to data quality, performance and scalability, and applicable implementation strategies. Such an encompassing view supports the design and management of cloud-based data integration solutions that address the scalable data-related requirements of organizations in smart healthcare ecosystems.
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