AI-Enhanced Data Engineering for Smart Hospital Management

Abstract: The influence of AI on healthcare data engineering across the data lifecycle is vast, enabled by key capabilities that affect processes from creation to sharing and archiving. Data quality and provenance are primary concerns, and healthcare organizations can achieve better, faster results when appropriate AI methods are applied. For hospitals and healthcare institutions with a network of branches, real-time sharing of data is critical, allowing clinical and operational decisions to be based on up-to-date information from across the entire organization. Machine learning acts as a principal driver of operations, from clinical decision support to analytics, diagnostics, resource optimization, workload assessment, and demand forecasting. Support from a well-defined data architecture does not eliminate the risk of unanticipated outcomes or the need for adequate protection of patient information, which is safeguarded by privacy-preserving techniques that include access control and auditability. Common standards such as Health Level 7 Fast Healthcare Interoperability Resources, Digital Imaging and Communications in Medicine, Representational State Transfer and gRPC, and the OpenAPI Specification and Postman ecosystem simplify integration, while consistent adherence enhances usability and reliability.

Keywords: AI-Driven Healthcare Data Engineering, Healthcare Data Lifecycle Management, Clinical Data Quality And Provenance, Real-Time Healthcare Data Sharing, Distributed Hospital Data Systems, Machine Learning In Healthcare Operations, Clinical Decision Support Systems, Healthcare Analytics And Diagnostics, Resource Optimization In Healthcare, Demand Forecasting In Hospitals, Healthcare Data Architecture, Patient Privacy Protection, Privacy- Preserving Data Techniques, Access Control And Auditability, Healthcare Interoperability Standards, HL7 FHIR Integration, DICOM Medical Imaging, API-Driven Healthcare Systems, Secure Healthcare Data Exchange, Scalable Health Data Platforms.