Building a Centralized Data Operations Hub for Healthcare Enterprise Integration

A Centralized Data Operations Hub represents a transformative architectural paradigm for healthcareorganizations facing unprecedented challenges in managing diverse data generated across their ecosystems.This hub architecture integrates structured electronic health record entries, unstructured clinical notes,medical imaging, and real-time monitoring data into a cohesive framework that enables comprehensivepatient information access. By implementing a modern data lake/lakehouse foundation, unified ingestionlayer, comprehensive metadata management, robust governance, and sophisticated processing pipelines,healthcare organizations can overcome the fragmentation of traditional approaches while establishingtechnical foundations for advanced analytics. The hub prioritizes healthcare-specific integration standardsincluding FHIR, HL7, DICOM, and IHE profiles to ensure semantic interoperability across systems.Benefits include improved data accessibility for clinicians, enhanced decision support capabilities,operational efficiency improvements, expanded research opportunities, strengthened regulatorycompliance, and significant cost optimization. Implementation considerations encompass phaseddeployment strategies, cultural change management, technical skills development, vendor ecosystemintegration, and continuous improvement processes that ensure the architecture remains aligned withevolving organizational needs.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-04-03
DOI
https://doi.org/10.71097/ijsat.v16.i2.3219
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Building a Centralized Data Operations Hub for Healthcare Enterprise Integration

Pavan Kumar Boyapati -
1 citations
International Journal on Science and Technology
Big Data and Business Intelligence
2.28
article

Building a Centralized Data Operations Hub for Healthcare Enterprise Integration

Pavan Kumar Boyapati -
article en
1 citations

Abstract

A Centralized Data Operations Hub represents a transformative architectural paradigm for healthcareorganizations facing unprecedented challenges in managing diverse data generated across their ecosystems.This hub architecture integrates structured electronic health record entries, unstructured clinical notes,medical imaging, and real-time monitoring data into a cohesive framework that enables comprehensivepatient information access. By implementing a modern data lake/lakehouse foundation, unified ingestionlayer, comprehensive metadata management, robust governance, and sophisticated processing pipelines,healthcare organizations can overcome the fragmentation of traditional approaches while establishingtechnical foundations for advanced analytics. The hub prioritizes healthcare-specific integration standardsincluding FHIR, HL7, DICOM, and IHE profiles to ensure semantic interoperability across systems.Benefits include improved data accessibility for clinicians, enhanced decision support capabilities,operational efficiency improvements, expanded research opportunities, strengthened regulatorycompliance, and significant cost optimization. Implementation considerations encompass phaseddeployment strategies, cultural change management, technical skills development, vendor ecosystemintegration, and continuous improvement processes that ensure the architecture remains aligned withevolving organizational needs.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 13%
Big Data and Business Intelligence
2.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.