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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Leveraging enterprise analytics to align risk mitigation, health IT deployment, and continuous clinical process improvement

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  • Leveraging enterprise analytics to align risk mitigation, health IT deployment, and continuous clinical process improvement

Enitan Hannah Adeniji 1, *, Kayruwhuka Chinyere Owhonda 1, Glory Stephen-Kings 2 and Jervis Ohikhuare 1

1 Department of Business and Management, University of Illinois Springfield.
2 Illinois Department of Healthcare and Family Services, Illinois, USA.

Research Article

 

International Journal of Science and Research Archive, 2023, 10(02), 1314-1329.
Article DOI: 10.30574/ijsra.2023.10.2.1003
DOI url: https://doi.org/10.30574/ijsra.2023.10.2.1003

Received on 23 October 2023; revised on 24 December 2023; accepted on 27 December 2023

The dynamic landscape of modern healthcare demands a proactive, integrated approach to performance optimization that balances clinical quality, operational efficiency, and risk exposure. As healthcare organizations grapple with rising costs, regulatory scrutiny, and rapidly evolving technologies, enterprise analytics has emerged as a cornerstone for aligning strategic goals across risk management, health IT deployment, and clinical process improvement. This paper explores how enterprise-wide analytics platforms enable data-driven alignment across three critical domains: risk mitigation, health information technology (IT) rollout, and continuous clinical improvement. By leveraging advanced data visualization, predictive modeling, and real-time dashboards, healthcare systems can identify systemic risks—ranging from compliance lapses to safety events—and proactively address them through early intervention strategies. Simultaneously, analytics supports the optimization of health IT deployment by tracking implementation metrics, monitoring user adoption, and correlating technology use with clinical outcomes. This evidence-based approach reduces resistance to change, informs EHR configuration, and ensures that IT investments translate into measurable gains in patient care delivery. Furthermore, enterprise analytics empowers continuous clinical process improvement by uncovering inefficiencies, standardizing care pathways, and tracking quality indicators such as readmission rates, medication errors, and throughput delays. When integrated with lean methodologies and quality improvement frameworks, analytics becomes a strategic enabler of both short-term operational gains and long-term transformation. The paper concludes with a framework for embedding enterprise analytics into organizational governance structures, reinforcing a culture of accountability, agility, and clinical excellence. Through unified data intelligence, healthcare institutions can synchronize risk, technology, and clinical outcomes to achieve sustainable value-based care.

Enterprise Analytics; Health IT Deployment; Risk Mitigation; Clinical Improvement; Healthcare Quality; Data-Driven Strategy

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2023-1003.pdf

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Enitan Hannah Adeniji, Kayruwhuka Chinyere Owhonda, Glory Stephen-Kings and Jervis Ohikhuare. Leveraging enterprise analytics to align risk mitigation, health IT deployment, and continuous clinical process improvement. International Journal of Science and Research Archive, 2023, 10(02), 1314-1329. Article DOI: https://doi.org/10.30574/ijsra.2023.10.2.1003

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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