Optimizing health IT project delivery through integrated data governance, continuous process improvement, and predictive analytics for population health outcomes
1 Illinois Department of Healthcare and Family Services, Illinois, USA.
2 Department of Business and Management, University of Illinois Springfield.
Review Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 1262-1277
Publication history:
Received on 10 April 2022; revised on 22 December 2022; accepted on 25 December 2022
Abstract:
Health IT project delivery in modern healthcare environments is increasingly complex, requiring coordination across technical, clinical, and administrative domains. The need to manage growing datasets, evolving regulatory requirements, and rapidly advancing digital health tools necessitates a more integrated and predictive approach. By aligning data governance, process improvement, and predictive public health analytics, organizations can improve implementation success, optimize outcomes, and ensure long-term sustainability of health IT initiatives. This paper presents a strategic framework for enhancing health IT project delivery by embedding robust data governance policies that prioritize data quality, security, and compliance across all implementation stages. With clear stewardship roles, standardized terminologies, and access controls, organizations can mitigate risks and foster data integrity throughout system lifecycles. Simultaneously, the integration of continuous process improvement methodologies such as Lean Six Sigma into IT project workflows enables organizations to identify bottlenecks, eliminate inefficiencies, and improve stakeholder engagement. These frameworks also support rapid-cycle feedback loops essential for adapting project plans to emerging challenges and user needs. Further, the application of predictive analytics tools in public health contexts allows IT teams to forecast demand surges, monitor population health trends, and prioritize functionality based on real-time epidemiological indicators. By leveraging machine learning and GIS-integrated data models, healthcare systems can deliver scalable IT solutions aligned with public health objectives. The paper concludes with a roadmap for cross-functional governance, highlighting key enablers for agile, data-informed health IT project execution that supports resilience, equity, and patient-centered innovation
Keywords:
Health IT; Data governance; Process improvement; Predictive analytics; Public health; Project delivery
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0