Real-Time Decision Intelligence in Healthcare Project Delivery Using Adaptive Data Environments
Kelvin E. Rabbles *
College of Professional Studies, Northeastern University, Boston, USA.
Oladapo Aiyenitaju
Department Information Technology, Coolbet, Tallinn, Estonian.
*Author to whom correspondence should be addressed.
Abstract
Introduction: Healthcare project delivery increasingly relies on data-intensive systems to manage complex infrastructure, multidisciplinary stakeholders and rapidly evolving clinical requirements. Traditional project reporting approaches are often unable to provide the timely insights required for proactive decision-making.
Aim: This review synthesises current evidence on real-time data architectures, performance impacts and governance mechanisms that support decision intelligence in healthcare project delivery.
Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Literature searches were performed in Scopus, Web of Science and IEEE Xplore databases, covering publications from 2015 to 2025. The SPIDER framework guided the search strategy, focusing on decision intelligence, healthcare infrastructure and real-time analytics. Following screening and eligibility assessment, 12 high-quality studies were selected from an initial pool of 400 records. Methodological quality was evaluated using the Mixed Methods Appraisal Tool (MMAT), and the evidence was synthesised through critical analysis of technical architectures, operational outcomes and governance frameworks.
Results: The findings identify Lambda Architecture as the predominant framework for integrating real-time stream processing with long-term data auditability. Adaptive data environments demonstrated substantial operational benefits, including process efficiency improvements of up to 32%, reductions in medication turnaround times by 26% and enhanced stakeholder coordination through centralised decision-support systems. Large-scale implementations reported significant financial savings and reduced infrastructure costs. Automated governance mechanisms, including machine learning-based data quality assurance and AI-driven compliance monitoring, achieved high levels of data integrity and security compliance, supporting reliable decision-making in complex healthcare environments.
Conclusion: Real-time decision intelligence transforms healthcare project management from a reactive reporting function into a proactive decision-support capability. Adaptive data environments provide a robust foundation for improving project performance, governance and organisational resilience. Despite challenges related to interoperability, organisational readiness and regulatory compliance, these technologies represent a critical pathway towards future-ready healthcare project delivery and infrastructure management.
Keywords: Decision intelligence, adaptive data environments, healthcare project delivery, real-time analytics, healthcare informatics, Data Governance, project management office, building information modelling, internet of things, clinical decision support systems, data quality assurance, digital transformation