DEVELOPMENT OF A HYBRID MODEL FOR REAL-TIME BUSINESS INTELLIGENCE DECISION-MAKING IN HEALTHCARE

Authors

  • Onyebuchi Evidence Chiedozie Department of Computer Science, College of Physical and Applied Sciences, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
  • Beloved Obinnaya Omankwu Department of Computer Science, College of Physical and Applied Sciences, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Keywords:

Business Intelligence, Real-Time Analytics, Healthcare, Decision-Making, Hybrid Model, Data Analytics, Predictive Analytics, Decision Support Systems

Abstract

The increasing volume, variety, and velocity of healthcare data have created significant opportunities for data-driven decision-making while simultaneously exposing limitations in conventional Business Intelligence (BI) systems. Healthcare organizations often rely on fragmented information systems, delayed reporting processes, and historical data, which can limit the ability of healthcare professionals and managers to respond promptly to emerging clinical, operational, and administrative situations. This study proposes the development of a hybrid model for real-time Business Intelligence decision-making in the healthcare sector. The proposed model integrates real-time data acquisition, data integration, business intelligence, predictive analytics, and decision-support mechanisms to provide timely and actionable insights to healthcare decision-makers. The hybrid architecture is designed to integrate data from diverse sources, including electronic health records, hospital information systems, laboratory systems, administrative databases, and healthcare monitoring devices. A Design Science Research approach is adopted to guide the requirements analysis, model design, prototype development, implementation, and evaluation of the proposed framework. The model combines descriptive and predictive analytics with real-time processing and visualization to support faster, evidence-based decision-making. The proposed approach is expected to improve decision-making speed, information availability, predictive capability, resource utilization, and operational efficiency within healthcare organizations. The study contributes a structured framework for integrating real-time Business Intelligence and advanced analytics into healthcare decision-support processes and provides a foundation for developing more responsive and data-driven healthcare management systems.

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Published

2026-09-14 — Updated on 2026-09-21

Issue

Vol. 4 No. 1 (2026): Advanced Journal of Engineering and Scientific Applications (AJESA) Volume 4 No. 1, July/August 2026

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