Developing Advanced Predictive Models for Patient Flow Forecasting in Healthcare Facilities: A Systematic Review and Analysis
1 Department of Computer Science, Kwame Nkrumah University of Science and Technology, Ghana
2 Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, USA
Review
International Journal of Frontline Research in Life Science, 2025, 03(02), 019-025.
Article DOI: 10.56355/ijfrls.2025.3.2.0024
Publication history:
Received on 14 June 2025; revised on 22 July 2025; accepted on 25 July 2025
Abstract:
Healthcare facilities globally face challenges in managing patient inflow and maximizing the utilization of resources. Incorrect patient flow predictions have the potential to result in overcapacity, waiting lists, staff burnout, and ineffective care delivery. The paper undertakes a systematic review and critical analysis of advanced predictive models utilized in patient flow prediction in clinics and hospitals. Through a critical examination of various modeling techniques, including time-series forecasting, queuing theory, discrete-event simulation, and machine learning algorithms, this paper identifies their strengths, limitations, and areas for further improvement. The study brings to the fore the possibility of using artificial intelligence (AI), through deep learning and ensemble modeling, to increase forecasting accuracy. Empirical evidence from hospital pilot study reports, case reports, and peer-reviewed journals suggests that while traditional models offer initial perspectives, AI-driven models offer greater flexibility to adapt to system uncertainty and real-time information. This paper suggests implementing dynamic predictive tools within hospital management systems to support strategic planning and operational planning, especially in emergency departments (EDs), intensive care units (ICUs), and outpatient clinics.
Keywords:
Patient Flow; Predictive Modeling; Healthcare Forecasting; Machine Learning; Hospital Resource Management
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0