System Models for Synchronous Strategies in Operational Healthcare Forecasting

Arnesh Telukdarie (), Logistic Makoni, R. Raghunatha Sarma, Megashnee Munsamy and Sunil Kumar
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Arnesh Telukdarie: Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa
Logistic Makoni: Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa
R. Raghunatha Sarma: Vidyagiri, 5R77+32M Prasanthi Nilayam, Puttaparthi 515134, Andhra Pradesh, India
Megashnee Munsamy: Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa
Sunil Kumar: Vidyagiri, 5R77+32M Prasanthi Nilayam, Puttaparthi 515134, Andhra Pradesh, India

IJERPH, 2025, vol. 22, issue 2, 1-30

Abstract: The delivery of healthcare in Low-to-Medium-Income Countries (LMICs) has long posed challenges, with established models predominantly found in wealthier nations. These models are found to be either strategic or operational, and very rarely combine these two perspectives. Most importantly, these models lack a comprehensive, holistic and synchronous construct that accompanies a systems thinking approach. This research evaluates international best practices, fundamental global theories and existing systems and tools in healthcare through a systems approach. It collates these data to propose a customized systems-based, comprehensive framework for modeling and optimizing both the management and operational tiers of healthcare in LMICs. The approach is based on the adoption of digital tools, inclusive of AI, to analyze, assimilate, align and develop advanced, holistic and inclusive frameworks. The current gap in global healthcare delivery is characterized by an ongoing lack of ability to provide quality and cost-effective care, especially in the LMICs. Despite the fact that developmental challenges are unique and specific to respective countries, there are commonalities with regard to healthcare processes that present opportunities for optimization. The main challenge lies in the effective collation and synchronization of data and tools with the specific contexts of each country. This situation highlights the need for a cohesive systems approach to enhance healthcare delivery in LMICs, allowing for tailored solutions that can bridge existing gaps. This paper presents a strategic model, with initial data quantification guiding the development of the system model. The practical significance of this research lies in its potential to transform healthcare delivery in LMICs, leading to enhanced access and quality of care through optimized systems.

Keywords: healthcare delivery; healthcare forecasting; LMICs; optimization; digital tools; artificial intelligence (AI) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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