Optimizing Maintenance Systems of Healthcare Facilities in Low-Resource Settings Through Modeling and Multi-Scenario Discrete Event Simulation

Abstract

Healthcare systems in low-income countries are in dire need of more effective methods for managing their scant resources, especially people and equipment. Industry 4.0 has the potential to provide the means for circumventing current constraints that keep low-income economies from improving healthcare service delivery. Significant successes have already been realized from application of queuing theory and simulation techniques such as discrete event simulation, agent-based modeling and system dynamics, in solving various optimization problems within different operational contexts. However, literature reveals that maintenance optimization as associated with overall healthcare systems improvement remains relatively unexplored. This study considers the problem of maintenance workflow optimization with respect to skilled labor, equipment availability and time, by forecasting the demand on resources in the special case of task requests flowing from multiple queues, in parallel, into the same process for resolution, with different task priorities and queue characteristics. The paper presents findings on how discrete event simulation may be adopted in developing a maintenance management decision-support tool for healthcare. The tool could aid asset managers, terotechnologists and other healthcare practitioners, particularly from low-income economies, in leveraging operational performance data to project future asset-performance trends accurately and thereby determine appropriate interventions that guarantee optimal performance. The study demonstrates that healthcare facilities in low-income economies can achieve optimum levels of service delivery in a cost-effective manner through the tool-assisted selection of appropriate maintenance strategies for all equipment within a healthcare facility and that any future changes can be expeditiously and cost effectively re-evaluated and addressed.

Note:

Funding Information: The authors acknowledge the financial support from PEETS and MerSETA that made this research possible. The authors are also grateful to the University of Johannesburg for material support rendered in carrying out this study.

Declaration of Interests: The authors have no competing interests to declare.

Keywords: Maintenance management, Healthcare facilities, Discrete Event Simulation, Modeling, Simulated Annealing