No abstract

Purpose A growing body of empirical research has emerged, focused on leveraging Industry 4.0 technologies to develop and optimise systems within various operational contexts, including healthcare delivery. However, even though a significant number of studies have been published on application of digital technologies in enhancing delivery and health outcomes of health systems, systematic studies that review how extensively these technologies have been applied within a low- and middle-income economies’ context remain scarce in the literature. This work attempts to close that gap by investigating the impact of industry 4.0 on healthcare systems in emerging economies. Methods The study follows a systematic review approach and uses PRISMA guidelines to conduct the research and synthesise its findings. A final sample of 72 articles is selected for in-depth review following a systematic screening from an initial list of 597 results. Results The study successfully synthesises the latest research in the subject area and reveals that, hitherto, approaches to use of digital tools have been fragmented and thus unable to provide holistic optimisation solutions for healthcare systems in low-resource settings. The analysis exposes a heavy skew towards adoption of mobile health and telemedicine technologies, with conspicuous research gaps in the use of augmented reality, additive manufacturing as well as simulation and digital twin technologies. Conclusions The study provides researchers, health-care practitioners and systems engineers with knowledge on the state-of-the-art in healthcare systems optimisation and points out research gaps that may be addressed through future empirical studies.

No abstract

No abstract

Healthcare systems in low-resource settings need effective methods for managing their scant resources, especially people and equipment. Digital technologies may provide means for circumventing the constraints hindering low-income economies from improving their healthcare services. Although analytical and simulation techniques, such as queuing theory and discrete event simulation, have already been successfully applied in addressing various optimisation problems across different operational contexts, the literature reveals that their application in optimisation of healthcare maintenance systems remains relatively unexplored. This study considers the problem of maintenance workflow optimisation with respect to labour, equipment availability and cost. The study aims to provide objective means for forecasting resource demand, given a set of task requests with varying priorities and queue characteristics that flow from multiple queues, and in parallel, into the same maintenance process for resolution. The paper presents how discrete event simulation is adopted in combination with simulated annealing to develop a decision-support tool that helps healthcare asset managers leverage operational performance data to project future asset-performance trends objectively, and thereby determine appropriate interventions for optimal performance. The study demonstrates that healthcare facilities can achieve efficiency in a cost-effective manner through tool-generated maintenance strategies, and that any future changes can be expeditiously re-evaluated and addressed.