Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review.

The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field.

A sociotechnical approach to vaccine manufacturer selection as part of a global immunization strategy against epidemics and pandemics.

These are unprecedented times while the world weathers the highly infectious respiratory pandemic caused by coronavirus disease-19 (COVID-19). Humanity has experienced other cataclysmic events, but something as novel as this pandemic cannot be easily described. A safe COVID-19 vaccine is often hailed as the only effective public health method to prevent the further spread of this virus. New vaccines' cost has increased even as policymakers struggle with limited resources and budget constraints. Thus, more decision-support tools are needed to facilitate the selection of vaccine manufacturers as part of a global immunization strategy against COVID-19 or other epidemics and pandemics. This study sought to address this issue by combining three well-established operational research methods (i.e., cognitive mapping, decision-making trial and evaluation laboratory, and the Choquet integral). Based on the insights provided by a panel of experts on vaccination and infectious diseases, a vaccine manufacturer selection mechanism was developed that incorporates the World Health Organization's guidelines. This approach facilitated the identification of multiple selection criteria regarding vaccine manufacturers, their allocation into six major clusters (i.e., soundness of scientific approach and technology used; speed of delivery; cost; liability and risk sharing; ability to supply sufficient quantities through production capacity development; and global solidarity), and subsequent analysis of the respective cause-and-effect relationships. The results of a real-life application of the proposed selection system were further consolidated by a member of Saint Francisco Xavier Hospital Infectious Diseases Unit in Lisbon, Portugal. The mechanism's advantages and limitations are also discussed.

Resource planning strategies for healthcare systems during a pandemic.

We study resource planning strategies, including the integrated healthcare resources' allocation and sharing as well as patients' transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters.