OR is a vast field of study! A reference to the ‘Wiley Encyclopedia of Operations Research and Management Science’ (Cochran et al., 2011) provides scholarly insights into the enormity of this discipline. The encyclopaedia is marketed as an ‘unparallel undertaking’ that is devoted towards the advancement of OR and is an eight-volume authoritative reference set made up of over 6,000 pages! The field of OR is also expanding! The International Abstracts in Operational Research (IAOR, n.d.) is an indicator of the growing literature in OR. IAOR indexes abstracts from OR/MS journals as well as some specialized journals that are relevant to OR/MS research (IAOR, n.d.). It is maintained by the International Federation of Operational Research Societies (IFORS) and is quoted to be the ‘the only dedicated source for bibliographic and abstract information in operations research and management science’ (IFORS, n.d.). It consists of 68,000 abstracts from over 145 journal titles (Miser, 2000); the number of journals has now increased to approximately 180. Mustafee and Katsaliaki (n.d.) report that in 1961 the first issue of IAOR listed work on models of common processes in 35 categories, on problems in 11 arenas of application and on 31 types of related theory development. By 1975, the numbers in these categories had reached 47, 34 and 53, respectively, and by 1998 they were 66, 43 and 77. In the specific case of healthcare OR there is evidence that this continues to be a growing area of research (Brailsford et al., 2009; Mustafee et al., 2010; Katsaliaki and Mustafee, 2011).

The focus of Volume 1 was on OR for Emergency Planning at an Operational Level. It consisted of 13 studies which were presented in three parts, namely, OR for Locating Emergency Services (Part I; three chapters), OR for Operational Planning in Emergency Services (Part II; six chapters) and OR for Inventory Management in Emergency Services (Part III; four chapters). The chapters included the application of mainly ‘Hard’ OR techniques, for example, mathematical modeling, MCDA, Bayesian decision model and discrete-event simulation (DES).

Volume 2 of this book is mainly on the application of OR for Healthcare Emergency Planning at a Strategic Level. It also includes a few literature review papers. Like Volume 1, this book consists of 13 studies which are organized under specific parts, namely, OR for Assessment and Review of Emergency Services (Part IV; Chapters 2–4), OR for Policy Formulation in Emergency Services (Part V, Chapters 5–6), OR for Broader Engagement in Planning for Emergency Services (Part VI, Chapters 7–10) and Application of OR within the Wider Healthcare Context (Part VII, Chapters 11–14). The chapters included in this volume are outlined in Table 1.1; for every chapter (except for Part VII) the table lists the OR technique, domain of application and context of use.

Part V of the book includes two studies on OR for Policy Formulation in Emergency Services. The study by Fletcher et al. (Chapter 5) describes a generic A&E simulation model that was developed by OR analysts within the Department of Health (DH). The primary purpose of the model was to inform the national policy team of significant barriers in achieving the national targets related to A&E. The model was used by stakeholders at the national level (DH, national A&E team) to discuss patient flows and the impact of potential policies. As the A&E model was developed to be a generic model its application was also possible at the local level, and indeed it was used as a consultancy tool to aid hospital trusts to improve their A&E departments. Chapter 6 is a study by Lane, Monefeldt and Rosenhead, which was motivated by public concern over long waiting times for admissions. The authors developed a SD model to capture the interaction of demand pattern, A&E resource deployment, other hospital processes and bed numbers. The study revealed the interconnectedness of A&E service levels, bed provision and the experience of patients on waiting lists. One principal message that came out was that using A&E waiting times alone to judge the effect of bed reductions was a naïve approach.

Part VI is the Penultimate part of the book and concentrates on OR for Broader Engagement in Planning for Emergency Services and it consists of four chapters. Lane and Husemann present a study that uses qualitative SD for mapping the flows of acute patients within the NHS (Chapter 7). The UK Department of Health provided funding and broad direction for this study. The chapter mainly focuses on the activities associated with the three workshops wherein NHS staff were able to propose ideas for improving patient flows and on the basis of which a suite of maps of general acute hospitals were drawn up. The maps and ideas were then communicated back to the DoH and subsequently informed about the modernization of A&E. Chapter 8 focuses on OR for disaster response. Planning related to Disaster Operations Management (DOM) generally takes place in multi-stakeholder environments, consisting of, for example, representatives from healthcare, fire brigade, police, disaster relief terms, politicians and indeed the vulnerable group who stand to be affected. In such environments the use of Soft OR methods like Soft Systems Methodology (Checkland, 1981), Qualitative SD, Strategic Option Development Analysis, etc. can be used for developing a shared understanding within this diverse group (Wienke and Mustafee, 2015). In Chapter 8 authors Gregory and Midgley report on one such ‘Soft OR’ study on multi-agency planning for disaster-response. The chapter describes how Critical Systems Thinking and methods from SSM were used in six, one-day workshops that were attended by representatives from 19 agencies (including four health authorities and the ambulance service) who came together to plan the basis for a counselling network that could be activated during an emergency. Further to the use of ‘Soft OR’ techniques (which are qualitative in nature), mathematical modeling, fuzzy sets, statistical techniques, decision theory and queuing theory have been used in relation to DOM, for example, for predicting environmental disasters, estimating damage, evacuation planning, among others (Wienke and Mustafee, 2015). Huang, Tsai and Wen are the authors of Chapter 9. The chapter focuses on the use of network-based simulation for teaching epidemiology. It is arguable that the use of public health simulations as a pedagogical tool enables students to better understand spreading situations; further it allows the analysis of disease patterns, prediction of epidemic dynamics and allows a safe environment for testing the possible effectiveness of public health interventions, all of which contribute towards better planning of emergencies associated with epidemic outbreak. There are also examples in Operations Management literature where business-simulation games like the Blood Supply Chain Game (Katsaliaki et al., 2014) have been used to train participants in making better decisions under pressure and in complex situations where an outcome arises from the interaction of multiple factors and interventions. A systems vision of knowledge management in emergency care is presented by authors Edwards, Hall and Shaw in Chapter 10; it draws upon research conducted in two healthcare organizations that appeared to be approaching knowledge management in a fragmented way and which prevented them from having a holistic view of the whole of the care process. The chapter explores the complexity of knowledge management in emergency healthcare and draws the distinction for knowledge management between managing local and operational knowledge, and global ...