ORIGINAL ARTICLE Anne Miller Yan Xiao Multi-level strategies to achieve resilience for an organisation operating at capacity: a case study at a trauma centre Received: 27 February 2006 / Accepted: 21 April 2006 / Published online: 19 July 2006 Ó Springer-Verlag London Limited 2006 Abstract Healthcare services are examples of organisa- tions that operate frequently at capacity, as reflected by periods of high demand and hospital overcrowding. Using the safe operating envelope framework (Qual Saf Health Care 14:130–134, 2005), this study identifies the strategies hospital staff use to respond to high patient demand pressures. A surgical unit (SU) in a dedicated trauma hospital provides the context for the study. Re- sults are based on the outcomes of structured, critical decision method and contextual interviews involving six participants selected according to their roles in relation to resource allocation within the SU. The study’s central findings are (1) that temporally nested patterns of emergency patient admissions are the dominant influ- ence on three levels of management decision making in the SU and (2) that compensatory buffers are actively planned at multiple levels of work organisation. These results are discussed in terms of their theoretical impli- cations and implications for technological design. The methodological limitations of the research are also dis- cussed. Keywords Safe operating envelope Compensation Temporality Patient admissions Resilience Abbreviations CRNA: Certified registered nurse anaesthetist RN: Registered nurse SU: Surgical unit TRU: Trauma resuscitation unit 1 Introduction High-risk organisations operate in a confluence of forces such as economic pressures, political pressures and safety requirements that push them towards the limits of their operating capacity (Leveson et al. 2006). Increasing resource capacity by recruiting more workers or expanding physical resources is not always feasible (Duckett 1999), thus there is a growing interest in improving organisations’ ability to operate reliably and safely at capacity. Healthcare services and hospitals in particular are increasingly under intense pressure to operate at capac- ity. According to recent reports in the United States healthcare is estimated to consume 17% of the gross domestic product by 2010 (Heffler et. al. 2002). Ageing populations and new medical technologies have in- creased patient demand (Locker et. al. 2005; Best 2002). In this context, healthcare services are likely to experi- ence more frequent occurrences of emergency depart- ment overcrowding and bed gridlock in peak demand times. During bed gridlock patient waiting times in- crease the risk of complications and a patient’s length of hospital stay which further contributes to gridlock in other parts of a hospital (Fatovich et.al. 2005; Bell and Reidelmeier 2004; Locker et. al. 2005; Molony et. al. 2005; Weiss et. al. 2004; Fatovich and Hirsch 2003). One approach to solving demand–capacity mis- matches is to develop demand forecasting algorithms. However, their effectiveness may be limited (a) due to inaccuracy given available data (Green 2004), and (b) as a consequence of poor reliability given system modelling issues (Cromwell 2004; Harper and Shahani 2002). Another approach is to improve staff members’ ability to better manage resources in high demand periods (Pickard et al. 2004). Improving staff members’ adaptive abilities has been advocated in high-risk industries for many years. Tech- nology is often recommended to support worker flexi- bility and adaptation. However, new technology can A. Miller (&) ARC Key Centre for Human Factors and Applied Cognitive Psychology, The University of Queensland, McElwain Building (24A), St Lucia Campus, Brisbane, QLD 4072, Australia E-mail: amiller@humanfactors.uq.edu.au Y. Xiao School of Medicine, University of Maryland, Baltimore, MD, USA Cogn Tech Work (2007) 9: 51–66 DOI 10.1007/s10111-006-0041-0