Objective: The aim was to develop a method based on resilient healthcare principles to proactively identify system vulnerabilities and quality improvement interventions. Design: Ethnographic methods to understand work as it is done in practice using concepts from resilient healthcare, the Concepts for Applying Resilience Engineering model and the four key activities that are proposed to underpin resilient performance-anticipating, monitoring, responding and learning. Setting: Accident and Emergency Department (ED) and the Older People's Unit (OPU) of a large teaching hospital in central London. Participants: ED-observations 104 h, and 14 staff interviews. OPU-observations 60 h, and 15 staff interviews. Results: Data were analysed to identify targets for quality improvement. In the OPU, discharge was a complex and variable process that was difficult to monitor. A system to integrate information and clearly show progress towards discharge was needed. In the ED, patient flow was identified as a complex high-intensity activity that was not supported by the existing data systems. The need for a system to integrate and display information about both patient and organizational factors was identified. In both settings, adaptive capacity was limited by the absence of systems to monitor the work environment. Conclusions: The study showed that using resilient healthcare principles to inform quality improvement was feasible and focused attention on challenges that had not been addressed by traditional quality improvement practices. Monitoring patient and workflow in both the ED and the OPU was identified as a priority for supporting staff to manage the complexity of the work.

Current approaches to quality improvement rely on the identification of past problems through incident reporting and audits or the use of Lean principles to eliminate waste, to identify how to improve quality. In contrast, Resilience Engineering (RE) is based on insights from complexity science, and quality results from clinicians' ability to adapt safely to difficult situations, such as a surge in patient numbers, missing equipment or difficult unforeseen physiological problems. Progress in applying these insights to improve quality has been slow, despite the theoretical developments. In this chapter we describe a study in the Emergency Department of a large hospital in which we used RE principles to identify opportunities for quality improvement interventions. In depth observational fieldwork and interviews with clinicians were used to gather data about the key challenges faced, the misalignments between demand and capacity, adaptations that were required, and the four resilience abilities: responding, monitoring, anticipating and learning. Data were transcribed and used to write extended resilience narratives describing the work system. The narratives were analysed thematically using a combined deductive/inductive approach. A structured process was then used to identify potential interventions to improve quality. We describe one intervention to improve monitoring of patient flow and organisational learning about patient flow interventions. The approach we describe is challenging and requires close collaboration with clinicians to ensure accurate results. We found that using RE principles to improve quality is feasible and results in a focus on strengthening processes and supporting the challenges that clinicians face in their daily work.

Background Resilience engineering (RE) is an emerging perspective on safety in complex adaptive systems that emphasises how outcomes emerge from the complexity of the clinical environment. Complexity creates the need for flexible adaptation to achieve outcomes. RE focuses on understanding the nature of adaptations, learning from success and increasing adaptive capacity. Although the philosophy is clear, progress in applying the ideas to quality improvement has been slow. The aim of this study is to test the feasibility of translating RE concepts into practical methods to improve quality by designing, implementing and evaluating interventions based on RE theory. The CARE model operationalises the key concepts and their relationships to guide the empirical investigation. Methods The settings are the Emergency Department and the Older Person’s Unit in a large London teaching hospital. Phases 1 and 2 of our work, leading to the development of interventions to improve the quality of care, are described in this paper. Ethical approval has been granted for these phases. Phase 1 will use ethnographic methods, including observation of work practices and interviews with staff, to understand adaptations and outcomes. The findings will be used to collaboratively design, with clinical staff in interactive design workshops, interventions to improve the quality of care. The evaluation phase will be designed and submitted for ethical approval when the outcomes of phases 1 and 2 are known. Discussion Study outcomes will be knowledge about the feasibility of applying RE to improve quality, the development of RE theory and a validated model of resilience in clinical work which can be used to guide other applications. Tools, methods and practical guidance for practitioners will also be produced, as well as specific knowledge of the potential effectiveness of the implemented interventions in emergency and older people’s care. Further studies to test the application of RE at a larger scale will be required, including studies of other healthcare settings, organisational contexts and different interventions.

Stroke is a clinical priority requiring early specialist assessment and treatment. A London (UK) stroke strategy was introduced in 2010, with Hyper Acute Stroke Units (HASUs) providing specialist and high dependency care. To support increased numbers of specialist staff, innovative multisite multiprofessional simulation training under a standard protocol-based curriculum took place across London.

Background: Specialist trainees in Geriatrics need to manage complex scenarios in a range of settings. Simulation provides an education platform for clinicians to become immersed in realistic scenarios where outcome is dependent upon technical and non-technical skills. Methods: 27 trainees attended 4 similar one-day courses focussed on curriculum-mapped clinical scenarios using high-fidelity life-size manikins, patient-actors, patient actors with integrated clinical skills, and role-play exercises. Trainees participated in scenarios individually or in small groups whilst others watched live audio-visual transmission remotely. Debriefs by trained faculty were completed after each scenario. Participants completed validated pre-and post-course questionnaires to assess confidence in managing clinical scenarios (on a linear 0-100 scale) and to evaluate the course's educational value. Results: Trainees' confidence in clinical and non-clinical skills was improved, as shown in the table. Median scores on a 1-5 Likert scale showed trainees to evaluate the course as educational (4), interesting (5), relevant (5) and useful for reflection (4). Median overall satisfaction score was 5. The most common constructs learned were clinical knowledge, situational awareness and communication skills; all of which were judged not to have been taught as effectively by other learning media. Conclusions: A specialist Geriatrics simulation training programme is feasible and perceived to address areas of the curriculum successfully and to improve clinical and non-clinical skills.

Background: City-wide re-organisation of stroke care in London, incorporating 8 hyperacute stroke units (HASUs), has improved thrombolysis rates and survival cost-effectively. Continued staffing of HASUs requires stroke-specialist training to develop competencies for managing neurological emergencies. Simulation training provides an education platform for health care professionals to become immersed in realistic scenarios where outcome is dependent upon technical and non-technical skills. Methods: A standardised, curriculum-mapped, high-fidelity, simulation-training programme was developed on 4 HASUs for city-wide staff to attend. Learning outcomes included technical (acute stroke assessment/management) and non-technical skills (including time management/decision-making/teamwork). A mixed-methods evaluation approach was used to evaluate data from participants before, during, and after training. Results: Over a 2 year period, 152 HASU staff (70 medical; 82 nursing) participated. Quantitative analysis showed a pre/post-course increase in candidates’ ability to manage emergency stroke situations (t=6.6, p<0.001), leadership skills (t=6.7, p<0.001) and communication skills (t=3.7, p<0.001), more so in junior compared with senior clinicians. Simulation training was enjoyable (mean (SD) rating 5.7(2) on 7 point Likert scale), with higher ratings from doctors compared with nurses (t=3; p<0.01). Enjoyment correlated positively (r=0.853; p<0.001) and previous experience of simulation correlated negatively (r=-0.228; p<0.05) with relevance to clinical practice. Thematic analysis of post-course semi-structured interviews demonstrated 5 important learning outcomes (assertiveness; calling for help; situational awareness; teamwork; verbalising thoughts) and 3 main responses for transference to practice (general enthusiasm with no particular practical change; immediate recognition of an emergency situation providing recall of the course; reflective change). Conclusion: Simulation training may be effective in helping achieve HASU-specific learning outcomes and may be delivered in a standardised manner across multiple sites.

Few studies have taken a whole system approach to engineering resilience in healthcare. Doing so involves challenges in operationalising and measuring concepts, developing interventions and assessing their impact at a systems level. In this paper we have outlined a newly funded programme of work to operationalise key resilience concepts, develop and implement interventions to increase resilience, develop metrics to assess their effects and to make recommendations about how the insights of resilience engineering can be harnessed to improve patient safety. The results will provide evidence about the implementation and impact of four complex interventions in the areas of learning, responding, monitoring and anticipating, both singly and in combination, allowing future resilience interventions to be chosen based on knowledge of their effectiveness. The study will also yield an in depth picture of resilience engineering in action to inform the development of theory and the maturation of the approach.

National clinical guidelines have emphasized the need to identify acute stroke as a clinical priority for early assessment and treatment of patients on hyperacute stroke units. Nurses working on hyperacute stroke units require stroke specialist training and development of competencies in dealing with neurological emergencies and working in multidisciplinary teams. Educational theory suggests that experiential learning with colleagues in real-life settings may provide transferable results to the workplace with improved performance. Simulation training has been shown to deliver situational training without compromising patient safety and has been shown to improve both technical and non-technical skills (McGaghie et al, 2010). This article describes the role that simulation training may play for nurses working on hyperacute stroke units explaining the modalities available and the educational potential. The article also outlines the development of a pilot course involving directly relevant clinical scenarios for hyperacute stroke unit patient care and assesses the benefits of simulation training for hyperacute stroke unit nurses, in terms of clinical performance and non-clinical abilities including leadership and communication.