The initial five articles in this series report on studies of healthcare delivery and health systems. The empirical topics are diverse; they cover mental health services, respiratory conditions, medicines management, hospital-based rapid response teams, system-level accreditation mechanisms and digital health solutions (such as video consultations, assisted living technologies and remote monitoring).
In the first paper, Braithwaite et al.  challenge traditional thinking on implementation science as based, to a greater or lesser extent, on linear and mechanistic (‘pipeline’) models of knowledge translation. Drawing on systems thinking in social and organisational science, the authors discuss how implementation can be understood as an emergent and dynamic process. To achieve system-level change, complexity-informed approaches to implementation would need to depart from a narrow focus on intervention fidelity to embrace effective adaptation and tailoring to context, working closely with local stakeholders, and viewing implementation as an iterative, recursive and long-term process. Taking the example of two large-scale system transformations in Australia (implementation of rapid response systems and introduction of quality standards for health services accreditation), the paper argues that quality and safety improvement can be achieved by ‘attending to’, rather than trying to ‘control for’, complexity.
Wolpert and Rutter  address what is often seen as the cornerstone of evaluation and improvement – routinely collected quantitative datasets. Their paper raises important questions about the value and usefulness of such datasets with regards to measuring and representing change in complex health systems. As the authors argue, quantitative datasets invariably contain Flawed, Uncertain, Proximate and Sparse (FUPS) data, which either become over-interpreted (leading to unwarranted conclusions) or end up being dismissed as incomplete and unreliable. The authors propose a third option, namely to embrace FUPS datasets – warts and all – as a key contributor in the change effort, recognising that, whilst they cannot fully capture the complex world they represent, they still have the potential to expose issues for interrogation, act as sensitising devices for developing understanding and mobilise uncomfortable knowledge. The paper includes an important list of principles for analysing and facilitating discussion on the basis of FUPS data, illustrating these through an empirical example in UK child mental healthcare.
Greenhalgh et al.’s  article addresses complexity as manifested in technological innovation. Drawing on an extensive empirical dataset from six contrasting case studies of technology-supported change in health and social care, they present an evidence-based, theory-informed, and pragmatic framework to explain the Non-adoption or Abandonment of technology by individuals and difficulties achieving Scale-up, Spread and Sustainability (NASSS) in organisations . The NASSS framework embraces multiple levels of analysis to help predict and evaluate programme success. Technology failures, partial successes and unanticipated problems are explained by teasing out the multiple aspects of complexity across interacting domains, including the condition, technology, value proposition, adopter system, organisation, wider context and temporal change. The discussion section includes recommendations for both reducing the complexity of a technology-supported change programme and ‘running with’ aspects of complexity that cannot be reduced.
Long et al.  offer a perspective of how to engage with complexity in practice, based on synergies between complexity theory and pragmatist philosophy. These authors depict pragmatism as a way of prioritising actionable knowledge linked to its contexts of use and suited to address practical questions. Their analysis draws on a 3-year project aimed at developing simulation models to provide strategic decision support for senior leaders in a large public mental health service in Australia. Through a study of simulation modelling to support service implementation and evaluation, they illustrate how complexity theory and pragmatism can be used as complementary approaches to guide and iteratively enhance understanding. Their analysis focuses primarily on the role of agency (that is, human initiative and action), emergent outcomes (things that happen as a result of multiple unfolding events and phenomena), continuous learning and adaptive use of methods. This paper also questions prevailing assumptions about complexity and suggests there is a need to further explore its philosophical and epistemological underpinnings.
Reed et al.  respond to the call for complexity-informed approaches in healthcare by synthesising learning from an extensive programme of improvement initiatives in the UK. Their contribution introduces the empirically-driven and theory-supported framework on Successful Healthcare Improvement From Translating Evidence (SHIFT-Evidence). Through analytical auto-ethnography and grounded theory analysis on data collected across 22 quality improvement projects over a 5-year period, the authors foreground the emergent behaviour of complex systems and the iterative, adaptive nature of change. Their analysis discusses tensions in embedding evidence-based practices against local constraints (‘acting scientifically and pragmatically’), the importance of recognising and appreciating the complexity of systemic issues (‘embracing complexity’), and the need to facilitate commitment and engagement from important stakeholders (‘engaging and empowering’). These three principles are illustrated in two examples of improvement projects in community-acquired pneumonia and medicines management. Grounded in the practical reality of healthcare improvement, the SHIFT-Evidence framework is also accompanied by 12 ‘simple rules’ to guide evidence translation.
The articles included in this collection illustrate the value of iterative research approaches that are theoretically grounded, methodologically pluralistic, flexible and ecologically focused. They adopt a range of approaches to produce grounded explanations of what happened when someone attempted to achieve change in a complex, fast-changing healthcare environment. None of the papers offers simple solutions, predictive tools or universal formulae (though some submissions that were rejected from this call did purport to ‘solve’ complexity in this way). This series builds on the creative work of other research teams who have taken a nuanced and theory-informed approach to the study of complexity in healthcare - for example, Dixon-Woods et al.  on ex-post theorising of patient safety initiatives, Nugus et al.  on integrated care in the emergency department, and Lanham et al.  on scale-up and spread in healthcare.
All these studies engage, in different ways, with what Tsoukas has called ‘conjunctive theorising’, that is, avoiding the temptation to simplify and abstract (an approach which Tsoukas calls ‘disjunctive theorising’), towards an approach to theory that generates rich pictures of complex phenomena by drawing together different kinds of data from multiple sources . Conjunctive theorising, proposes Tsoukas, is characterised by an open-world ontology (viewing the world as subject to multiple interacting influences, and recognising that it serves no useful analytic purpose to strip away these layers of influence in artificial simplifications), a performative epistemology (that is, a focus on real-world action and on what becomes possible through action), and a poetic praxeology (that is, a way of writing up case studies that values descriptive detail, apt metaphor and narrative coherence).
The case studies in this series suggest a number of high-level themes that could be explored further in future research calls. First, new research could address the general proposal that the health services research community should embrace a richer and more diverse methodological repertoire when researching complex systems. How, specifically, might that methodological repertoire be extended? Second, new research could respond to Tsoukas’ call for a retreat from simplification and abstraction, and explore how conjunctive theorising could extend the possibilities of the mixed-method case study . Third, research in real-world settings could generate new ways of working productively with imperfect (FUPS) data . Fourth, as Long et al.  have shown, there is much potential still to be explored in relation to the use of simulation modelling in the study of complexity in healthcare. Finally, we need to put more effort into developing theory-driven and empirically focused frameworks that can guide implementation and evaluation from a complexity perspective [10, 11].