Complex adaptive system

About: Complex adaptive system is a research topic. Over the lifetime, 3190 publications have been published within this topic receiving 111947 citations. The topic is also known as: Complex adaptive system, CAS.

Mutual Empathy, Ambiguity, and the Implementation of Electronic Knowledge Management within the Complex Health System*

Abstract: Healthcare can be characterized as a complex adaptive system. New Zealand is recognized as having one of the highest rates of enmeshed clinical information and communication technology within this complex system. This paper describes the implementation of an integrated series of electronic clinical health knowledge management systems in a large New Zealand District Health Board. In combination with standard project management, the core implementation team utilized an action research reflective learning approach to enhance their capability to cope with emergent issues, and plan for each subsequent project stage. The emergent focus on "process" issues of connectedness, competency, and control were not the "technical" concerns the principal author was initially expecting, but can be understood through an appreciation of individual and group dynamics, system and complexity theories. In particular, mutual empathy for both self and others was identified as a core capability requirement to cope with the inherent ambiguity within complex systems. Introduction Healthcare can be characterized as a complex adaptive system. From the cellular to sociopolitical levels, multiple "agents" and systems interact across shadowy boundaries and contribute to our concepts of health and healthcare. Nonlinear dynamics and sensitivity to initial conditions are inherent; and small changes in one part of the system, or embedded system, can change the context and outcome of another part, leading to significant variability and emergence in health outcomes (Goldberger, 1996; McDaniel et al, 2003; Plesk & Greenhalgh, 2001; Wilson et a?., 2001). If we accept this complexity conceptualization of healthcare (while recognizing doubters and the risk of fadism: e.g., Price, 2004; Reid & Notcutt, 2002), then we need to appreciate the innate unpredictability of health outcomes. We need to appreciate the limitations of healthcare management that is unduly bounded by the search for increasing data analysis and prediction models that will allow this complexity to be controlled. Rather than focusing investment on increasingly complex and costly "rational" control and decision mechanisms, we should be building the capability to cope with and indeed exploit this inherent variability and emergence (Anderson et al, 2000; Fraser & Greenhalgh, 2001; Kurtz & Snowden, 2003; Lemak & Goodrick, 2003; Plesk & Wilson, 2001). Globally, information and communication technology (ICT) is increasingly being applied to the health system. Objectives and predicted benefits vary by stakeholder and system, but coordination, integration, safety, and efficiency are common themes (Institute of Medicine, 2000). There may be a range of views on the constituent parts or overall makeup of an ideal health knowledge management system. However, recurrent identified core features or principles that may be independent of place, time, or technology can be encapsulated in the mnemonic C.A.R.E. G.A.P.S. F.I.R.S.T. The system should enhance every stakeholder's "capacity to C.A.R.E."; that is, perform their integral Clinical, Administrative, Research, and Educational healthcare functions. The system should accommodate the complex and holistic environment in which it is enmeshed, while recognizing, connecting, and enabling all the key stakeholders, primarily General practitioners, Allied health services (including hospitals), Patients and their Supports, as well as being Fast, Intuitive, Robust, Stable, and Trustworthy (Orr, 2004; Standards Australia, 2001; Standards Australia/New Zealand, 2001; Sveiby, 2001; Wyatt, 2001). Historically, clinician-valued, cost-efficient systems that have sustainably delivered their predicted benefits have proven to be relatively elusive. A failure to recognize complexity, or a focus on trying to control the complexity of healthcare via increasing levels of data collection, analysis, and detailed "decision support" guideline or protocol creation, could explain, at least in part, this relative failure (Ash, 1 997; Ash et al, 2004; Berger & Kichak, 2004; Bryant, 1998; Garg, 2005; Heeks etal, 1999; Southon etal, 1997). …

Person-centred health care: a critical assessment of current and emerging research approaches.

Abstract: Rationale, aims and objectives Person-centred health care is prominent in international health care reforms. A shift to understanding and improving personal care at the point of delivery has generated debates about the nature of the person-centred research agenda. This paper purviews research paradigms that influence current person-centred research approaches and traditions that influence knowledge foundations in the field. It presents a synthesis of the emergent approaches and methodologies and highlights gaps between static academic research and the increasing accessibility of evaluation, informatics and big data from health information systems. Findings Paradigms in health services research range from theoretical to atheoretical, including positivist, interpretive, postmodern and pragmatic. Interpretivist (subjective) and positivist (objectivist) paradigms have been historically polarized. Yet, integrative and pragmatic approaches have emerged. Nevertheless, there is a tendency to reductionism, and to reduce personal experiences to metrics in the positivist paradigm. Integrating personalized information into clinical systems is increasingly driven by the pervasive health information technology, which raises many issues about the asymmetry and uncertainty in the flow of information to support personal health journeys. The flux and uncertainty of knowledge between and within paradigmatic or pragmatic approaches highlights the uncertainty and the ‘unorder and disorder’ in what is known and what it means. Transdisciplinary, complex adaptive systems theory with multi-ontology sense making provides an overarching framework for making sense of the complex dynamics in research progress. Conclusion A major challenge to current research paradigms is focus on the individualizing of care and enhancing experiences of persons in health settings. There is an urgent need for person-centred research to address this complex process. A transdisciplinary and complex systems approach provides a sense-making framework.

Monitoring in a complex world — seeking slow variables, a scaled focus, and speedier learning

Abstract: Recent developments from complex systems theorists provide new insights into our understanding and, hence, monitoring of rangelands. Designing monitoring systems around the slow variables should lead to the generation of information that could greatly improve decision making in an adaptive management context. Monitoring can also be far better focused on supporting faster development of local environmental knowledge when traditional experiential learning modes cannot always keep up with the increasingly rapid pace of change in rangelands. After casting the design and purpose of monitoring systems in theoretical terms, the authors draw on applications from southern Africa to confront these conceptual approaches with real world practice. Using two cases, one from a community based agro-pastoral system and the other from a conservation area, the authors examine how the theory and practice inform monitoring systems design. Does theory really guide us in developing useful monitoring systems when faced with the ...

Complexity and health professions education: a basic glossary

Abstract: Rationale and aims The study of health professions education in the context of complexity science and complex adaptive systems involves different concepts and terminology that are likely to be unfamiliar to many health professions educators.Alist of selected key terms and definitions from the literature of complexity science is provided to assist readers to navigate familiar territory from a different perspective. Terms and concepts include agent, attractor, bifurcation, chaos, co-evolution, collective variable, complex adaptive systems, complexity science, deterministic systems, dynamical system, edge of chaos, emergence, equilibrium, far from equilibrium, fuzzy boundaries, linear system, non-linear system, random, selforganization and self-similarity.

Facilitating evolutionary innovation by developmental modularity and variability

Abstract: Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of "evo-devo" computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.