“As complex as things are today, everything will be more complex tomorrow.”— K. Kelly in Out of Control: The New Biology of Machines, Social Systems and the Economic World [1]“One question … is whether the implementation of radical organizational change in health care is actually the core issue … there are many small-scale improvements and experimental projects … thus the primary issue is one of evaluation and spread.”— L. Fitzgerald in Challenging Perspectives on Organizational Change in Health Care edited by L. Fitzgerald and A. M. McDermott [2]
Background
Shifting the paradigm
Concept | Linear causal thinking | Systems thinking |
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Ideas | One invention, operationalized | Reinvention, proliferation, reimplementation, discarding, and termination |
Innovator(s) | An entrepreneur with a fixed set of full-time people over time | Many entrepreneurs and other players, sometimes on-track and sometimes distracted, fluidly engaging and disengaging over time in a variety of roles |
Transaction | A defined network of people or firms working out details of an idea between themselves | Expanding, contracting, and flexing networks of partisan stakeholders who converge and diverge on ideas |
Context | The environment provides opportunities and constraints on the innovation process | The innovation process is captured by political and cultural features, and creates opponents or is constrained by multiple enacted environments |
Process | Simple, orderly, cumulative sequences of stages or phases | Multiple messy, imprecise journeys; many divergent, parallel and convergent paths; some related, others not |
Outcomes | Final result predictable; a stable new order comes into being | Final result indeterminate; many in-process perturbations, assessments and spinoffs; integration of any new order with old orders |
Bringing the systems view together
Complex Adaptive Systems (CAS) theory – raising the bar in the challenge to linearity
Glossary of terms | |
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Adaptation | The capacity to adjust to internal and external circumstances; usually thought of in terms of modifying behaviors over time |
Agents | The individual components of a complex system – typically, individuals, whose capacity for sense-making means they can learn and adapt their behaviors across time, or artefacts |
Complex Adaptive System | A dynamic, self-similar collectivity of interacting, adaptive agents and their artefacts |
Complexity | The behavior embedded in highly composite systems or models of systems with large numbers of interacting components (e.g., agents, artefacts and groups); their ongoing, repeated interactions create local rules and rich, collective behaviors |
Culture | The sum of the shared values, attitudes, and beliefs across part of or the whole of an organization (e.g., across the division of medicine, or an entire hospital or health service) |
Emergence | Behaviors that are built from smaller or simpler entities, the characteristics or properties of which arise through the interactions of those smaller or simpler entities; the larger entities are one level up in scale, and manifest as social structures, patterns, or properties |
Feedback loop | A recursive mechanism creating reciprocal behaviors that reverberate back in on themselves; a positive (self-reinforcing) feedback loop increases the rate of change of a factor, creating more of its own output; in a negative (self-correcting) feedback loop, the output responses dampen the change or modulate its direction |
Implementation science | The processes of translating research into practice, understanding what influences translational outcomes, and evaluating the adoption of interventions |
Network | An interlocking web of relationships or connections at varying levels of scale in a system; the agents or artefacts are the nodes and the relationships between them are lines or vectors, which together describe the structure of the interactions of the network’s membership |
Path dependence | Current events and circumstances are influenced, and can be determined, by prior events and circumstances, harking back to the origins of the entity or system; path dependence underpins the point that ‘history matters’ |
Perturbation | An internal or external disruption or unexpected event that affects normal patterned behaviors, structures or processes; often thought of as an external disturbance or interruption to the current state-of-affairs |
Self-organization | The way in which agents interact to coordinate their own circumstances, workplaces, processes and procedures, such that they order their work and they autonomously, or semi-autonomously, organize their localized behavior; this can occur passively or actively |
Sensemaking | Methods by which individuals figure out what is going on around them; a typically social process among agents in which they come to a shared meaning of their experience, and is necessary for action in the face of ambiguity or uncertainty |
Social network | A set of people who have relationships, communications, ties, or interactions that connect them |
System dynamics | An analytical modelling methodology used for problem solving, which combines qualitative and quantitative data and identifies the fundamental elements of a system, and how they influence one another over time |
Tipping point | A critical point in a system in which a kind of radical, potentially irreversible, change may occur, resulting in a different state of system behavior, which can settle into a new equilibrium |
Enter implementation science
Features | Implementation science | Complexity science | Complexity science and implementation science |
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Task | The task is specific: getting evidence into clinical practice in an understandable way | The task is context dependent; properties of complexity apply to biology, ecology, physics, computer science, human social systems | Tailored solutions and iterative processes |
Theoretical assumptions | Heterogeneous and diverse – numerous theories, frameworks, and models | Homogenous – core assumptions of complexity science are characterized by ‘universality’ (i.e., they apply across all complex systems) | Different theories, frameworks, and models require an understanding of complexity features such as unpredictability, uncertainty, emergence, interconnection |
The intervention | To be standardized to permit generalizability | To be adapted to meet needs | Factoring in complex interventions and complex settings |
The context | Full of confounders, a ‘problem’ to be solved for successful implementation | An intrinsic part of a complex system; a dynamic environment that must be factored in for any intervention to be successfully taken up | For improvement to be realized, the context must be re-etched or re-inscribed such that its culture, politics, and characteristics are altered |
Historical underpinnings | Evidence-based practice movement, statistics, and the scientific method | Systems theory, chaos theory; emanating from diverse scientific disciplines | More sophisticated change models can be encouraged to arise over time |
Aims within health services research | - Describing or guiding the process of translating research into practice (process models) - Understanding or explaining what influences implementation outcomes (determinant frameworks, classic theories, implementation theories) - Evaluating implementation (evaluation frameworks) | - Description of complex system • Understanding context • Relationships among agents • Dynamics • How rules and governance structures emerge, i.e., self-organization - For prediction rather than implementation | - Ensure that turning evidence into practice is accomplished without too many unintended negative consequences; improvement might be sustained, potentially through the adaptation of the intervention to different settings - Implementation is not merely based on effective planning but anticipation of a range of possible outcomes |
Tools and methods | Randomized controlled trials, behavior change interventions, step-wedge designs | Causal loop diagrams, system dynamics modelling, network articulations | Realist evaluation, long-term case study, participatory research, stakeholder analysis, systems mapping, social network analysis |
Informing implementation with complexity
Case 1: Rapid response systems and the New South Wales ‘Between the Flags’ (BtF) program
Case 2: New nation-wide safety and quality standards
Discussion
Selected implementation or complexity characteristic | Case 1: Rapid response systems’ adoption and spread | Case 2: Introduction of national quality standards |
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Overarching strategy and implementation sequence | Bottom-up followed by top-down implementation, with middle-out support | Top-down with localized middle-out and then bottom-up acceptance |
Adaptation | Localized arrangements, then accommodating to an agreed, state-wide model | Legislated authority; brokered national agreement following extensive consultations |
Agents | Clinicians in intensive care units; later, managers and policymakers; acceptance by admitting clinicians in wards | Policymakers and regulators; accreditation agencies; organizational adoption |
Culture | Positive values and attitudes amongst intensivists; eventual behavior and practice change across the system | Policy enactment from the highest levels as a driver of eventual change through the hierarchy |
Feedback | Local clinicians influencing each other recursively for many years; eventually, formal design and implementation to reinforce and institutionalize the agreed framework | Policy implementation model: Ministerial endorsement, ongoing consultation and education leading to dampening of opposition and widespread take-up and adoption |
Networks | Intensive care physicians as prime movers; later, policymakers, managers, and other clinicians | Policy and accreditation bodies, with research partners lending expertise and support |
Path dependence | Thirty years in the making, leading to eventual acceptance against systems and clinical inertia | Ten years of policy and managerial discussion and maneuvering before implementation |
Type of perturbation | Gradual radiation of acceptance over time nationally and internationally | Legislation as an enabler, acting as an initial mover |
Self-organization | Intensive care physicians particularly; followed by whole-of-system acceptance | Influence groups of policymakers, managers and academics followed by big-bang introduction |
Tipping point | Growing acceptance by clinicians leading to leaders eventually invoking the authority of the Clinical Excellence Commission | Ministerial authority, legislative enactment, sustained pressure from peak bodies, eventual system-wide acceptance |