BackgroundImplementation science has a core aim – to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline.DiscussionThe knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.ConclusionConstruing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.

Rationale, aims, and objectives“Implementation science,” the scientific study of methods translating research findings into practical, useful outcomes, is contested and complex, with unpredictable use of results from routine clinical practice and different levels of continuing assessment of implementable interventions. The authors aim to reveal how implementation science is presented and understood in health services research contexts and clarify the foundational concepts: diffusion, dissemination, implementation, adoption, and sustainability, to progress knowledge in the field.MethodImplementation science models, theories, and frameworks are critiqued, and their value for laying the groundwork from which to implement a study's findings is emphasised. The paper highlights the challenges of turning research findings into practical outcomes that can be successfully implemented and the need for support from change agents, to ensure improvements to health care provision, health systems, and policy. The paper examines how researchers create implementation plans and what needs to be considered for study outputs to lead to sustainable interventions. This aspect needs clear planning, underpinned by appropriate theoretical paradigms that rigorously respond to a study's aims and objectives.ConclusionResearchers might benefit from a return to first principles in implementation science, whereby applications that result from research endeavours are both effective and readily disseminated and where interventions can be supported by appropriate health care personnel. These should be people specifically identified to promote change in service organisation, delivery, and policy that can be systematically evaluated over time, to ensure high‐quality, long‐term improvements to patients' health.

Rationale, aims, and objectives: The field of implementation science has developed in response to slow and inconsistent translation of evidence into practice. Despite utilizing increasingly sophisticated approaches to implementation, including applying a complexity science lens and conducting realist evaluations, challenges remain to getting the kinds of outcomes hoped for by implementation efforts. These include gaining access and buy-in from those implementing the change and accounting for the influence of local context. One emerging approach to address these challenges is embedded implementation research-a collaborative, adaptive approach to improvement. It involves researchers and implementers working together in situ from the outset of, and throughout, an implementation project. Both groups can benefit from the collaboration: it increases the rigor of evaluation, provides opportunities to improve the intervention through direct feedback, and promotes better on-the-ground understanding of the change process. We aimed to examine the potential benefits, and some of the challenges, of increased embeddedness. Method:We performed a multi-case analysis of implementation research projects that varied by degree of embeddedness.Results: Embedded implementation research may offer a range of advantages over dichotomized research-practice designs, including better understanding of local context and direct feedback to improve the implementation along the way. We present a model that spans four approaches: dichotomized research-practice, collaborative linking-up, partially-embedded, and deep immersion. Conclusion: Embedded implementation research approaches hold promise in comparison to traditional dichotomized-research practice designs, where the research is external to the implementation and conducts a summative evaluation. We are only beginning to understand how such partnerships operate in practice and what makes them successful. Our analysis suggests the time has come to consider such approaches. Box 1 : Terminology Action research (or participatory action research): Researchers engage health professionals in the design and implementation of research,working with them as opposed to externally studying them. 42,43 Co-located: The researcher is physically located within the setting of the implementation initiative to conduct collaborative research with implementers and end-users. 44 Co-creation: See Co-production of knowledge. 10Co-production of knowledge: Concerns the production and spread of knowledge in the context it will be used through the collaboration of relevant stakeholders. Co-production may occur at the design, implementation, or evaluation stages of a program (co-design, co-evaluation, and co-implementation, respectively). 19,22 Engaged scholarship: Research conducted in collaboration with stakeholders, eg, implementers and practitioners, to gain insight into the context and phenomenon being studied. 45 Insider academic research: Research carried out by someone employed within the organization under study. ...

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Background: Uncertainty is a ubiquitous and dynamic presence throughout healthcare systems and encounters, affecting the quality and safety of care. Although previous research has attempted to categorize varieties of uncertainty, it is not clear if these classifications are applicable across various healthcare settings.Objective: The purpose of this review was to examine the applicability of the issues of uncertainty delineated in an established taxonomy across diverse healthcare domains, professions, and countries and to consider the complexity of these issues.Methods: Drawing on empirical research from three databases, we conducted a scoping review of the literature to identify types of uncertainty experienced by healthcare professionals. A framework synthesis design was employed to review and synthesize the literature across multiple healthcare settings.Results: The search identified 2285 articles, of which 94 met the inclusion criteria.Findings from included studies suggested professional uncertainty in healthcare is complex and pervasive. On the basis of our inability to categorize some studies in the issues of uncertainty outlined in the existing taxonomy, we proposed a revised model of uncertainty for healthcare professionals.Conclusions: The revised model of uncertainty, the model of uncertainty in complex healthcare settings (MUCH-S), is applicable to various healthcare ecosystems and proposes a reflexive archetype that recognizes different issues of uncertainty while establishing that these are often interrelated in healthcare systems. This review offers healthcare professionals greater levels of understanding of this complex phenomenon and may support more informed and reflective decision-making, assisting them to better navigate uncertainties experienced in healthcare workplaces.

Background Patient‐reported outcome measures (PROMs) are questionnaires that collect health outcomes directly from the people who experience them. This review critically synthesizes information on generic and selected condition‐specific PROMs to describe trends and contemporary issues regarding their development, validation and application. Methods We reviewed academic and grey literature on validated PROMs by searching databases, prominent websites, Google Scholar and Google Search. The identification of condition‐specific PROMs was limited to common conditions and those with a high burden of disease (eg cancers, cardiovascular disorders). Trends and contemporary issues in the development, validation and application of PROMs were critically evaluated. Results The search yielded 315 generic and condition‐specific PROMs. The largest numbers of measures were identified for generic PROMs, musculoskeletal conditions and cancers. The earliest published PROMs were in mental health‐related conditions. The number of PROMs grew substantially between 1980s and 2000s but slowed more recently. The number of publications discussing PROMs continues to increase. Issues identified include the use of computer‐adaptive testing and increasing concerns about the appropriateness of using PROMs developed and validated for specific purposes (eg research) for other reasons (eg clinical decision making). Conclusions The term PROM is a relatively new designation for a range of measures that have existed since at least the 1960s. Although literature on PROMs continues to expand, challenges remain in selecting reliable and valid tools that are fit‐for‐purpose from the many existing instruments. Patient or public contribution Consumers were not directly involved in this review; however, its outcome will be used in programmes that engage and partner with consumers.

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