Uncertainty and unpredictability are a significant part of healthcare, and the ability to adapt and change quickly is something that health professionals are required to do on a daily basis. However, when it comes to more substantive practice change that relies on change to systems and processes in healthcare, the challenge seems far greater. Decision-making in these complex and inimitable contexts is based on many sources of information as well as tacit and explicit knowledge. Langton has termed the set of circumstances that call for adaptive behaviors "the edge of chaos".1

Systematic reviews, the principal document type published in the JBI Database of Systematic Reviews and Implementation Reports, are arguably the platform upon which many knowledge translation (KT) attempts are built in health services and government departments (from a policy and practice development perspective). However, the KT movement is largely about "discourse" (dialogue and communication). It involves the creation of strategies to implement the results of these syntheses in policy and practice to inform decision-making at the point of care.

Most of us would agree that, with all this in mind, KT is a "fuzzy" process. Although many academics and clinicians have attempted to establish rules and guidelines for how it might best be achieved, it often remains elusive. This is because organizational systems and cultures are equally fuzzy and hence the discourse around knowledge translation is a vital component of any implementation endeavor.

"Fuzzy logic" applications have been used predominantly in computer science, mathematics and linguistics, but the principle could just as easily be utilized in complex organizations when change is required. Fuzzy logic is a methodology designed to deal with imprecision and uncertainty, and it has already been applied to the development of clinical decision support systems. Researchers from the University of South Australia state "vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture."2(p. 1) It is important to have an understanding of how this might be articulated at both the systems and individual level with respect to KT and this will be explored in the paragraphs that follow.

Healthcare systems are inimitable, complex and ever changing. Organizations like this are sometimes referred to as complex adaptive systems. Ultimately, complex systems tend to exhibit "self-organizing" behavior: starting in a random state, they usually evolve toward order instead of disorder.3 What might be possible here is a semantic conceptual adaptation of fuzzy logic as applied to hospital and healthcare settings. Fuzzy logic was developed in order to handle the concept of partial truth values and, in complex health systems with countless changing variables, it is not always possible to transition (or translate) one practice to another in a sharp, precise fashion. There is no "silver bullet" for implementation. This approach is designed for intelligent reasoning where organizational and individual variables are ill defined and can change significantly across contexts locally, nationally and internationally.

Of course people run health systems, whether they are health professionals (from a broad range of disciplines), managers or administrators. To achieve successful KT in complex systems it is important to acknowledge that there are various "actors", "agents" or "participants" within any implementation scenario, and that these will be different from one setting (or system) to another and bring with them different backgrounds (whether related to culture, generation, discipline, gender or ethnicity) that will impact on how they relate to others in the multidisciplinary team. Not only will they behave according to their own mental models, which are not fixed, but equally the systems in which these individuals work influence and are influenced by other systems.

According to Scollon, Scollon and Jones (2001), each of us simultaneously participates in many different "discourse systems" (gender, occupation, ethnicity, generation) and as a result nearly all communication is across some lines which divide us according to the systems of discourse in which we participate.4 What this means is that communication in these settings is often even more complicated than we think and is influenced by far more than we appreciate. Scollon and colleagues also argue that culture is a verb and that the way in which people "do" culture may be different at different times and in different circumstances. Thus they suggest that rather than trying to understand the culture of a group it is more important to understand what they are doing and what "tools" they have at their disposal to do it. The unit of analysis thus becomes not systems of culture, but people doing things and this is true also of KT.

In light of the above, "discourse" forms part of the toolkit we utilize in KT. A discursive approach to KT means that the focus is less on the system and more on the individual. It provides conversational mechanisms that address issues of interdisciplinary dialogues related to power, evidence and resistance to change in evolving environments. So what does this mean for those undertaking the evidence synthesis component of the KT process? Well, it means that KT is more than just the science of synthesizing evidence. There is an end point and that end point is clinical practice. Therefore the questions that we ask of the evidence need to be cognizant of the questions that require answers at the point of care. These discourse communities (i.e. the synthesis discourse community and the implementation discourse community) need to be aware of each other and each other's needs if KT is to even come close to being successful. The way in which this is articulated from a pragmatic standpoint might include multidisciplinary review teams, or review teams that are connected with clinical settings where health professionals drive review generation and question development, for example. In this way, more meaningful recommendations can be made to inform practice, which consider the system and the individuals within it.

It is a vital component of "closing the gap" between what we know and what we do.

Acting Executive Director, Joanna Briggs Institute

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