Health research, teaching and provision of care: applying a new approach based on complex systems and a knowledge translation complexity network model

Despite increased emphasis on the translation of research-based knowledge into practice, studies in the U.S.A. and Australia have found that up to 50 per cent of health care delivered does not accord with evidence-based guidelines. Health research, teaching and practice have traditionally emphasised defined inputs to produce specific, linear outputs and changes in teaching and practice may suffer delays in implementation when required to overcome barriers around spheres of interest. We are exploring a new approach based on the principles of complex systems and networks. In this paper, we used a successful knowledge translation project and a case study of a natural disaster, to model the effective application of these principles to a new health knowledge translation model, the Knowledge Translation Complexity Network. Following the Indian Ocean Tsunami of 2004, there were major challenges in identifying many of the dead. Research identified that Dental Age could be used to estimate the chronological age of unidentified victims up to 20 years of age. However, at the time the existing data were insufficient for this purpose and one author (HL) undertook to lead a knowledge creation and synthesis project. The research was evaluated by peer review, published in a leading journal and was subsequently implemented into practice as an identification tool in both paper and electronic forms. Subsequently the data charts and instructions have been translated into 18 languages and are used internationally in university teaching courses as well as in disaster identifications, with feedback evaluation from users providing further refinement. In conclusion, the development of the dentitions showed the characteristics of a complex adaptive system; of emergence, self-organisation, dynamic interactions, robustness and co-evolution. Further, the Dental Atlas incorporated elements of the key sub-networks of the new Knowledge Translation Complexity Network of problem identification (PI), knowledge creation (KC), knowledge synthesis (KS), implementation (I) and evaluation (E). Investigating real-world examples in this way can both highlight key aspects for future planning and identify gaps for development.