Cynefin Framework in Healthcare

The scheme of Cynefin framework acts as a guidance to the healthcare practitioners and researchers because of its foundation in the management of information (Snowden, 2007). This particular tool was developed with an aim to offer support and right direction in the process of decision making in those situation where the existing intricacy within the outcomes affects the nature of knowledge, forecast, and choice (Snowden, 2007). It has varied domains which necessitate different actions for instance the straightforward and the complex context is considered equivalent to an “ordered state” of universe which can be interpreted based on the causal and effect association of the facts or findings and therefore the right orientation or pattern can be decoded (Kempermann, 2017). However, in case of “complicated or chaotic” data outcomes where the researchers or the healthcare practitioners are unable to formulate the cause effect association apparently as there is no such immediate conceivable relationship, there the Cynefin framework guides the professionals to choose the right orientation based on the “emerging patterns” (Kempermann, 2017). This means that the chaotic or unordered state of the world requires pattern dependent management for proper orientation and right decision making (Kempermann, 2017). which can significantly benefit those seeking healthcare dissertation help

According to scientific evidence the parts or the components of the complex system may not show direct association in a linear pattern. These factors or parameters are openly prevailing within the environment due to which the interactions in between those factors can occur at varied levels via recursive feedback loops (Gray, B., 2017).

The above Fig: 2 refers to the cause effect association in between the parameters decoded with the aid of Cynefin framework (Sturmberg, 2009). In the figure the square block indicates the “causal agent” and the dots indicate the “effect agent” (Sturmberg, 2009). The proper lines refer the direct association in between the two agents whereas the dotted lines refer to the weak or probable association in between two agents. When the relation or the conduct of the components of the complex adaptable systems cannot be perceived in direct terms it is said to be emergent and active in nature. Moreover, these factors again show alterations with time and pressure of the surrounding environment due to which it develops into a new form (Sturmberg, 2009).

The concept of medicine is complex and the complexity that arises at the time of general practice varies widely (Gray, B., 2017). The approach of general practice formulates expertises based on the overall profile of the patients. In this regard, the theory of complexity demonstrates that these associations between varied confounding factors are complimentary in nature (Gray, B., 2017). The clinical specialist has to deal with the complex domain frequently during the practice. It is evident that detailed analysis using statistical or analytical tools (AI) along with investigation and specialised knowledge of the specialist helps to provide excellent solutions to complicated problems for instance cataract replacement problems and factors that lead to the development of cataracts (Kempermann, 2017). The clinical specialist often finds problems within the disordered or chaotic zone as the data do not show any direct or known association between the varied agents however, with the help of this tool the practitioners or the researchers can recognize the probable underlying cause which would be of immense help in the field of healthcare. This aspect is important a particular medical problem can arise due to varied domains and the recognition of the right one is essential form a complex chunk of chaotic raw data along with the identification of the emerging pattern. The developer of the tool named Snowden highlighted that the concept of “best practice” is attributable to identifiable or known problems; for the complicated situations the “good practice” is advisable and for the complex problems the “emergent practice” is considered to be the most suitable (Kempermann, 2017). Therefore, it is scientifically evident that with the use of Cynefin framework the complex or chaotic data outcomes of study on ophthalmology can be solved to find an emergent association between the co-factors that cause the development of cataract and ultimately will bring awareness among the community (Kempermann, 2017).

References:

Snowden, D.J. and Boone, M.E., 2007. A leader's framework for decision making. Harvard business review, 85(11), p.68.

Kempermann, G., 2017. Cynefin as reference framework to facilitate insight and decision-making in complex contexts of biomedical research. Frontiers in neuroscience, 11, p.634.

Gray, B., 2017. The Cynefin framework: applying an understanding of complexity to medicine. Journal of Primary Health Care, 9(4), pp.258-261.

Sturmberg JP, Martin CM. Complexity and health–yesterday’s traditions, tomorrow’s future. J Eval Clin Pract. 2009 Jun 1;15(3):543-8.