Plenty data are generated each second under different locations and ways. In return, several valuable data are not used due to neglect or a wrong criteria selec-tion, affecting the results. To have the accurate decision the exact information is needed. But, in case of lack of data, an overall vision about all existing findings make a big difference especially in Medicine.
The approach of Systematic Literature Review give the possibility to have this clear vision related to a specific topic, for the right input. We aim to identify all important steps to conduct a high quality of systematic literature review, independently of research domain.
We first explain the reason behind our research and the significance of Systematic Literature Review in research fields, one of the way for minimizing the loss of infor-mation. Since our goal is to have an explicit methodolo-gy, we select several papers mentioning the procedure in different years to have a general view. Subsequently, we explain the approaches used to select the key steps to follow for selected articles.
We settle this study with a complete step resuming all finding key phases. This research highlight also the im-portance of the use of an explicit methodology to vanish any misunderstanding or ignoring basic points to accom-plish a valuable and high quality work.
This paper make a clear complete methodology to fol-low, in any domain and specially medicine, to conduct a best quality of Systematic Literature Review and reach the right gain using best practices.

The digital transformation of the company is a condition for the socio-economic development. Dealing with ICT integration in the enterprise, the paper’s aim is the identification of the digital transformation conceptual components, required for designing a knowledge model.
To do this, a literature review is established to identify the dimensions, and their interrelations, to consider in the construction of a model, and which led to an ontology-driven model for digital transformation’s knowledge.
In comparison with other models proposed in the literature, this ontology is exhaustive in terms of knowledge, adaptable to any sector of activity and scalable in terms of dimensions and relationships composing it.

Driver behavior is the main cause of road crashes; it is the key element that insures a better understanding and improves predictions of car accidents. The main goal of our study is to determine the set of driver behavior features that are the most encountered in literature; we were based on behavioral questionnaires as a source for these features. We selected the questionnaires that are most cited in literature and therefore proved their efficiency through many studies they were employed in. Then we extracted the features considered in their items and classified them by rate of appearance according to the Pareto & ABC principle. In the second part of our study we collaborated with the National Committee for Circulation Accident Prevention (CNPAC) of the Ministry of Transportation of Morocco in order to compare the findings we gathered from literature with the researches they administer. We prepared a questionnaire that contains the final set of features and we transmitted it to experts working in the road safety field to rate it according to their knowledge and experience. Data analysis showed significant differences in some features, which demonstrates the gap between theoretical results and field research.