SECI Model in Data-Based Procedure for the Assessment of the Frailty State in Diabetic Patients

Abstract

The manuscript provides a theoretical consideration of the process of knowledge creation in the area of the complex medical domain and the case of unanswered medical uncertainties. We used the SECI model to help explain the challenges that must be faced and the complex structure of the procedures that stay herein. Typically, the SECI model proposed by Nonaka in 1994 represents the best-known conceptual framework for understanding organization knowledge generation processes. In this model, the knowledge is continuously converted and created within user practices, collaboration, interaction, and learning. This paper describes an application of the SECI model to the data-based procedure for assessing the frailty state of diabetic patients. We focused on effectively supporting collaboration and knowledge transfer between participating data analysts and medical experts. We used Exploratory Data Analysis, cut-off values extraction, and regression to create new knowledge (combination) based on the expressed tacit ones (externalization). Also, we used internalization and socialization to design experiments and describe the results achieved in the discussion. Finally, we could conclude that effective knowledge transfer, conversion, and creation, are the basis of every data-based diagnostic procedure. In the case of the complex medical domain, the role of the medical expert is more important than usual, and this aspect of knowledge creation is mainly unconscious in the scientific literature.