Fuzzy Approach to Computational Classification of Burnout—Preliminary Findings

Abstract

There is a common belief that medical professions generate more work-related stress and earlier job burnout. We tested two groups: study group 1: medical (physical therapists, n = 30), and study group 2: non-medical (informaticians, n = 30). The purpose of this study was to find new, more reliable models for calculating work-related stress and burnout in the two aforementioned different professional groups. In the paper, we focused on a new model of algorithm based on AI methods that extends the interpretability of the scale of results obtained using the MBI test. The outcomes of the Maslach Burnout Inventory (MBI) were analysed in both study groups. These became the starting point for the development of three different fuzzy models, from which, after comparison, the one best suited to the study groups and the way they were evaluated was selected. Among the patients participating in the study, the following results were obtained: MBI values expressed as median values were significantly higher in group 2 than in group 1. The computational analysis showed that the contribution of the different parts of the MBI test to the final score was unequal in both groups. AI allowed for optimal selection of the model parameters for the study group, from which an algorithm was created to optimise the selection of tools or their parameters. A computational tool can do this faster, more accurately, and more efficiently, becoming an important supporting tool. In the medical context, the main benefit of the results presented in this paper is the definition of an evaluation model that transforms the MBI test scores into a universal percentage scale while preserving the properties of the guidelines underlying the MBI. An additional advantage of the proposed solution is the readability and flexibility resulting from the linguistic rules underlying the model.

Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring

“…Assumptions and rules of the data sources that are impossible to describe with traditional mathematical models are preserved. So far, the hierarchical fuzzy systems model has proved effective in biomedical and industrial applications, which confirms the advisability of its selection for use in the present study [ 19 , 20 , 21 , 22 , 23 ]. The hierarchical modular design improves the deterioration detection and assessment procedures’ transparency, flexibility and scalability.…”

“…The proposed model develops a simplified fuzzy approach from the work of Prokopowicz and Mikołajewski [ 15 , 21 , 23 ]. It is a three-level hierarchical system based on the division into three main modules: Mental state assessment module, collecting data from PSS10 and SWLS; Burnout assessment module collecting data from MBI (in three areas: emotions, depersonalization and lack of achievement); A physical condition assessment module, collecting data from NMQ ( Table 7 ).…”

“…We provided a summary of the methods, materials, and performance used in our previous articles on modeling job burnout [ 15 , 21 ]. There, we had three fuzzy systems that we compared in terms of accuracy in reflecting the results of aggregate quality of life and job burnout tests.…”

“…The final-tier system’s purpose is the aggregation of the results from the modules. Such a structure (and configuration) is the result of evolution during research conducted in the last few years and described in [ 15 , 21 , 23 ]. It is worth noting that the structure of the final-tier fuzzy system is the same as for Module 3, presented above.…”

“…The basis for the implementation of data processing has been developed in previous publications [ 15 , 21 ], where various attempts to define the model were analyzed.…”