| Autor: |
Elena Zaitseva, Vitaly Levashenko, Jan Rabcan, Emil Krsak |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Symmetry, Vol 12, Iss 1, p 93 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2073-8994 |
| DOI: |
10.3390/sym12010093 |
| Popis: |
A structure function is one of the possible mathematical models of systems in reliability engineering. A structure function maps sets of component states into system performance levels. Methods of system reliability evaluation based on structure function representation are well established. A structure function can be formed based on completely specified data about system behavior. Such data for most real-world systems are incomplete and uncertain. The typical example is analysis and evaluation of the human factor. Therefore, the structure function is not used in human reliability analysis (HRA) typically. In this paper, a method for structure function construction is proposed based on incomplete and uncertain data in HRA. The proposed method application is considered for healthcare to evaluate medical error. This method is developed using a fuzzy decision tree (FDT), which allows all possible component states to be classified into classes of system performance levels. The structure function is constructed based on the decision table, which is formed according to the FDT. A case study for this method is considered by evaluating the human factor in healthcare: complications in the familiarization and exploitation of a new device in a hospital department are analyzed and evaluated. This evaluation shows the decreasing of medical errors in diagnosis after one year of device exploitation and a slight decrease in quality of diagnosis after two months of device exploitation. Numerical values of probabilities of medical error are calculated based on the proposed approach. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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