Reliability analysis for patient safety in the healthcare sector using dual hesitant pythagorean fuzzy set

Currently, in healthcare sector, the relevance of fuzzified data in medical field play key role for patient safety. As of this moment, the majority of hospitals pay more attention to the well-being of their patients by reviewing previous medical errors, training their staff over medical facilities, and also by providing instructions to their patients, so that the likelihood of any catastrophes decreases with a good rate. Due to practical considerations, the data that is generally available is not always sufficient for clinical treatment up to desired level of accuracy. Therefore, it is important to carefully assess the results because there may be some uncertainties in the data that was collected. This study investigates the reliability of a fault tree for medication delivery to patients, utilizing a dual hesitant fuzzy set approach. The research employs a generalized reliability approach to analyze fault trees, leveraging the reliability models of series, parallel, and bridge configuration. The analysis integrates fuzzified data to account for uncertainty and imprecision in the reliability assessments. This paper introduces a novel approach of dual hesitant fuzzy sets to fault tree analysis, providing a critical evaluation of system reliability in the context of medication delivery. Here, the integration of OR and AND logic gates within the fuzzy framework offers a comprehensive enhancement over traditional methods. The results demonstrate that the newly proposed fuzzy fault tree model significantly improves the reliability assessment compared to conventional approaches. The findings have important implications for healthcare systems, particularly in improving the reliability of medication delivery processes. By adopting this enhanced fault tree analysis, practitioners can better anticipate and mitigate potential failures, ultimately leading to safer and more reliable patient care.