A hybrid model to assess the risk of COVID-19 in hospital screening procedures under uncertain information

Abstract

The large-scale outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly since 2019 to various countries around the world, making “infection control” a topic of concern for governments, medical institutions, and the general population alike. This study proposes a risk assessment model based on a combination of Failure Mode and Effects Analysis (FMEA) and Multi-Criteria Decision-Making (MCDM) which can be applied to conduct a comprehensive risk analysis of the hospital disease screening process and explore the risk ranking issues. Faced with changes in the pandemic situation and high information uncertainty, this model uses Single Valued Trapezoidal Neutrosophic Sets (SVTNS) to integrate expert opinions and the Best-Worst Method (BWM) to find the risk factor weights. The SVTNS is then combined with the Reference Ideal Method (RIM) to calculate the ranking of risk modes and identify the priorities for improvement. The results show that “failure of physician judgment,” “inaccurate patient filling,” and “failure of negative pressure isolation room sterilization” are the three most risky failure modes in the screening and treatment process. This study addresses each of these high-risk failure modes and proposes recommendations for improvement so that decision-makers can prioritize improvements to reduce the incidence of hospital-acquired infections.