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Therefore, in this article, we introduce a framework to support the automation of some key steps of risk management.
The activities carried out in the improvement programme can be described in terms of a simple reliability engineering framework involving the three steps of risk analysis, risk assessment and risk management being applied to the business process.
Key components of the framework are public stakeholder discussion in the problem formulation, scoping, and planning steps of risk assessment; increased transparency throughout the entire process; and tailoring risk assessments more closely to the risk context.
While pre-anesthetic anticipation and planning are essential steps of risk management, missing test results and incomplete transfer of information can promote erroneous conclusions leading to poor decisions that ultimately jeopardize patient safety [ 5].
The NRC (2009) recommended that "risk assessments should characterize and communicate uncertainty and variability in all key computational steps of risk assessments" while recognizing that "uncertainty analysis and characterization pose difficult technical issues, and in general related best practices have not been established".
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The first step of risk assessment is to identify the hazards that are present.
In this study, we focused on the risk characterization step of risk assessment.
This final step of risk assessment integrates dose response and exposure data to describe the overall nature and magnitude of risk.
Each step of risk assessment presents uncertainty, and it is difficult or impossible to comprehensively quantify the propagated effect of uncertainty introduced at multiple points in the analysis (Briggs et al. 2009; Levy et al. 2001).
This difference in scale requires an interpretative procedure to select an ecologically effective project alternative, and we propose a decision process involving several steps of hazard and risk analysis.
Here, we focus on the last two steps of the risk assessment process by estimating potential dose and risk quotient in scenarios of exposure regarding the local seasonality.
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