Risk assessment quantification techniques

Chapter 4 focuses on techniques which are applied to a new or existing system to optimize human performance or qualitatively predict errors. Chapter 5 shows how these teclmiques are applied to risk assessment, and also describes other techniques for the quantification of human error probabilities. Chapters 6 and 7 provide an overview of techniques for analyzing the underlying causes of incidents and accidents that have already occurred. 

In addition, the chapter will provide an overview of htunan reliability quantification techniques, and the relationship between these techniques and qualitative modeling. The chapter will also describe how human reliability is integrated into chemical process quantitative risk assessment (CPQRA). Both qualitative and quantitative techniques will be integrated within a framework called SPEAR (System for Predictive Error Analysis and Reduction). 

Because most research effort in the human reliability domain has focused on the quantification of error probabilities, a large number of techniques exist. However, a relatively small number of these techniques have actually been applied in practical risk assessments, and even fewer have been used in the CPI. For this reason, in this section only three techniques will be described in detail. More extensive reviews are available from other sources (e.g., Kirwan et al., 1988 Kirwan, 1990 Meister, 1984). Following a brief description of each technique, a case study will be provided to illustrate the application of the technique in practice. As emphasized in the early part of this chapter, quantification has to be preceded by a rigorous qualitative analysis in order to ensure that all errors with significant consequences are identified. If the qualitative analysis is incomplete, then quanhfication will be inaccurate. It is also important to be aware of the limitations of the accuracy of the data generally available... 

The goal of human error quantification is to produce error probabilities, building on task analysis and error identification techniques to provide a probabilistic risk assessment (PRA). This provides numerical estimates of error likelihood and of the probability of overall likelihood of system breakdown. Quantification of error is the most difficult aspect of HRA, often heavily reliant on expert judgement, rather than the more rigorous approach of actual observation and recording of error frequencies. Such techniques are little used in healthcare but have been successfully applied to anaesthesia (Pate-Cornell and Bea, 1992). Nevertheless, some hospital tasks, such as blood transfusion, are highly structured and the quantification of errors probabilities would seem to be eminently feasible (Lyons et al, 2004). 

Fortunately, the Bayesian approach allows incorporation of all available relevant information into the assessment of probabilities, ft allows quantification of both epistemic as well as aleatory imcertainties, and the combination of their effects into a single probability value of an undesirable event, or into a single probability distribution for the consequences of assumed risk. Once the uncertainties are determined, they can be propagated through the risk model using techniques such as Monte Carlo simulation. 

How successful these new risk management techniques are is the subject of a critical literature. The trend to the greater quantification of risk, for example, is hotly debated. The mathematical basis of the quantified risk assessments (QRAs) is disputed, especially where there are small numbers involved or where there are no reliable data to work from (Cohen, 1996 Toft, 1996). The interpretation of the data may prove difficult in a variety of ways. For example, the causes of a risk may not be clear, and even where they are clear the decision about what is an acceptable risk needs to be taken and that is essentially a political decision. Indeed some claim that the procedures themselves are value laden (Hood and Jones, 1996). A more extreme position negates the whole attempt to produce an objective measure of risk, arguing that all assessments are inherently subjective (Slovic, 1992). Difficulties with these measures and approaches were recognized by industry representatives and regulators alike. One of the... 

The Chemical Process Industry (CPI) uses various quantitative and qualitative techniques to assess the reliability and risk of process equipment, process systems, and chemical manufacturing operations. These techniques identify the interactions of equipment, systems, and persons that have potentially undesirable consequences. In the case of reliability analyses, the undesirable consequences (e.g., plant shutdown, excessive downtime, or production of off-specification product) are those incidents which reduce system profitability through loss of production and increased maintenance costs. In the case of risk analyses, the primary concerns are human injuries, environmental impacts, and system damage caused by occurrence of fires, explosions, toxic material releases, and related hazards. Quantification of risk in terms of the severity of the consequences and the likelihood of occurrence provides the manager of the system with an important decisionmaking tool. By using the results of a quantitative risk analysis, we are better able to answer such questions as, Which of several candidate systems poses the least risk Are risk reduction modifications necessary and What modifications would be most effective in reducing risk ... 

Monte Carlo analysis is a specific probabilistic assessment method that can be used to characterize health risks and their likelihood of occurrence based on a wide range of parameters (Shade and Jayjock 1997). The U.S. EPA s Stochastic Human Exposure and Dose Simulation (SHEDS) model allows for the quantification of exposures based on a probabilistic assessment of multiple exposure pathways and multiple routes of exposure (Mokhtari et al. 2006 US EPA 2003b). Additional applications of probabilistic techniques wiU be discussed in the section below on conducting an uncertainty analysis of reconstructed exposure values. 

Delayed enhancement MRI may prove to be a powerful noninvasive technique that based on quantification of scar burden can risk stratify patients for SCD and select patients who may need further therapy. Ongoing trials will clarify the role of MRI in risk stratification, and whether assessment of infarct mass will supplant LVEF as the best sole risk stratification tool. 

Techniques for the identification and evaluation of human error are typically labeled Human Reliability Assessments (HRA). A complete HRA starts with a definition of the problem and development of a task analysis to support the HRA (Kirwan, 2005). The core of HRA is the Human Error Identification (HEI) and Human Error Quantification (HEQ) stages, and several methods have been developed to specifically focus on these two areas. From these, control measures can be identified to reduce the overall system risk. 

Chapter 9 describes a framework for the identification and quantification of human error in fishing vessel operation, following a brief review of human error assessment techniques. This framework ranks the impact of human error and further integrates the available risk control options into the analysis. The approach uses Analytical Hierarchy Processing (AHP) theory to rank the preference of each control option. The advantages of employing the AHP technique are discussed and the integration of such a technique within the FSA framework is described. 

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