Human performance-shaping factor

Insufficient criteria for choosing human Performance Shaping Factors (PSF s)... 

Finally, the third generation of human reliability methods, started in 2005 and continues today (Di Pasquale et al., 2013), focusing on human performance shaping factors, relations, and dependencies. The only method now defined as third generation is Nuclear Action Reliability Assessment (NARA), Kirwan et al. (2005). 

An early example of a causal factors checklist is Swain s checklist on human-performance-shaping factors (Swain, 1974). It is human-factor oriented and lists factors that affect the quality of human performance and thus the likelihood of human errors. Table 6.11 shows an extract from this checklist. 

Table 6.11 Extracts from Swain s checklist on human-performance-shaping factors...

Checklists in Tables 26.2 and 26.3 support the human-factor evaluation. First, the analysis team evaluates whether the human error is related to wrong detection or diagnosis of the disturbed situation or to erroneous execution of action. Thereafter, the team looks into causes. Here a variation of Swain s checklist in Table 6.11 on human performance-shaping factors is used. The checklist includes items related to working conditions, physiological and psychological stresses and expected individual characteristics of the operators. The results of the human-factor evaluation are documented in columns three and four. Finally, the team makes an overall evaluation and comes up with recommendations on remedial actions. 

Human errors may be dependent on the specific accident sequence displayed in the event tree, and, for that reason, may be included in the event tree. This requires the human-factors specialist to consider the context of the error in terms of stress, operator training in response to the accident, di.tgnosiic paiierns, environmental, and other performance-shaping factors. 

A PSA analyst is usually interested in determining the probability of error for a task first case, no HRA ev ent tree is needed unless performance on that task is affected by other factors whose probabilities should be diagramed. A description of the ta.sk and knowledge of the performance-shaping factors are sufficient to determine the probability of a single human error. 

Such a task description invites task analysis, which would lead naturally to human reliability analysis (HRA). Indeed, perhaps the earliest work in this field applied HRA techniques to construct fault trees for aircraft structural inspection (Lock and Strutt 1985). The HRA tradition lists task steps, such as expanded versions of the generic functions above, lists possible errors for each step, then compiles performance shaping factors for each error. Such an approach was tried early in the FAA s human factors initiative (Drury et al. 1990) but was ultimately seen as difficult to use because of the sheer number of possible errors and PSFs. It is occasionally revised, such as in the current FRANCIE project (Haney 1999), using a much expanded framework that incorporates inspection as one of a number of possible maintenance tasks. Other attempts have been made to apply some of the richer human error models (e.g.. Reason 1990 Hollnagel 1997 Rouse 1985) to inspection activities (La-toreUa and Drury 1992 Prabhu and Drury 1992 Latorella and Prabhu 2000) to inspection tasks. These have given a broader understanding of the possible errors but have not helped better define the PoD curve needed to ensure continuing airworthiness of the civil air fleet. 

SLIM-MAUD (Embrey 1984) implements a related approach in which expert ratings are used to estimate human error probabilities (HEPs) in various environments. The experts first rate a set of tasks in terms of performance-shaping factors (PSFs) that are present. Tasks with known HEPs are used as upper and lower anchor values. The experts also judge the importance of individual PSFs. A subjective likelihood index (SLI) is then calculated for each task in terms of the PSFs. A logarithmic relationship is assumed between the HEP and SLI, allowing calculation of the human error probability for task j (HEPj) from the subjective likelihood index assigned to task j (SLIj). More specifically ... 

A data-informed model of performance shaping factors and their interdependencies for use in human reliability analysis... 

In the first generation of HRA methods, human failure was seen and investigated as random phenomenon, with some distribution in time formed by performance shaping factors influence. In HRA second generation method/framework ATHEANA, treatment of human failure is different, as it is seen as cause based consequence of error forcing context actuation. Still, the plant specific experience can lead to the conclusion that some residual randomness should be kept in hiunan failure model, similarly to the case of (equipment) dependent errors and residual common cause failures. 

HMI specifications and applied solutions as Piping and Instrumentation Diagrams (P ID), data formats, types of data obtained, ergonomics of control room and organizational factors directly influencing on operator performance and should be incorporated into HRA analysis. Under consideration should also be taken the performance shaping factors (PSFs), such as stress, estabUshed procedures affecting human operator performance, etc. 

Modeller (TM), a method of task decomposition and prediction of task deviations. The TM also identifies critical Performance Shaping Factors (PSFs) that influence task performance and provides input to the Fuzzy Probabihty Estimator (FPE) for the quantification of human errors. 

The specific case study aims at analyzing the startup of a gas turbine used to drive the compressor of the butane/propane refrigeration section of an LPG storage and treatment complex. For the start up of the turbine a very specific and detailed sequence of actions is performed. The completion of actions includes tasks performed by operators from the control room as well as by on-site operators. Performance Shaping Factors (PSFs) that influence operators reliability have been identified and their quality has been rated for the specific site according to expert judgment from safety experts and on site observations from the human factor experts of the project. 

The focus of VR tool for the Risk Assessment is the investigation and integration of Human Factors (HF) aspects and loss or delays on safety-critical tasks using the information and output data from VR experiments, that represent in a real way the accidental scenarios identified. Human Errors and Performance Shaping Factors (PSFs) resulting in accidental scenario can be investigated by means of post-experiments replays and reviews. 

The EFC is defined as the situation that arises when particular combinations of performance shaping factors (PSFs) and plant conditions create an enviromnent in which unsafe actions (UA) are more likely to occur. The UA is a mode of human fadure that results in the Human Fadure Event (HFE) and, thus, is a specific inappropriate action taken (error of commission) or not taken when needed (error of omission), that results in a degraded plant condition. 

While human reliabihty analysis (HRA) has been well established and integrated into safety analysis in other industries (nuclear, aviation...) its application to healthcare is limited (Lyons, et.al. 2004). HRA studies human operator performance in the context of a specific task enviromnent. It is often focused on estimating the probability of human error, and how this probability might increase or decrease when coupled with various performance shaping factors. 

It is also necessary to be mindful of the fact that human performance in general is very heavily influenced by the conditions under which the operator performs. These conditions are known as performance shaping factors, and can help to further clarify why an error occurred, and also provide a great deal of extra information to help specify a practical solution. 

Human reliability analysis (HRA) With technological development and incorporation of redundancy it is possible to reduce equipment failure to a great extent. However, human behavior is not that predictable. So, there are chances that failure could occur because of human factors. This is a method by which probability is measured. It is also used in PFLA. This could be quantitative as well as qualitative. Although the exact value is not certain it is estimated that error committed by a human could be as high as 60—80% (even 90%). Human performance is affected by several factors, referred to as the performance shaping factor (PSF). By this method, PSF is identified and tries to improve it. In addition to PSF, normal human error probability (HEP) is also calculated on the basis of human activity. There are so many factors that affect this analysis accuracy, reproducibility, bias, etc. There have been several methods and each needs to be understood before application. An HRA event tree is often used. It may be informative to refer to Table V/1.0-1 (Chapter V). 

This human error probability could then be modified based on the performance shaping factors or error producing conditions relating to the people carrying out the task and the conditions under which they are working. 

Human performance is determined by certain factors that influence how people act. These factors are called performance-shaping factors (PSFs). PSFs are usually a complex confluence of itans that affect the operator in a system and are divided into external PSFs, internal PSFs, and stressor PSFs. PSFs can greatly affect how safely a system is operated. 

Boring, R.L., Griffith, C.D. Joe, J.C. 2007. The Measure of Human Error Direct and Indirect Performance Shaping Factors (ed. ). In Human Factors and Power Plants and HPRCT ISth Annual Meeting. IEEE, 170-176. 

Quantitative methods emphasized are HRA methods based on a proposed set of selection criteria. We suggest SPAR-H as a simple and usable method for space. The method is developed for the US Nuclear Research commission. The method is applicable for selected scenarios to evaluate probabilities and areas of great concern for the space domain. SPAR-H accounts for the context associated with Human Failure Events (HFEs) by using Performance Shaping Factors (PSF) (Gertman et al. 2005). 

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