Human error assessment and reduction technique

Human Error Assessment and Reduction Technique (HEART) examines particular task types and their associated error probabilities using tables of task types and faaors which impact on the performance of the task. 

Human error probability assessments using the Human Error Assessment and Reduction Technique (HEART)... 

HEART Human error assessment and reduction technique. Relatively quick to apply, and understood by engineers for quantification of human errors. It is a generic one and has general use. 

Various human reliability assessment techniques may be used to evaluate the effectiveness of cross-checking activities - eg THERP (Technique for Human Error Rate Prediction) and HEART (Human Error Assessment and Reduction Technique). It is important that any assessment is made by a competent human reliability specialist and that it is based on information provided by the operators who actually carry out the filling operation. 

HEART Human error assessment and reduction technique... 

For some time there has been an interest in exploring the underlying reasons, as well as probabilities, of human error. As a result there are currently several models, each developed by separate groups of analysts working in this field. Estimation methods are described in the UKAEA document SRDA-Rll, 1995. The better known are HEART (Human Error Assessment and Reduction Technique), THERP (Technique for Human Error Rate Prediction), and TESEO (Empirical Technique to Estimate Operator Errors). 

The Human Error Assessment and Reduction Technique (HEART) structures PSF considerations for the analyst who uses these to modify basic error probabilities. It assumes that human reliability is dependent on the generic nature of the task and that the level of reliability will be consistent (given a likelihood within probabilistic limits) under perfect conditions. The reliability can be varied for non-perfect conditions using Error Producing Conditions (EPCs). 

Similarly, there many techniques to face and comhat human error such as technique for human error rate prediction (THERE), human error assessment and reduction (HEART), etc. already discussed in previous chapters. 

HEART Human Error rale Assessment and Reduction Technique Williams, 1988... 

Task analysis is a fundamental methodology in the assessment and reduction of human error. A very wide variety of different task analysis methods exist, and it would be impracticable to describe all these techniques in this chapter. Instead, the intention is to describe representative methodologies applicable to different types of task. Techniques that have actually been applied in the CPI will be emphasized. An extended review of task analysis techniques is available in Kirwan and Ainsworth (1993). 

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). 

HEART is an error quantification process that is quick to use. The process defines a set of generic error probabilities for the types of tasks being examined and identifies the error-producing conditions associated with them. For each of the error-producing conditions the human error probability is multiplied by the error-producing condition multiplier. The tool also provides some guidance on approaches towards error reduction. A human performance model-based technique utilising some standard probabilities. Data-based method to assess and reduce human error and improve operational performance. 

Principal Component Analysis (PCA) is performed on a human monitoring data base to assess its ability to identify relationships between variables and to assess the overall quality of the data. The analysis uncovers two unusual events that led to further investigation of the data. One, unusually high levels of chlordane related compounds were observed at one specific collection site. Two, a programming error is uncovered. Both events had gone unnoticed after conventional univariate statistical techniques were applied. These results Illustrate the usefulness of PCA in the reduction of multi-dimensioned data bases to allow for the visual inspection of data in a two dimensional plot. 

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