A methodology for collection and analysis of human error data based on a cognitive model: IDA Original Research Article

This paper presents a model-based human error taxonomy and data collection. The underlying model, IDA (described in two companion papers), is a cognitive model of behavior developed for analysis of the actions of nuclear power plant operating crew during abnormal situations. The taxonomy is established with reference to three external reference points (i.e. plant status, procedures, and crew) and four reference points internal to the model (i.e. information collected, diagnosis, decision, action). The taxonomy helps the analyst: (1) recognize errors as such; (2) categorize the error in terms of generic characteristics such as ‘error in selection of problem solving strategies’ and (3) identify the root causes of the error. The data collection methodology is summarized in post event operator interview and analysis summary forms. The root cause analysis methodology is illustrated using a subset of an actual event. Statistics, which extract generic characteristics of error prone behaviors and error prone situations are presented. Finally, applications of the human error data collection are reviewed. A primary benefit of this methodology is to define better symptom-based and other auxiliary procedures with associated training to minimize or preclude certain human errors. It also helps in design of control rooms, and in assessment of human error probabilities in the probabilistic risk assessment framework.