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‘Learning Objectives of Error and Risk’. Give a critical account of generic models of error Understand the conceptual difficulties involved in the use of the term error To begin to be able to plan error prevention/minimisation strategies in organisations

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‘Learning Objectives of Error and Risk’

  • Give a critical account of generic models of error

  • Understand the conceptual difficulties involved in the use of the term error

  • To begin to be able to plan error prevention/minimisation strategies in organisations

  • Give a critical account of two models of risk taking behaviour

  • Evaluate the utility of models of risk taking behaviour in the workplace.



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Error

  • Conceptual Issues

  • Generic models

  • Prevention


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Conceptual Issues

  • What is an error?

  • Can you make an error while you are asleep?

  • Can you make an error by doing nothing?

  • If there are no negative outcomes from an ‘error’, has there been an error?


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Conceptual Issues (cont.)

  • Does an error involve intent?

  • What do you do to people who make errors?

  • How do organisational factors impinge on error?

  • Can one persons error be another's intended action?


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To What Extent is ‘blame’ useful

  • Is it fair?

  • Who might apportion blame?

  • Operators normally get blamed - this is cheap.

  • Allocation of individual responsibility removes moral responsibility from management


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Blame (cont.)

  • Does blame have a certain utility?

  • The term ‘error’ precipitates a mantle of causality around around those proximate to an event


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Emerging Themes

  • Errors are rarely issues of personal responsibility.

  • Errors are completely dependent on context.

  • Errors are normally distributed system failures not the result of one persons actions

  • Errors are multi-causal.


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Models of Error

  • Many variants

  • Slips vs. Mistakes

  • Omission vs. Commission

  • Skill, Rule and Knowledge based errors

  • Generic Error Modelling Systems


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Some Problems with these models

  • Generic models, by their nature play down the importance of context

  • The distinction between slips and mistakes - e.g... valve operation

  • The role of intention

  • The difficulty in distinguishing between skills rules and knowledge


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Problems (cont.)

  • Tend to be non falsifiable

  • Tend to be post hoc and descriptive, not prospective (this would allow effective planning)

  • The baby disappears with the bath water.


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Error Prevention

  • Not possible

  • So we try to

    • Reduce the probability of error

    • Design systems that degrade gracefully, rather than fail catastrophically

    • Allow error recovery


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What can be done to mitigate ‘error’ (general)

  • View errors as distributed failures of systems

  • Appreciate the complex causality of ‘error’

  • Operate a blame free culture - errors are very useful ways of learning about organisations and system performance


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Mitigation (gen.) cont...

  • Be flexible and responsive

  • Plan

  • Distinguish between prevention e.g... by design, and recovery

  • Recognise operators are part of system not separate from it.


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What can be done to mitigate ‘error’ (particular)

  • Train and inform designers, trainers, procedure writers, managers as well as operators - ensure very one has domain specific knowledge and is not far removed.

  • Train operators in dealing with unusual or unexpected systems configurations, not just in routine operations.

  • Ensure procedures are congruent with system characteristics


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Mitigation (particular - cont.)

  • Ensure management style is consistent and has no hidden agendas (q. is this possible)

  • Pay attention to communication between different professional groups

  • Pay attention to communications both within and between operations


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Continued

  • Learn from past mistakes in general terms

  • Institute operational feedback systems

    • e.g... CHIRPS

    • e.g... Operational feedback officers

  • Train ( properly)

  • Attend to organisational safety culture

  • Have management commitment to safety


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Automation - a particular issue

  • Creeping de-skilling

  • Opaque systems

  • Complex and unpredictable systems states

  • Additional reading on this topic ‘Ironies of Automation’ by Lisanne Bainbridge, in ‘New Technology and Human Error’ eds, Rasmussen, Duncan and Leplat’ LEA, 1987. Also Chapter by Brehmer.


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