Overview
Definitions of Error
Basic Tenets of Human Error
Human Factors Engineering
Human Performance
Vocabulary
Types of Errors
Systems to Reduce Errors
Stroop Test
Swiss Cheese Model
Toxic Cascades
Lessons from Other Industries
Basic Safety Principles
Summary

Human Factors Engineering

Human factors engineering (HFE) is the science of designing systems to fit human capabilities and limitations. These include limitations in perception, cognition, and physical performance. HFE involves the application of specific methods and tools in the design of systems (e.g., human-centered design).

Human information processing is influenced by multiple factors:

  • Attention – may be limited in duration or focus, especially if attention to several things is necessary
  • Memory constraints – working memory is limited, especially when active processing of information is required
  • Automaticity – consistent, overlearned responses may become automatic, and completed without conscious thought
  • Situation awareness – a person’s perception of elements in the environment may affect their processing of information

Humans have certain tendencies and biases that can predispose them to error. These heuristics are usually very useful and successful, but at times can get us into trouble.

  • People avoid careful reasoning, preferring to pattern-match
  • Given uncertainty, people will choose what has worked before
  • Availability heuristic – giving undue weight to facts that come readily to mind, and ignoring that which is not immediately present
  • Confirmation bias – once a decision is reached, there is a tendency to seek evidence to support it
  • Selectivity – focus of attention on what is logically important vs. what is psychologically salient
  • Frequency gambling – betting on the condition that occurs most frequently (this is not always undesirable in medicine, as common conditions are more likely than uncommon—but we do need to keep our minds open to the possibility of something unusual)

 

Tversky A, Kahneman D. Judgment under uncertainty:
Heuristics and biases. Science 1974; 185:1124-31.


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