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Automatic-controlled information processing and error detection in a simulated pharmacy-verification task.

Sean Reilly1, Anthony F Grasha, Gerald Matthews

  • 1Department of Psychology, Morehead State University, KY 40351, USA. s.reilley@morehead-st.edu

Perceptual and Motor Skills
|November 8, 2003
PubMed
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This study explored how attention and information processing impact pharmacy dispensing errors. Controlled processing predicted accuracy, while visual changes had mixed effects on performance and stress.

Area of Science:

  • Cognitive Psychology
  • Human Factors Engineering
  • Pharmacy Practice

Background:

  • Understanding cognitive factors in pharmacy dispensing errors is crucial.
  • The cognitive-systems performance model provides a framework for analyzing these errors.

Purpose of the Study:

  • To evaluate individual differences in attention and visual task alterations on simulated pharmacy verification.
  • To assess perceived workload and self-reported stress in pharmacy dispensing tasks.

Main Methods:

  • Employed the cognitive-systems performance model with 73 college-age volunteers.
  • Utilized psychological measures of automatic and controlled information processing.
  • Simulated 200 prescriptions with varying font sizes and visual aids, assessing detection accuracy, workload (NASA-TLX), and stress (DSSQ).

Related Experiment Videos

Main Results:

  • Both automatic and controlled processing influence pharmacy verification performance.
  • Controlled processing mildly predicted detection accuracy; automatic processing did not.
  • Font size alterations affected detection, while magnification/illumination devices yielded mixed results.
  • Perceived workload and stress patterns aligned with a three-tier behavioral framework.

Conclusions:

  • Individual differences in controlled information processing are key to reducing pharmacy dispensing errors.
  • Visual presentation of prescription information significantly impacts cognitive performance and stress.
  • The findings support existing models of cognitive performance and stress prediction in automated tasks.