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Background <p>In the Intensive Care Unit (ICU), clinicians frequently make complex, high-stakes judgments, where inaccuracies can profoundly affect patient outcomes. This perspective examines human judgment error in ICU settings, specifically bias (systematic error) and noise (random error). While past research has emphasized bias, we explore the role of noise in clinical decision making and its mitigation.</p> Main body <p>System noise refers to unwanted variability in judgments that should ideally be identical. This variability stems from level noise (variability in clinicians’ average judgments), stable pattern noise (variability in clinicians’ responses to specific patient characteristics), and occasion noise (random, within-clinician variability). Two strategies to reduce noise are the use of algorithms and the averaging of independent judgments.</p> Conclusion <p>Recognizing and addressing noise in clinical decision making is essential to enhancing judgment accuracy in critical care. By implementing effective noise reduction strategies, clinicians can reduce errors and improve patient outcomes, ultimately advancing the quality of care delivered in ICU settings.</p>

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Human judgment error in the intensive care unit: a perspective on bias and noise

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月24日

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  • I. P. Peringa,
  • E. G. M. Cox,
  • R. Wiersema,
  • I. C. C. van der Horst,
  • R. R. Meijer,
  • J. Koeze

摘要

Background

In the Intensive Care Unit (ICU), clinicians frequently make complex, high-stakes judgments, where inaccuracies can profoundly affect patient outcomes. This perspective examines human judgment error in ICU settings, specifically bias (systematic error) and noise (random error). While past research has emphasized bias, we explore the role of noise in clinical decision making and its mitigation.

Main body

System noise refers to unwanted variability in judgments that should ideally be identical. This variability stems from level noise (variability in clinicians’ average judgments), stable pattern noise (variability in clinicians’ responses to specific patient characteristics), and occasion noise (random, within-clinician variability). Two strategies to reduce noise are the use of algorithms and the averaging of independent judgments.

Conclusion

Recognizing and addressing noise in clinical decision making is essential to enhancing judgment accuracy in critical care. By implementing effective noise reduction strategies, clinicians can reduce errors and improve patient outcomes, ultimately advancing the quality of care delivered in ICU settings.