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Lessons Learned in Emergency Pelvic and First-Trimester US: Focus on Cognitive Biases.

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Diagnosing acute pelvic pain in women, both pregnant and nonpregnant, is challenging. Recognizing common and uncommon ultrasound findings and understanding cognitive biases can improve diagnostic accuracy in emergency settings.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Emergency Medicine

Background:

  • Acute pelvic pain in nonpregnant and pregnant women presents a diagnostic challenge due to nonspecific symptoms.
  • Pelvic ultrasound (US) is the primary imaging modality for suspected obstetric or gynecologic causes, but interpretation can be difficult.
  • Cognitive biases can lead to systematic errors in imaging interpretation, contributing to diagnostic inaccuracies.

Purpose of the Study:

  • To illustrate pelvic ultrasound findings in emergency settings for patients with acute pelvic pain.
  • To highlight diagnostic errors and cognitive biases encountered in interpreting pelvic US.
  • To improve prompt and accurate diagnosis by increasing awareness of common/uncommon findings and potential biases.

Main Methods:

  • Review of peer learning cases involving nonpregnant and first-trimester pregnant patients with acute pelvic pain.
  • Inclusion of nonobstetric and nongynecologic causes of pelvic pain diagnosed initially with pelvic US.
  • Analysis of diagnostic errors and cognitive biases associated with image interpretation.

Main Results:

  • Pelvic US is crucial for diagnosing acute pelvic pain, but interpretation requires expertise.
  • Cognitive biases can significantly impact diagnostic accuracy in pelvic US interpretation.
  • Familiarity with diverse pelvic US findings and awareness of biases are essential for radiologists.

Conclusions:

  • Radiologists' awareness of cognitive biases can refine differential diagnoses and reduce errors in pelvic US interpretation.
  • Enhanced understanding of pelvic US findings aids in accurate diagnosis of acute pelvic pain.
  • This educational approach using peer learning cases aims to improve diagnostic performance in emergency settings.