Default bias in medical patient simulators: Differences in availability and procedures

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Default biases in the design of new medical training technologies may lead to disparities, especially for women and people of color in patient treatment and outcomes in prehospital emergency services and combat medicine. The goal of this work was to develop a taxonomy of medical patient simulators to determine their demographic makeup and whether there was evidence of default bias in product catalogs. Methods: The authors identified 114 full-body patient simulators from seven major medical device companies. Simulator demographic information (e.g., patient sex/gender, race/skin tone) and simulator prehospital care procedure features (e.g., tension pneumothorax, massive hemorrhage) were cataloged using company website information sheets and product catalogs using a binary coding system. Procedures were further classified into two categories: sensitive procedures and non-sensitive procedures. Results: Findings revealed that although several companies offer demographically diverse patient simulators, marketing of many existing patient simulators use the white, male body as the featured default patient. Female patient simulators and simulated patients of color were significantly less likely to have procedure capabilities that allow for the practice of treating sensitive areas of the body compared to default male patient simulators. Discussion: Default bias in the design of medical patient simulators illustrate the importance of inclusive design to improve medical decision-making and reduce disparities in civilian and military patient outcomes and care. Practice implications include increasing the diversity of complex medical patient simulators, removing default selections, and featuring female and patients of color in product catalogs.

Original languageEnglish (US)
Article number100040
JournalHuman Factors in Healthcare
Volume3
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023

Keywords

  • Decision making
  • Default bias
  • Gender
  • Patient simulators
  • Race

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