AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities

Human Bias in ED: Implications for AI-Driven Patient Flow Optimization

Auteurs-es

  • Dylan Russon University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, France
  • Marta Avalos University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, and SISTM INRIA France https://orcid.org/0000-0002-5471-2615
  • Ariel Guerra-Adames University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, France
  • Cédric Gil-Jardiné University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, France; University Hospital of Bordeaux, Pole of Emergency Medicine, F-33000, Bordeaux, France
  • Emmanuel Lagarde University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, France

DOI :

https://doi.org/10.32473/flairs.37.1.135570

Mots-clés :

Human/AI bias, Systematic discrimination issues via AI, Responsible AI, Data diversity and representation, Literature survey, AI in healthcare

Résumé

There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.

Biographie de l'auteur-e

Marta Avalos, University of Bordeaux, Bordeaux Population Health Research Center, UMR U1219, INSERM, F-33000, Bordeaux, and SISTM INRIA France

Associate Professor of Biostatistics, University of Bordeaux / Bordeaux population health INSERM 1219 / INRIA SISTM

Bordeaux, France

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Publié-e

2024-05-13

Comment citer

Russon, D., Avalos, M., Guerra-Adames, A., Gil-Jardiné, C., & Lagarde, E. (2024). AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities: Human Bias in ED: Implications for AI-Driven Patient Flow Optimization. The International FLAIRS Conference Proceedings, 37(1). https://doi.org/10.32473/flairs.37.1.135570

Numéro

Vol. 37 (2024): Vol. 37 (2024): Proceedings of FLAIRS-37

Rubrique

Posters

Licence

© Dylan Russon, Marta Avalos, Ariel Guerra-Adames, Cédric Gil-Jardiné, Emmanuel Lagarde 2024

Licence Creative Commons

Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale 4.0 International.