Emergency triage is a crucial process within healthcare systems, designed to rapidly assess and categorise patients based on the severity of their condition. Triage nurses evaluate key factors such as symptoms, vital signs and medical history to assign an acuity score that determines the urgency of medical intervention. This process aims to prioritise patients who require immediate attention while ensuring the efficient allocation of healthcare resources. However, research has shown that biases can influence triage decisions, potentially leading to discrepancies in patient outcomes.
Judgment biases, defined as systematic deviations from objective assessment, can manifest in emergency triage through cognitive shortcuts and implicit assumptions. These biases may be linked to demographic factors such as sex, gender, age and ethnicity, which, though not necessarily clinically relevant, can shape healthcare decisions. A recent study has examined sex and gender biases in emergency triage using advanced language models trained on real-world data from Bordeaux University Hospital. By systematically modifying patient sex references in triage notes and analysing the resulting changes in severity ratings, researchers have uncovered significant disparities in triage assessments.
Biases in Emergency Triage Decisions
Triage nurses operate in high-pressure environments where rapid decision-making is essential. Given the volume of cases and the need for immediate judgement, cognitive shortcuts often influence assessments. While these shortcuts can be useful in streamlining workflows, they also introduce the risk of bias. The research findings indicate that female patients are assigned lower severity ratings compared to male patients presenting with the same clinical conditions. This discrepancy suggests that implicit assumptions about gender may be influencing the perception of medical urgency.
The study further identifies specific conditions under which these biases become more pronounced. Female nurses exhibit a stronger tendency towards assigning lower severity scores to female patients, potentially reflecting internalised biases or variations in clinical assessment frameworks. Additionally, when patients report higher pain levels, biases appear to be magnified, suggesting that subjective symptoms may be interpreted differently based on gendered expectations. However, one mitigating factor observed in the study is the level of experience among triage nurses. More experienced nurses display a reduced tendency towards biased decision-making, indicating that clinical expertise and training can help counteract implicit biases.
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These findings highlight the complexity of judgment biases in emergency triage. While some biases may be unconscious, they can still impact patient outcomes by influencing the speed and quality of care. Under-triage—where a patient is assigned a lower severity score than appropriate—can lead to treatment delays and increased health risks. Conversely, over-triage—where a patient receives a higher severity score than warranted—can strain hospital resources, leading to inefficiencies in patient management. Addressing these biases is essential for ensuring equitable and effective triage practices.
AI as a Tool for Bias Detection
Artificial intelligence has emerged as a powerful tool for analysing decision-making processes in healthcare. Large language models (LLMs), which are trained on vast amounts of clinical data, can replicate human judgement patterns and identify inconsistencies. In this study, LLMs were fine-tuned using historical triage data from Bordeaux University Hospital to evaluate whether biases could be detected and quantified.
The research employed a novel approach where references to patient sex in triage notes were systematically altered. By comparing severity scores assigned to original and modified records, researchers were able to isolate the impact of sex and gender biases on triage outcomes. The results showed statistically significant differences in severity ratings before and after transformation, reinforcing the hypothesis that biases influence clinical decision-making. These findings demonstrate that AI-driven analysis can serve as an effective method for identifying judgment biases, providing an evidence-based approach to understanding disparities in emergency triage.
Unlike traditional methods for assessing discrimination, which often rely on retrospective analysis or survey-based approaches, AI models offer a scalable and objective means of evaluating bias. By processing vast datasets with high accuracy, LLMs can reveal subtle patterns that may go unnoticed in conventional audits. Additionally, these models can be refined to assess other potential sources of bias, such as ethnicity, socioeconomic status or insurance coverage, further broadening their applicability in healthcare research.
Implications for Clinical Practice
Recognising and addressing biases in emergency triage is essential for improving patient care and ensuring fair treatment across diverse patient populations. The study’s findings suggest that targeted interventions can help mitigate the impact of biases in clinical decision-making. One key strategy is enhancing training programmes for triage nurses, with a focus on implicit bias awareness and evidence-based assessment methods. By incorporating structured training sessions that expose healthcare professionals to real-world bias scenarios, institutions can equip staff with the skills needed to make more objective triage decisions.
Additionally, standardised triage protocols can help reduce variability in decision-making. Implementing decision-support tools that provide objective guidelines for assigning severity scores can serve as a safeguard against cognitive biases. AI-powered triage assistance systems could further reinforce standardised approaches by providing real-time recommendations based on patient data, ensuring that assessments align with established medical criteria. However, it is crucial to design these systems with fairness in mind, ensuring that AI does not perpetuate existing disparities but rather helps correct them.
Continuous assessment and feedback mechanisms also play a crucial role in addressing biases. Regular audits of triage decisions, combined with data-driven insights from AI analysis, can help healthcare providers monitor trends and implement corrective measures when necessary. By fostering an environment of accountability and continuous improvement, hospitals can create a triage process that prioritises clinical need over subjective judgment.
The study provides compelling evidence that biases in emergency triage impact patient outcomes, particularly in the context of gender disparities. By leveraging AI to uncover and quantify these biases, researchers offer a new approach to understanding and addressing inequities in healthcare decision-making. The findings underscore the importance of continuous training, standardised protocols and AI-assisted decision support in ensuring fair and effective triage assessments.
Addressing these challenges requires a multifaceted approach that combines technological innovation, professional development and policy adjustments. As AI becomes increasingly integrated into clinical practice, its role in detecting and mitigating biases will be instrumental in improving patient care. By proactively addressing these issues, healthcare institutions can take meaningful steps towards a more equitable and efficient triage system, ultimately enhancing outcomes for all patients regardless of demographic factors.
Source: Proceedings of Machine Learning Research
Image Credit: iStock
