Gender bias in AI-based model according to the sex composition of the training sets: the case of a model triaging patients requiring referral to hospital emergencies among those seeking emergency or unscheduled care by phone
Abstract
Abstract Background Gender differences in emergency care are well documented and generally disadvantage women, including at the triage stage. AI-based triage tools may reproduce or amplify these differences. We previously developed a model identifying, among individuals seeking emergency or unscheduled care through a medical call centre, those requiring referral to an emergency department (ED), and observed better discrimination in men than in women. This study aimed to analyse the effect of the sex composition of the training data on this gender difference in discriminatory performance. Methods We used telephone calls (from 2018 to 2023) and medical records from SOS Médecins Grand Paris (France), which provides same-day home visits for patients. At the end of each visit, the doctor records the patient's outcome (left at home versus referred to EDs; 3% referrals) in the patient's medical file. Calls were transcribed using an automatic speech recognition model and then processed using a text‑based model to classify whether the patient required ED attendance. From sex-balanced training, validation and test subsets (n=601 560), three subsets of identical size (n=300 780) were derived - female, male, and sex-balanced-reduced-by-half - and used to train three models. Discrimination was compared by sex using receiver operating characteristic area under the curve (ROC-AUC). Results Of 1 876 172 calls followed by a home visit, 1 098 748 (60%) were matched with their medical record.…
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