When algorithms infer gender: revisiting computational phenotyping with electronic health records data

Abstract Computational phenotyping has emerged as a practical solution to the incomplete collection of data on gender in electronic health records (EHRs). This approach relies on algorithms to infer a patient’s gender using the available data in their health record, such as diagnosis codes, medication histories, and information in clinical notes. Although intended to improve the visibility of trans and gender-expansive populations in EHR-based biomedical research, computational phenotyping raises significant methodological and ethical concerns related to the potential misuse of algorithm outputs. In this paper, we provide a narrative review of computational phenotyping of gender and examine its challenges through a critical lens. We also highlight existing recommendations for biomedical researchers and propose priorities for future work in this domain.