Capturing patients, missing inequities: Data standardization on sexual orientation and gender identity across unequal clinical contexts

In effort to address fundamental causes and reduce health disparities, public programs increasingly mandate sites of care to capture patient data on social and behavioral domains within Electronic Health Records (EHRs). Data reporting drawing from EHRs plays an essential role in public management of social problems, and data on social factors are commonly cited as foundational for eliminating health inequities. Yet one major shortcoming of these data-centered initiatives is their limited attention to social context, including the institutional conditions of biomedical stratification and variation of care provision across clinical settings. In this article, we leverage comparative fieldwork to examine provider and system responses to mandated data collection on patient sexual orientation and gender identity (SOGI), highlighting unequal clinical contexts as they appear across a large county safety-net institution and an LGBTQ-oriented health organization. Although point of care data collection is commonly justified for governance in the aggregate (e.g., disparity monitoring), we find standardized data on social domains presents a double-edged sword in clinical settings: formal categories promote visibility where certain issues remain hidden, yet constrain clinical utility in sites with greater knowledge and experience with related topics. We further illustrate how data standardization captures patient identities yet fundamentally misses these unequal contexts, resulting in limited attenuation of inequity despite broad expectations of clinical change. By revealing the often-invisible contexts of care that elude standard measurement, our findings underline the strengths of qualitative social science in accounting for the complex dynamics of enduring social problems. We call for deeper engagement with the unequal contexts of biomedical stratification, especially in light of increasing pressure to quantify the social amidst the rising tide of data-driven care.