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

Abstract

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.

Racing the Machine: Data Analytic Technologies and Institutional Inscription of Racialized Health Injustice

“…Meanwhile, the racialized social conditions of differential population health profiles (e.g., higher burden of chronic illness), stratified living and working conditions, and legacies of residential and institutional segregation remain structurally intact despite heightened public awareness of COVID-19 inequities. This research thus lends empirical support to the Black Public Health Collective’s (2020) declaration that “race-based data are not racial justice,” recognizing technological effects may be paradoxical (Cruz and Paine 2021; Hoeyer 2023; Thompson 2021; Ziebland, Hyde, and Powell 2021) and themselves highly unequal (Noble 2018; Obermeyer et al 2019; Vyas et al 2020). It further joins recent critical scholarship in reimagining the role of data within collective struggles for freedom and social justice (Benjamin 2019; Hatch 2022; Nelson 2016; Rodríguez-Muñiz 2016).…”

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review

“…Condensing a nuanced conversation about social risks between a patient and provider into a structured field could inappropriately oversimplify patients' experiences. 73,76 Standardized documentation of social risks, however, may in fact facilitate more involved discussions between patients and providers about how patients' experiences impact health and wellness. Moving forward, diverse stakeholder involvement (including patients, caregivers, and frontline healthcare team members), training and education of personnel, and necessary resources/ infrastructure are vital to overcome the barriers of integrating social risk data into the EHR.…”

Data for Equity: Creating an Antiracist, Intersectional Approach to Data in a Local Health Department

“…For example, some races/ethnicities are not represented in data collection at all, or are ultimately grouped heterogeneously for analysis and presentation. Similarly, people who experience oppression due to the societal enforcement of a binary understanding of gender identity and gender expression have historically been invisible in data 11…”

“…Similarly, people who experience oppression due to the societal enforcement of a binary understanding of gender identity and gender expression have historically been invisible in data. 11 To be a truly antiracist and intersectional public health agency and effectively eliminate health disparities requires recognition of the subjectivity of data and of the power of data to dictate and reinforce narratives, accompanied by intentional reform of data practices. Although the public health community has made strides in foregrounding racial equity in public health rhetoric, 12 fewer resources are available that address how to actually incorporate equity principles in the collection, analysis, and reporting of data that influence public health decisions.…”