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

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

Recent scientific and policy initiatives frame clinical settings as sites for intervening upon inequality. Electronic health records and data analytic technologies offer opportunity to record standard data on education, employment, social support, and race-ethnicity, and numerous audiences expect biomedicine to redress social determinants based on newly available data. However, little is known on how health practitioners and institutional actors view data standardization in relation to inequity. This article examines a public safety-net health system's expansion of race, ethnicity, and language data collection, drawing on 10 months of ethnographic fieldwork and 32 qualitative interviews with providers, clinic staff, data scientists, and administrators. Findings suggest that electronic data capture institutes a decontextualized racialization within biomedicine as health practitioners and data workers rely on biological, cultural, and social justifications for collecting racial data. This demonstrates a critical paradox of stratified biomedicalization: The same data-centered interventions expected to redress injustice may ultimately reinscribe it.

A call to action to improve the completeness of older adult sexual and gender minority data in electronic health records

Sexual and gender minority (SGM) older adults experience greater health disparities compared to non-SGM older adults. The SGM older adult population is growing rapidly. To address this disparity and gain a better understanding of their unique challenges in healthcare relies on accurate data collection. We conducted a secondary data analysis of 2018-2022 electronic health record data for older adults aged ≥50 years, in 1 large academic health system to determine the source, magnitude, and correlates of missing sexual orientation and gender identity (SOGI) data among hospitalized older adults. Among 153 827 older adults discharged from the hospital, SOGI data missingness was 67.6% for sexual orientation and 63.0% for gender identity. SOGI data are underreported, leading to bias findings when studying health disparities. Without complete SOGI data, healthcare systems will not fully understand the unique needs of SGM individuals and develop tailored interventions and programs to reduce health disparities among these populations.

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

OBJECTIVE

To develop recommendations to embed equity into data work at a local health department and a framework for antiracist data praxis.

DESIGN

A working group comprised staff from across the agency whose positions involved data collection, analysis, interpretation, or communication met during April-July 2018 to identify and discuss successes and challenges experienced by staff and to generate recommendations for achieving equitable data practices.

SETTING

Local health department in New York City.

RESULTS

The recommendations encompassed 6 themes: strengthening analytic skills, communication and interpretation, data collection and aggregation, community engagement, infrastructure and capacity building, and leadership and innovation. Specific projects are underway or have been completed.

CONCLUSIONS

Improving equity in data requires changes to data processes and commitment to racial and intersectional justice and process change at all levels of the organization and across job functions. We developed a collaborative model for how a local health department can reform data work to embed an equity lens. This framework serves as a model for jurisdictions to build upon in their own efforts to promote equitable health outcomes and become antiracist organizations.

Neuropsychological assessment with lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) individuals: Practice, education, and training survey

Objective: We sought to describe the LGBTQ + related education, training, and clinical practice of independently licensed neuropsychologists in the United States and to identify factors that predict affirmative neuropsychological practices. We hypothesized that LGBTQ + identity, female gender, more recent training, and extent of LGBTQ + education/training would predict use of LGBTQ + practice guidelines. Method: A workgroup of clinical psychologists with experience in LGBTQ + psychology and neuropsychology developed a survey to identify personal and professional factors that predict affirmative neuropsychological testing practices. The survey was distributed through professional organizations and listservs between August and September 2021 with 118 responses meeting inclusionary criteria. Results: The majority of participants identified as heterosexual (70.3%) and cisgender (97.5%), and most (48-63%) received LGBTQ + training post-licensure. Between 19% and 32% of participants reported never completing LGBTQ + specific education. Consistent with our hypotheses, factors predicting affirmative clinical practice behaviors were LGBTQ + education/training, and personal background (sexual minority status, female/feminine gender, and years since degree). Other significant factors included prior experience with LGBTQ + patients and primary patient population (child vs. adult). Qualitative responses indicated varying values, attitudes, and knowledge regarding collection of LGBTQ + information and modification of clinical practice. Conclusions: Neuropsychologists underutilize affirming practices as evidenced by low rates of querying pronouns, knowing whether LGBTQ + health information is available at their institutions, and adjusting evaluation and feedback approaches. We provide specific training and education recommendations to increase knowledge and skills and to address beliefs about LGBTQ + health that can serve to promote affirmative neuropsychological practice.

Discuss and remember: Clinician strategies for integrating social determinants of health in patient records and care

There is growing interest in standardizing data about social determinants of health (SDOH) in electronic health records (EHRs), yet little is known about how clinicians document SDOH in daily practice. This study investigates clinicians' strategies for working with SDOH data and the challenges confronting SDOH standardization. Drawing on ethnographic observation, interviews with patients and clinicians, and systematic review of patient EHRs-all at an urban teaching hospital in the US Midwest-we analyze three strategies clinicians deploy to integrate SDOH data into patient care. First, clinicians document SDOH using "signal phrases," keywords and short sentences that help them recall patients' social stories. Second, clinicians use other technology or face-to-face conversations to share about patients' SDOH with colleagues. Third, clinicians fold discussion of SDOH with patients into their personal relationships. While these local strategies facilitate personalized care and help clinicians minimize their computer workload, we also consider their limitations for efforts to coordinate care across institutions and attempts to identify SDOH in EHRs. These findings reveal ongoing tensions in projects of standardization in medicine, as well as the specific difficulty of standardizing data about SDOH. They have important clinical implications as they help explain how clinicians may attend to patients' SDOH in ways that are not legible in patient records. This paper is also relevant for policy at a time when mandates to include SDOH data in health records are expanding and strategies to standardize SDOH documentation are being developed.

Positive patient-provider relationships among transgender and nonbinary individuals in New York City

Background: Transgender and nonbinary (TGNB) individuals have diverse health needs and may face disproportionate barriers to healthcare, including developing positive patient-provider relationships. While there is mounting evidence of gender-based stigma and discrimination in healthcare, little is known about how TGNB individuals develop positive patient-provider relationships. Aims: To examine TGNB individuals' interactions with healthcare providers and identify main characteristics of positive patient-providers relationships. Methods: We conducted semi-structured interviews with a purposive sample of 13 TGNB individuals in New York, NY. Interviews were transcribed verbatim and analyzed inductively for themes related to characteristics of positive and trusting relationships with healthcare providers. Results: Participants' mean age was 30 years (IQR = 13 years) and most participants were nonwhite (n = 12, 92%). Receiving peer referrals to specific clinics or providers helped many participants find providers perceived to be competent and created initial grounds for positive patient-provider relationships. Providers with whom participants had positive relationships commonly managed primary care and gender-affirming care and relied on a network of interdisciplinary providers for other specialized care. Providers who were positively evaluated were perceived to possess in-depth clinical knowledge on the issues they were responsible for managing, including gender-affirming interventions, particularly for TGNB patients who perceived themselves to be knowledgeable about TGNB-specific care. Provider and staff cultural competence and a TGNB-affirming clinic environment were also important, particularly early in the patient-provider relationship, and if combined with TGNB clinical competence. Discussion: Provider-focused training and education programs should combine components of TGNB clinical and cultural competence to facilitate development of positive relationships between TGNB patients and providers, thereby improving the health and wellbeing of TGNB people.

Redesigning culturally tailored intervention in the precision health era: Self-management science context

BACKGROUND

Nurse scientists have significantly contributed to health equity and ensuring cultural tailoring of interventions to meet unique needs of individuals. Methodologies for cultural tailoring of self-mangament interventions among marginalized populations have limitedly accommodated intersectionality and group heterogeneity when addressing health needs.

PURPOSE

Identify methodological limitations in cultural tailoring of interventions among priority populations and issue recommendations on cultural elements that researchers can target to ensure valid cultural tailoring approaches.

METHODS

Synthesis of literature on health equity, self-management, and implementation and dissemination research.

FINDINGS

Among priority populations, intersectionality and group heterogeneity has made group-based cultural tailoring approaches less effective in eliciting desirable health outcomes. Precision health methodology could be useful for cultural tailoring of interventions due to the methodology's focus on individual-level tailoring approaches.

DISCUSSION

We offer ways to advance health equity research using precision health approaches in cultural tailoring through targeting unique elements of culture and relevant psychosocial phenotypes.

Ensuring equitable, inclusive and meaningful gender identity- and sexual orientation-related data collection in the healthcare sector: insights from a critical, pragmatic systematic review of the literature

In several countries, no gender identity- and sexual orientation-related data is routinely collected, if not for specific health or administrative/social purposes. Implementing and ensuring equitable and inclusive socio-demographic data collection is of paramount importance, given that the LGBTI community suffers from a disproportionate burden in terms of both communicable and non-communicable diseases. To the best of the authors' knowledge, there exists no systematic review addressing the methods that can be implemented in capturing gender identity- and sexual orientation-related data in the healthcare sector. A systematic literature review was conducted for filling in this gap of knowledge. Twenty-three articles were retained and analysed: two focussed on self-reported data, two on structured/semi-structured data, seven on text-mining, natural language processing, and other emerging artificial intelligence-based techniques, two on challenges in capturing sexual and gender-diverse populations, eight on the willingness to disclose gender identity and sexual orientation, and, finally, two on integrating structured and unstructured data. Our systematic literature review found that, despite the importance of collecting gender identity- and sexual orientation-related data and its increasing societal acceptance from the LGBTI community, several issues have to be addressed yet. Transgender, non-binary identities, and also intersex individuals remain often invisible and marginalized. In the last decades, there has been an increasing adoption of structured data. However, exploiting unstructured data seems to overperform in identifying LGBTI members, especially integrating structured and unstructured data. Self-declared/self-perceived/self-disclosed definitions, while being respectful of one's perception, may not completely be aligned with sexual behaviours and activities. Incorporating different levels of information (biological, socio-demographic, behavioural, and clinical) would enable overcoming this pitfall. A shift from a rigid/static nomenclature towards a more nuanced, dynamic, 'fuzzy' concept of a 'computable phenotype' has been proposed in the literature to capture the complexity of sexual identities and trajectories. On the other hand, excessive fragmentation has to be avoided considering that: (i) a full list of options including all gender identities and sexual orientations will never be available; (ii) these options should be easily understood by the general population, and (iii) these options should be consistent in such a way that can be compared among various studies and surveys. Only in this way, data collection can be clinically meaningful: that is to say, to impact clinical outcomes at the individual and population level, and to promote further research in the field.

The social life of biomedical data: Capturing, obscuring, and envisioning care in the digital safety-net

Biomedical investment in digital technologies has flooded society with staggering volumes of data, spurring high-tech innovations such as performance metrics, clinical algorithms, and public data dashboards. In examining the social life of data artifacts, scholars draw from actor-network theory to emphasize data's ability to represent social reality while circulating within it, while others suggest formal data models fail to account for invisible relations on the ground. Yet little work has examined the role of human reflexivity in crafting complex human-data configurations in practice, such as how situated human actors relate to data representations within the social reality they intimately know themselves. Drawing on ethnographic fieldwork of Electronic Health Records (EHRs) and data analytics integration from inside the digital safety-net, this article shows how health care workers recognize data simultaneously capture, obscure, and envision their everyday work of caring for the marginalized. By demonstrating how the same data point may in one context demonstrate good care while in another obscure it, these findings suggest need to broaden attention to the social life of data beyond delimited focus on standards and their travels. Digital technologies do not simply capture the social, but multiply it. Biomedical data then do not have one social life, but many.