Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record System

Abstract

Objective: To create a natural language pro­cessing (NLP) algorithm to identify transgen­der patients in electronic health records.Design: We developed an NLP algorithm to identify patients (keyword + billing codes). Patients were manually reviewed, and their health care services categorized by billing code.Setting: Vanderbilt University Medical CenterParticipants: 234 adult and pediatric trans­gender patientsMain Outcome Measures: Number of transgender patients correctly identified and categorization of health services utilized.Results: We identified 234 transgender pa­tients of whom 50% had a diagnosed men­tal health condition, 14% were living with HIV, and 7% had diabetes. Largely driven by hormone use, nearly half of patients attended the Endocrinology/Diabetes/Me­tabolism clinic. Many patients also attended the Psychiatry, HIV, and/or Obstetrics/Gyne­cology clinics. The false positive rate of our algorithm was 3%.Conclusions: Our novel algorithm correctly identified transgender patients and provided important insights into health care utiliza­tion among this marginalized population. Ethn Dis. 2019;29(Suppl 2): 441-450. doi:10.18865/ed.29.S2.441

Development of a computable phenotype to identify a transgender sample for health research purposes: a feasibility study in a large linked provincial healthcare administrative cohort in British Columbia, Canada

“…There is limited literature on EHR-based studies with the ability to validate an administrative transgender measure using a ‘gold standard’ comparison measure. 16 The two previous studies that have developed and validated algorithms to identify transgender individuals have both been conducted in non-representative samples in the USA, one using Medicare data 38 and one in a university medical centre. 16 Similar to the current study, the Medicare study found high specificity when comparing an EHR-based and a two-step survey-based transgender measure.…”

“… 8 In response, researchers have called for advancing transgender health research methods—namely ascertainment of high-quality samples via systematic approaches—including for general population-based and health systems-based studies. 15 One opportunity for the advancement of transgender health research methods is the emerging use of CPs 16 or case ascertainment algorithms, to identify transgender samples in healthcare utilisation data. A CP is an algorithm for identifying a clinical feature, condition or set of characteristics that can be determined directly from EHR and other ancillary healthcare data systems (eg, disease registries, insurance claims data) data.…”

“…Researchers with the US Centers for Medicare & Medicaid Services identified 4098 transgender beneficiaries using national Medicare claims data, 20 and researchers at Vanderbilt University identified 234 transgender patients in their university clinic EHR data. 16 While these cohorts represent important opportunities for advancement of transgender health research, these methods have yet to be applied widely outside the US context. This is particularly important as different jurisdictions may vary in medical billing and coding practices, healthcare system patient populations and representativeness of the general population.…”

Achieving Health Equity through Science, Policy, and Partnerships

“…These studies exemplify challenges associated with intersectionality, or the extra-disadvantage experienced by individuals with multiple "minority" social positions. 19 Importantly, this work yielded feasible and actionable policy areas with the potential to address the health care needs of populations at high risk for morbidity, discrimination, and marginalization, including transgender patients, 8 adults with developmental disabili-…”

“…The topics covered underscore the breadth and depth of health inequities across populations, including adults with developmental disabilities, [5][6] persons living with chronic illnesses, 6 racial/minority youth with mental health concerns, 7 and transgender medical patients. 8 The articles encourage readers to consider the multilevel changes required to achieve the desired goal of optimal health for all, such as cultural competence training for health care providers, 9 health policy training, [10][11] the use of consumer health informatics applications for disease self care, 12 stakeholder education 13 and organizational engagement. 14 In addition to research dissemination, this supplement is designed to serve as: 1) a resource to researchers seeking to advance the science of health equity; 2) an educational resource for those working in community-based and/ or grassroots settings; and 3) a transdisciplinary document demonstrating the importance of keeping the policy implications of health research in the foreground, from project conceptualization through the interpretation and dissemination of findings.…”