EDITORS' INTRODUCTION: Transgender Health Equity and the Law.

The sheer gamut of issues impacting transgender health equity may seem overwhelming. This article seeks to introduce readers to the breadth of topics addressed in this symposium edition, exemplifying that transgender health equity is a global issue that demands an interdisciplinary approach.

The Challenges of Conducting Intrastate Policy Surveillance: A Methods Note on County and City Laws in Indiana.

Policy surveillance is critical in examining the ways law functions as a structural and social determinant of health. To date, little policy surveillance research has focused on examining intrastate variations in the structure and health impact of laws. Intrastate policy surveillance poses unique methodological challenges because of the complex legal architecture within states and inefficient curation of local laws.We discuss our experience with these intrastate policy surveillance challenges in Indiana, a state with 92 counties and several populous cities, a complicated history of home rule, systemically underfunded local governments, and variations in demography, geography, and technology adoption. In our case study, we expended significant time and resources to obtain county and city ordinances through online code libraries, jurisdiction Web sites, and (most notably) visits to offices to scan documents ourselves.A concerted effort is needed to ensure that local laws of all kinds are stored online in organized, searchable, and open access systems. Such an effort is vital to achieve the aspirational goals of policy surveillance at the intrastate level.

Using natural language processing to classify social work interventions.

OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms. STUDY DESIGN: Secondary data analysis of a longitudinal cohort. METHODS: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme. RESULTS: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92). CONCLUSIONS: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs.