Bacterial vaginosis testing gaps for transmasculine patients may exacerbate health disparities.

Introduction: Bacterial vaginosis (BV) is associated with non-optimal changes in the vaginal microbiome and increased susceptibility to STIs and HIV in cisgender women. Much less is known about the sexual health of transmasculine people and susceptibility to BV, STIs, and HIV. This study's objective was to assess BV testing and outcomes of transmasculine and cisgender women patient populations at a large, LGBTQ + federally qualified health center. Methods: Retrospective electronic health record data were extracted for eligible patients having at least one primary care visit between January 1, 2021, and December 31, 2021. Transmasculine patients were limited to those with a testosterone prescription in 2021. We conducted log binomial regression analysis to determine the probability of receiving a BV test based on gender identity, adjusting for sociodemographic characteristics. Results: During 2021, 4,903 cisgender women patients and 1,867 transmasculine patients had at least one primary care visit. Compared to cisgender women, transmasculine patients were disproportionately young, White, queer, privately insured, living outside Chicago, and had a lower rate of BV testing (1.9% v. 17.3%, p < 0.001). Controlling for sociodemographics, transmasculine patients were less likely to receive a BV test [Prevalence Ratio = 0.19 (95% CI 0.13-0.27)]. Discussion: The low rate of BV testing among transmasculine patients may contribute to disparities in reproductive health outcomes. Prospective community- and provider-engaged research is needed to better understand the multifactorial determinants for sexual healthcare and gender-affirming care for transmasculine patients. In particular, the impact of exogenous testosterone on the vaginal microbiome should also be determined.

First They Came for Us All: Responding to Anti-Transgender Structural Violence With Collective, Community-Engaged, and Intersectional Health Equity Research and Advocacy.

This article is a call for collective action across health equity researchers and advocates to build a more just world. We attempt to make sense of senseless structural and interpersonal brutality in the context of the current political climate across the United States, whereby the spectrum of gender nonconformity has been and continues to be stigmatized. From drag performance to transgender identities to gender-affirming health care, extremists have instrumentalized primary levers of democracy-the courts, legislatures, and social media-to attempt to outlaw and eradicate gender expansiveness and those who provide forms of support and care, including gender-affirming medical care, to transgender, nonbinary, and gender-expansive (TNBGE) individuals.

An Implementation of a Community-Engaged, Group-Level Mental Health Pilot for Black and Latina Transgender Women.

The primary aim is to assess the implementation of an eight-session, group therapy pilot for Black and Latina transgender women in Chicago in terms of implementation outcomes regarding intervention effectiveness, acceptability, appropriateness, and feasibility. The Exploration Preparation Implementation Sustainment (EPIS) framework guided implementation processes, including community engagement as an implementation strategy, and an implementation taxonomy was used to evaluate outcomes of acceptability, appropriateness, and feasibility, in addition to intervention effectiveness regarding anxiety and community connectedness. Two rounds of the pilot were completed in 2020, during the COVID-19 pandemic, at a community-based organization serving LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning) youth on Chicago's West Side. Participants (N = 14) completed a baseline and postintervention assessment and evaluations after each of eight intervention modules. Descriptive statistics show improvement across measures of anxiety and community connectedness, and high mean scores across domains of acceptability, appropriateness, and feasibility. Pilot findings indicate intervention effectiveness, acceptability, appropriateness, and feasibility to address mental health and social support of Black and Latina transgender women. Additional resources are needed for transgender community-engaged mental health programs and research to establish core and adaptable intervention elements, scaled-up evidence for clinical effectiveness, and, most importantly, to improve mental health outcomes and the sustainability of such interventions.

Temporary changes in STI & HIV testing & diagnoses across different phases of the COVID-19 pandemic, Chicago IL.

Introduction: While the U.S. has seen a sustained rise in STI cases over the past decade, the impact of the COVID-19 on STIs and HIV is unclear. Methods: To examine the short- and medium-term impacts of COVID-19 and HIV and STI testing and diagnosis, we compared pre-pandemic trends to three periods of the pandemic: early- pandemic, March-May 2020; mid-pandemic June 2020-May 2021; and late-pandemic, June 2021-May 2022. We compared average number of monthly tests and diagnoses, overall and by gender, as well as the monthly change (slope) in testing and diagnoses. Results: We find that after decreases in average monthly STI and HIV testing and diagnoses during the early- and mid-pandemic, cases were largely back to pre-pandemic levels by the late-pandemic, with some variation by gender. Conclusion: Changes in testing and diagnoses varied by phase of the pandemic. Some key populations may require additional outreach efforts to attain pre-pandemic testing levels.

The Trans Accountability Project: Community Engagement to Address Structural Marginalization and Health Inequities.

BACKGROUND: Black and Latina Transgender women face systemic marginalization and harm, increasing vulnerability to social stress and poor health outcomes. These communities have limited access to resources to mobilize and create paths toward health equity. OBJECTIVES: In this paper we report on the results of a community partnership to engage Black and Latina transgender communities on the South and West Sides of Chicago and establish service priorities for collective empowerment. METHODS: The Trans Accountability Project (TAP), a steering committee of racially diverse transgender and nonbinary representatives from four partner organizations, was established and led the design, recruitment, implementation, and analysis of a community needs assessment. World café and human-centered design methods, guided two community conversations/listening sessions around four activities: the perfect provider, my dream job, safety planning, and a stake-holder reflection. RESULTS: Sixty-three participants completed three activities and envisioned innovations for 1) accessible and holistic gender-affirming health care, 2) autonomous, flexible, and community-focused jobs in the arts, nonprofit/business, and care professions, and 3) safer social interactions and spaces. Ten stakeholders attended to listen and inform their organizational and clinical practices to empower Black and Latina transgender women. CONCLUSIONS: TAP prioritized accountability, connectedness, and centering the voices of Black and Latina transgender women as a starting point to intervene upon structural marginalization. Five insights emerged and have directed TAP's focus toward employment and collective care. Although further structural change remains a priority, TAP represents a mechanism for sharing power, improving communication and collaboration, and increasing transparency across relevant Chicago community-based organizations.

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design.

BACKGROUND: Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. OBJECTIVE: This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS: A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. RESULTS: The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. CONCLUSIONS: The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42971.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Community engagement to improve access to healthcare: a comparative case study to advance implementation science for transgender health equity.

BACKGROUND: Recent calls to action have been made for Implementation Science to attend to health inequities at the intersections of race, gender, and social injustice in the United States. Transgender people, particularly Black and Latina transgender women, experience a range of health inequities and social injustices. In this study, we compared two processes of transgender community engagement in Los Angeles and in Chicago as an implementation strategy to address inequitable access to care; we adapted and extended the Exploration Planning Implementation and Sustainment (EPIS) framework for transgender health equity. METHODS: A comparative case method and the EPIS framework were used to examine parallel implementation strategies of transgender community engagement to expand access to care. To foster conceptual development and adaptation of EPIS for trans health equity, the comparative case method required detailed description, exploration, and analyses of the community-engagement processes that led to different interventions to expand access. In both cities, the unit of analysis was a steering committee made up of local transgender and cisgender stakeholders. RESULTS: Both steering committees initiated their exploration processes with World Café-style, transgender community-engaged events in order to assess community needs and structural barriers to healthcare. The steering committees curated activities that amplified the voices of transgender community members among stakeholders, encouraging more effective and collaborative ways to advance transgender health equity. Based on analysis and findings from the Los Angeles town hall, the steering committee worked with a local medical school, extending the transgender medicine curriculum, and incorporating elements of transgender community-engagement. The Chicago steering committee determined from their findings that the most impactful intervention on structural racism and barriers to healthcare access would be to design and pilot an employment program for Black and Latina transgender women. CONCLUSION: In Los Angeles and Chicago, transgender community engagement guided implementation processes and led to critical insights regarding specific, local barriers to healthcare. The steering committee itself represented an important vehicle for individual-, organizational-, and community-level relationship and capacity building. This comparative case study highlights key adaptations of EPIS toward the formation of an implementation science framework for transgender health equity.

The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis.

The emergency department (ED) is a critical setting for the treatment of patients with opioid misuse. Detecting relevant clinical profiles allows for tailored treatment approaches. We sought to identify and characterize subphenotypes of ED patients with opioid-related encounters. A latent class analysis was conducted using 14,057,302 opioid-related encounters from 2016 through 2017 using the National Emergency Department Sample (NEDS), the largest all-payer ED database in the United States. The optimal model was determined by face validity and information criteria-based metrics. A three-step approach assessed class structure, assigned individuals to classes, and examined characteristics between classes. Class associations were determined for hospitalization, in-hospital death, and ED charges. The final five-class model consisted of the following subphenotypes: Chronic pain (class 1); Alcohol use (class 2); Depression and pain (class 3); Psychosis, liver disease, and polysubstance use (class 4); and Pregnancy (class 5). Using class 1 as the reference, the greatest odds for hospitalization occurred in classes 3 and 4 (Ors 5.24 and 5.33, p < 0.001) and for in-hospital death in class 4 (OR 3.44, p < 0.001). Median ED charges ranged from USD 2177 (class 1) to USD 2881 (class 4). These subphenotypes provide a basis for examining patient-tailored approaches for this patient population.

Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.

BACKGROUND: Substance misuse is a heterogeneous and complex set of behavioural conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital-wide solutions exist to comprehensively and reliably identify these conditions to prioritise care and guide treatment. The aim of this study was to apply natural language processing (NLP) to clinical notes collected in the electronic health record (EHR) to accurately screen for substance misuse. METHODS: The model was trained and developed on a reference dataset derived from a hospital-wide programme at Rush University Medical Center (RUMC), Chicago, IL, USA, that used structured diagnostic interviews to manually screen admitted patients over 27 months (between Oct 1, 2017, and Dec 31, 2019; n=54 915). The Alcohol Use Disorder Identification Test and Drug Abuse Screening Tool served as reference standards. The first 24 h of notes in the EHR were mapped to standardised medical vocabulary and fed into single-label, multilabel, and multilabel with auxillary-task neural network models. Temporal validation of the model was done using data from the subsequent 12 months on a subset of RUMC patients (n=16 917). External validation was done using data from Loyola University Medical Center, Chicago, IL, USA between Jan 1, 2007, and Sept 30, 2017 (n=1991 adult patients). The primary outcome was discrimination for alcohol misuse, opioid misuse, or non-opioid drug misuse. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC). Calibration slope and intercept were measured with the unreliability index. Bias assessments were performed across demographic subgroups. FINDINGS: The model was trained on a cohort that had 3·5% misuse (n=1 921) with any type of substance. 220 (11%) of 1921 patients with substance misuse had more than one type of misuse. The multilabel convolutional neural network classifier had a mean AUROC of 0·97 (95% CI 0·96-0·98) during temporal validation for all types of substance misuse. The model was well calibrated and showed good face validity with model features containing explicit mentions of aberrant drug-taking behaviour. A false-negative rate of 0·18-0·19 and a false-positive rate of 0·03 between non-Hispanic Black and non-Hispanic White groups occurred. In external validation, the AUROCs for alcohol and opioid misuse were 0·88 (95% CI 0·86-0·90) and 0·94 (0·92-0·95), respectively. INTERPRETATION: We developed a novel and accurate approach to leveraging the first 24 h of EHR notes for screening multiple types of substance misuse. FUNDING: National Institute On Drug Abuse, National Institutes of Health.

External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.

BACKGROUND AND AIMS: Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center. DESIGN: Retrospective cohort study. SETTING: The site for validation was a midwestern United States tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. PARTICIPANTS/CASES: Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605). MEASUREMENTS: The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under-reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT-derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis. FINDINGS: Using clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision-recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89-0.92) and 0.56 (95% CI = 0.53-0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62-0.69), 0.98 (95% CI = 0.98-0.98), 0.35 (95% CI = 0.33-0.38), and 1.0 (95% CI = 1.0-1.0), respectively. CONCLUSIONS: External validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.

Transgender data collection in the electronic health record: Current concepts and issues.

There are over 1 million transgender people living in the United States, and 33% report negative experiences with a healthcare provider, many of which are connected to data representation in electronic health records (EHRs). We present recommendations and common pitfalls involving sex- and gender-related data collection in EHRs. Our recommendations leverage the needs of patients, medical providers, and researchers to optimize both individual patient experiences and the efficacy and reproducibility of EHR population-based studies. We also briefly discuss adequate additions to the EHR considering name and pronoun usage. We add the disclaimer that these questions are more complex than commonly assumed. We conclude that collaborations between local transgender and gender-diverse persons and medical providers as well as open inclusion of transgender and gender-diverse individuals on terminology and standards boards is crucial to shifting the paradigm in transgender and gender-diverse health.

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse classifier. MATERIALS & METHODS: Two experiments were conducted with the classifier's original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier's predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics. RESULTS: We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included "heroin" and "substance abuse" across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). DISCUSSION: The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed. CONCLUSION: Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.

Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center's EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

External validation of an opioid misuse machine learning classifier in hospitalized adult patients.

BACKGROUND: Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. METHODS: An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. RESULTS: Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64). CONCLUSIONS: Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

The substance use intervention team: A hospital-based intervention and outpatient clinic to improve care for patients with substance use disorders.

PURPOSE: In response to the opioid crisis, public health advocates urge hospitals to perform substance use disorder (SUD) screening, brief intervention, discharge planning with referral to treatment, and naloxone education. Universal screening makes specialized treatment available to all patients and decreases stigma around SUDs, allowing patients and providers to address SUDs during their hospitalization. Additionally, hospital and emergency department-initiated medications to treat SUD improve patient engagement with treatment and decrease opioid use, and use of medications for opioid use disorder after nonfatal overdoses decreases mortality. SUMMARY: A substance use intervention team (SUIT) service was established to offer universal screening and consultation by an interdisciplinary team at our urban academic medical center. The SUIT program provides inpatient consultation services as well as medical and behavioral clinic visits to transition patients to long-term treatment and is comprised of physicians, nurse practitioners, a clinical pharmacist, social workers, and a nurse. Successes attributed to enhanced medication use as a function of having a designated pharmacist as an integral member of the team are highlighted. Our medical center initiated screening efforts in tandem with its interdisciplinary team and clinic. The team attempts to start appropriately selected patients with SUD on medications for SUD while hospitalized. From January through December 2018, 87.2% of patients admitted to the hospital received initial SUD screening. Of the patients who screened positive, 1,400 received a brief intervention by a unit social worker; the SUIT service was consulted on 880 patients, and multiple medications for SUD were started during inpatient care. CONCLUSION: A screening, brief intervention, and referral to treatment service was successfully implemented in our hospital, with the SUIT program in place to provide interdisciplinary addiction care and initiate medications for SUD in appropriate patients.

Stakeholder Experiences With Gender Identity Data Capture in Electronic Health Records: Implementation Effectiveness and a Visibility Paradox.

BACKGROUND: Advocates have endorsed transgender visibility via gender identity (GI) data capture with the advent of the Affordable Care Act and electronic health record (EHR) requirements. Visibility in data in order to enumerate a population contrasts with ways in which other LGBT and public health scholars have deployed these concepts. AIMS: The article aims to assess the effectiveness of GI data capture in EHRs and implications for trans health care quality improvements and research. METHOD: Semistructured interviews were conducted with 27 stakeholders from prominent gender-affirming care providers across the United States. A range of informants shared their experiences with GI data capture. Interviews were coded, themes were identified, and the extended case method was used to contextualize data in relation to key concepts. RESULTS: Data capture is effective for increasing patient counts and making quality improvements but limited in terms of enhancing gender-affirming care depending on provider size, type, and competencies. Many challenges were highlighted regarding use of GI data for research, sharing GI data across systems, as well the ways data capture erases the dynamism of GI. These issues create conditions for limited kinds of disclosure, capture of particular categories, and care and treatment barriers. DISCUSSION: Stakeholders exposed a visibility paradox emerging from GI data capture. While data fields are created to increase the visibility of trans persons in medical settings and in health research, they work to increase the visibility of some while reducing the visibility of other gender diverse persons, including trans, nonbinary, and cisgender. CONCLUSION: New approaches are needed to explore implications of GI data standardization and the logics of health care in the face of gender expansiveness.