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IntroductionWhile 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.MethodsTo 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.ResultsWe 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.ConclusionChanges in testing and diagnoses varied by phase of the pandemic. Some key populations may require additional outreach efforts to attain pre-pandemic testing levels.

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.

Background Despite recent reductions, youth substance use continues to be a concern in the United States. Structured primary care substance use screening among adolescents is recommended, but not widely implemented. The purpose of this study was to describe the distribution and characteristics of adolescent substance use screening in outpatient clinics in a large academic medical center and assess related factors (i.e., patient age, race/ethnicity, gender, and insurance type) to inform and improve the quality of substance use screening in practice. Methods We abstracted a random sample of 127 records of patients aged 12–17 and coded clinical notes (e.g., converted open-ended notes to discrete values) to describe screening cases and related characteristics (e.g., which substances screened, how screened). We then analyzed descriptive patterns within the data to calculate screening rates, characteristics of screening, and used multiple logistic regression to identify related factors. Results Among 127 records, rates of screening by providers were 72% (each) for common substances (alcohol, marijuana, tobacco). The primary method of screening was use of clinical mnemonic cues rather than standardized screening tools. A total of 6% of patients reported substance use during screening. Older age and racial/ethnic minority status were associated with provider screening in multiple logistic regression models. Conclusions Despite recommendations, low rates of structured screening in primary care persist. Failure to use a standardized screening tool may contribute to low screening rates and biased screening. These findings may be used to inform implementation of standardized and structured screening in the clinical environment. Clinical trial registration not applicable.

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.

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.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.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.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.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.

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.

BACKGROUND Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in usual care of patients hospitalized with conditions related to unhealthy substance use. Rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. OBJECTIVE To provide a study protocol to evaluate health outcomes and cost-benefit of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS A pre-post design to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards in a single medical center. Effectiveness in terms of patient outcomes will be determined by non-inferior rates of interventions (brief intervention/motivational interviewing, medication assisted treatment, naloxone dispensing, referral to outpatient care) in the post-period by a substance use intervention team compared to pre-period. A separate analysis will be performed to assess the cost-benefit to the health system of using automated screening. RESULTS A natural language processing 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 non-inferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. The study is approved by the Institutional Review Board and registered at clinicaltrials.gov with a plan for implementation beginning in September 2022. CONCLUSIONS The use of augmented intelligence for clinical decision support is growing with an increasing number of artificial intelligence tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a non-inferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. CLINICALTRIAL ClinicalTrials.gov NCT03833804

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.

BackgroundRecent 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.MethodsA 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.ResultsBoth 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.ConclusionIn 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.

BackgroundRecent 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. The study addressed inequitable access to care and adapted and extended the Exploration Planning Implementation and Sustainment (EPIS) framework for transgender health equity. MethodsA comparative case method and the EPIS framework were used to examine parallel implementations of transgender community engagement to expand access to care. In order to foster the 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 to expand access to healthcare. In both cities, the unit of analysis was a steering committee made up of local transgender and cisgender stakeholders. ResultsBoth 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 a transgender employment program. ConclusionIn Los Angeles and Chicago, transgender community-engagement guided exploration, planning, and implementation processes and led to critical insights regarding specific, local structural barriers to healthcare. The steering committee itself represented an important vehicle for individual- 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.

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Nurse practitioners in primary care are well positioned to mitigate the barriers to care and the health disparities that transgender and gender nonconforming individuals experience. This article synthesizes best practices in caring for transgender and gender nonconforming patients. Key domains highlighted include (1) inclusive communication strategies, (2) gender-affirming interventions, and (3) mental health considerations.

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.

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.

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.

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.

Medical education lags with respect to care for lesbian, gay, bisexual, and transgender populations, all of which experience disparities around access to care and health outcomes. (1) To evaluate M1 students’ perceived preparedness to care for transgender patients compared to LGB populations, (2) to identify knowledge and skills gaps, and (3) to recommend curricular developments. An online survey was administered to M1 students (N = 137) and assessed knowledge of and comfort levels with special populations. Responses were compared regarding transgender and nonbinary populations to those regarding LGB ones. Analyses consisted of t tests of response means and natural language processing of a free-text field querying for knowledge and skills needed to work with these subpopulations. With a 100% response rate, students expressed significantly lower levels of knowledge and comfort regarding transgender and nonbinary populations compared to LGB ones. Natural language processing of the sentiment of the free-text field revealed greater magnitude for transgender populations (83.7 vs. 62.1) and equal average sentiment scores (0.2). Content also revealed a greater emphasis on direct interaction with trans and nonbinary persons in order to develop gender-affirming language and skills whereas LGB content focused on acquiring knowledge of LGB health issues. M1 students feel less knowledgeable about and comfort with transgender persons compared to LGB ones; they want more exposure to and interaction with trans-patient experiences in order to develop gender-affirming language and clinical practices that address the range of healthcare needs.

Objective:In 2017, an academic health center in Chicago launched the multidisciplinary Substance Use Intervention Team (SUIT) to address opioid misuse across 18 inpatient units and in a new outpatient addiction medicine clinic. This report assesses the first 5 months of implementation and associations with patient health and healthcare utilization. Methods: Patient demographic and screening data were extracted from the administrative data warehouse of the electronic health record (EHR) infrastructure. Distribution of sample characteristics for positive initial screens for opioid misuse was tested against those of all patients screened using a 2-tailed test of proportions (P < 0.05). A second analysis compared length of stay and 30-day readmissions within a cohort of patients with a secondary diagnosis of substance use disorder. Results: Between November, 2017 and March, 2018, 76% of 15,054 unique patients were screened, 578 had positive scores on the Alcohol Use Disorders Identification Test and Drug Abuse Screening Test, 131 had positive scores for opioid misuse, and 52 patients initiated medication treatment. Patients with a secondary diagnosis of substance use disorder who received a SUIT consult (n = 161), compared with those who did not (n = 612), had a shorter average length of stay (5.91 vs 6.73 days) and lower 30-day readmission rate (13.6% vs 15.7%).Conclusion:Leveraging the EHR to conduct standardized screenings and treatment has helped identify an at-risk population - disproportionately younger, black, and male - and treat new cases of opioid and substance misuse. The intervention indicates trends toward a shortened length of stay, reduced 30-day readmissions, and has linked patients to outpatient care.

OBJECTIVE: This study analyzes effectiveness of screening, referrals, and treatment uptake of a collaborative care for depression intervention across 10 primary care clinics in Chicago. METHODS: Patients were screened with the PHQ-2/9 based on an eligibility algorithm. Electronic health record data were analyzed for sample characteristics, screening rates, referrals, and treatment pathways. To identify disparities, a test of proportions was conducted between eligible and screened patients and referred and treated patients. RESULTS: From November 2016 - December 2017, 25,369 patients were eligible for screening, and rates rose to 79%, versus 7% in the prior year. Screenings, referrals, and uptake occurred proportionately across subgroups except for patients ages 12–17. Adolescent age was associated with disproportionate PHQ-9 screenings and with treatment disengagement. CONCLUSION: The intervention shows promise in expanding access to care and reducing disparities. Greater access to psychotherapies and innovative treatment modalities, particularly for adolescents, may improve overall treatment uptake.

Background Transgender medicine is an emergent subfield with clearly identified educational gaps. Aims This manuscript evaluates a gender-affirming healthcare curriculum for second-year medical (M2) students. Methods Students received a survey assessing Gender Identity Competency in terms of skills, knowledge and attitudes regarding transgender and gender non-conforming (TGNC) issues. The authors administered the survey before and after the delivery of the curriculum. The curriculum included five online modules, a quiz, a 3-hour case-based workshop and a 2-hour interactive patient-provider panel. Results Approximately 60% of M2 students (n=77) completed both preassessments and postassessments. The following showed a statistically significant improvement from preassessment to postassessment: student Gender Identity Competency, t(76) = −11.07, p Conclusions The curriculum improved students’ gender-affirming medical competency, knowledge and skills. The development of a sustained, longitudinal curriculum is recommended in addition to the continuing education of faculty to reinforce this expanding knowledge and skills base and to address discomfort working with this population.

Purpose: In 2015, the Centers for Medicare and Medicaid Services ruled that health organizations comply with additional requirements for electronic health records (EHRs), known as “Meaningful Use,” and develop the capacity to collect gender identity data. Research has established effectiveness of a two-step gender identity question to collect these data. This study examines transgender patient perspectives on the use of a two-step question and experiences with privacy and sensitive disclosures in EHRs and healthcare settings. Methods: Four focus groups (N=30) were conducted in Chicago, Illinois in 2014–2015. Participants were asked to compare two intake forms—one with a two-step question and one with a single question—and discuss experiences with gender identity disclosure, privacy, and access to care. Narratives were transcribed verbatim to identify patterns and themes; the extended case method was used and grounded the data analysis process in the concept of intersectionality. Results: Participants expressed appreciation for improved reliability and competencies that the two-part question may afford. Narratives reveal concerns related to patient privacy, safety, and access because of the contexts in which these data are collected and transmitted. Virtually all participants described situations whereby sensitive gender identity information had been involuntarily disclosed, misinterpreted, or abused, and safety and care were compromised. Conclusion: Participants recognized the potential of the two-part question as a measurement and competency tool, but anticipated new privacy violations and involuntary disclosures. Narratives indicate that effects of sensitive disclosures may vary intersectionally, whereby white participants experienced lesser harms than their immigrant, HIV-positive, and black trans feminine counterparts. Discrimination and privacy violations may occur regardless of a two-part or one-part gender identity question, but increasing these sensitive disclosures within expanding EHR infrastructures may require a range of mechanisms that have flexibility across contexts to safeguard sensitive information and access to care.

BackgroundAddiction medicine consultation services (ACS) may improve outcomes of hospitalized patients with substance use disorders (SUD). Our aim was to examine the difference in length of stay and the hazard ratio for a routine hospital discharge between SUD patients receiving and not receiving ACS.MethodsStructured EHR data from 2018 of 1,900 adult patients with a SUD-related diagnostic code at an urban academic health center were examined among 35,541 total encounters. Cox proportional hazards regression models were fit using a cause-specific approach to examine differences in hospital outcome (i.e., routine discharge, leaving against medical advice, in-hospital death, or transfer to another level of care). Models were adjusted for age, sex, race, ethnicity, insurance status, and comorbidities.ResultsLength of stay was shorter among encounters with a SUD that received a SUIT consultation versus those admissions that did not receive one (5.77 v. 6.54 days, pConclusionsThe SUIT consultation service was associated with a reduced length of stay and an increased hazard of a routine discharge. The SUIT model may serve as a benchmark and inform other health systems attempting to improve outcomes in SUD patient cohorts.

We assess the psychometric properties of the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) with a sample of 312 transgender women in San Francisco, California. In 2010, the San Francisco Department of Public Health conducted the Transfemales Empowering and Advancing Community Health (TEACH) study and administered a cross-sectional survey. Long-chain, peer-referral sampling generated a diverse sample, and this analysis uses confirmatory factor analysis, model-based reliability and construct validity to assess the WHOQOL-BREF instrument. The scale demonstrated acceptable internal consistency ranging from 0.77 to 0.78 with the exception of the one domain (0.65) consisting of three relatively heterogeneous items. The confirmatory factor analysis showed marginally good model fit (RMSEA = 0.05, 90% CI [0.043, 0.058], CFI = 0.89) for a four-factor structure. Higher quality of life (QOL) mean scores among HIV-positive respondents may indicate robust HIV care in San Francisco but al...

The HIV epidemic has disproportionately affected marginalized populations. Societal marginalization contributes to the lack of appropriate prevention programs focusing on these populations, which in turn may contribute to ongoing HIV transmission. Transmen (female-to-male transgender persons) may also be a marginalized population at risk for HIV infection. Unfortunately, few studies that focus on transmen have been conducted. We considered the available existing data sources that may include members of the transmen population in San Francisco to describe what is known and what remains to be understood with respect to their risk for HIV. Data used for this descriptive analysis were collected at HIV test sites in 2009. In 2009, 59 unduplicated test seekers identified as transmen. While behavioral risk factors present in this study point to the high potential for HIV risk, there may still be low HIV prevalence and time to intervene. Lest we be caught off guard as another marginalized sexual minority experiences the tragedy of a rapid HIV epidemic, more detailed, specifically designed studies are needed to complete what we do not know about this population.