Sexual Orientation and Racial Bias in Relation to Medical Specialty.

Physician explicit and implicit biases involving race and sexual orientation (SO) affect patient and provider experiences in healthcare settings. An anonymous survey was disseminated nationally to graduating medical students, residents, and practicing physicians to evaluate SO and racial biases across medical specialties. SO explicit and implicit bias were measured with the Attitudes toward Lesbians and Gay Men Scale, short form (ATLG-S) and Gay-Straight Implicit Association Test (IAT).

A comparative analysis of machine learning approaches to predict C. difficile infection in hospitalized patients.

Background: Interventions to better prevent or manage Clostridioides difficile infection (CDI) may significantly reduce morbidity, mortality, and healthcare spending.

Methods: We present a retrospective study using electronic health record data from over 700 United States hospitals. A subset of hospitals was used to develop machine learning algorithms (MLAs); the remaining hospitals served as an external test set.

Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification.

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data.

"OMG, Yes!": Feasibility, Acceptability, and Preliminary Efficacy of an Online Intervention for Female Sexual Pleasure.

The purpose of this study was to evaluate the acceptability, feasibility, and preliminary efficacy of using an online educational resource that presents research-informed strategies for women's pleasure, OMGyes.com, as a resource to empower women to broaden the ways in which they understand, advocate for, and enjoy sexual pleasure. A cohort of 870 adult women was given access to OMGyes.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

Purpose: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources.

Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data.

Background: Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification.

An Analysis of the Presence and Composition of OutLists at United States, Canadian, and European Medical Institutions.

Sexual and gender minority (SGM) medical students and physicians are exposed to bias in professional contexts. One strategy for promoting SGM visibility and inclusion within medicine is the development of institutional OutLists, which are online, opt-in lists of SGM-identified individuals affiliated with an academic institution. We present the first quantitative evaluation of publicly accessible OutLists at medical institutions in the United States, Canada, and Europe.

Pediatric Residents' Beliefs and Behaviors About Health Care for Sexual and Gender Minority Youth.

Little is known about provider beliefs related to sexual and gender minority (SGM) youth, and how these have changed over time. Our objective was to compare pediatric residents' beliefs and behaviors about SGM youth to historical data. Forty-eight of 76 (63%) residents completed a survey of items drawn from 2 existing scales.

Principles and Practices for Developing an Integrated Medical School Curricular Sequence About Sexual and Gender Minority Health.

: Sexual and gender minority patients face well-documented health disparities. One strategy to help overcome disparities is preparing medical trainees to competently provide care for sexual and gender minority patients. The Association of American Medical Colleges has identified professional competencies that medical students should develop to meet sexual and gender minority health needs.

Sexual and gender minority health in medical curricula in new England: a pilot study of medical student comfort, competence and perception of curricula.

Background: Sexual and gender minority (SGM) individuals experience high rates of harassment and discrimination when seeking healthcare, which contributes to substantial healthcare disparities. Improving physician training about gender identity, sexual orientation, and the healthcare needs of SGM patients has been identified as a critical strategy for mitigating these disparities. In 2014, the Association of American Medical Colleges (AAMC) published medical education competencies to guide undergraduate medical education on SGM topics.