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). Racial explicit and implicit bias were measured with the Quick Discrimination Index (QDI) and the Black-White IAT. Medical specialty was associated with racial explicit bias and specialty prestige with Black-White IAT score. Medical specialty and specialty prestige were not associated with SO bias. Female sex, sexual and gender minority (SGM) identity, and decreased religiosity were associated with reduced SO and racial bias. Provider race was associated with racial implicit and explicit bias.

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. Three MLAs were evaluated: gradient-boosted decision trees (XGBoost), Deep Long Short Term Memory neural network, and one-dimensional convolutional neural network. MLA performance was evaluated with area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, diagnostic odds ratios and likelihood ratios. RESULTS: The development dataset contained 13,664,840 inpatient encounters with 80,046 CDI encounters; the external dataset contained 1,149,088 inpatient encounters with 7,107 CDI encounters. The highest AUROCs were achieved for XGB, Deep Long Short Term Memory neural network, and one-dimensional convolutional neural network via abstaining from use of specialized training techniques, resampling in isolation, and resampling and output bias in combination, respectively. XGBoost achieved the highest AUROC. CONCLUSIONS: MLAs can predict future CDI in hospitalized patients using just 6 hours of data. In clinical practice, a machine-learning based tool may support prophylactic measures, earlier diagnosis, and more timely implementation of infection control measures.

"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.com and asked to explore the resource over a four-week period and complete online pre/post questionnaires. Participants reported a high level of satisfaction with the relatability, usefulness, and functionality of OMGyes.com. We observed statistically significant, large effect size increases in participants' knowledge about their own pleasure preferences, their confidence and positivity about that knowledge, as well as how pleasurable their sexual experiences were during both masturbation and partner sex. Many participants reported that after using OMGyes.com they felt more motivated to explore their preferences and more confident to explain their preferences to their partners. Our data suggest that OMGyes.com may be useful for positively impacting how women think about sexual pleasure, how they understand their own specific preferences, how they advocate for what they enjoy with partners, and how they actually experience pleasure.

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. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.

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. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. CONCLUSIONS: A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient's risk profiles.

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. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time. METHODS: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment. FINDINGS: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04). IMPLICATIONS: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

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

Problem: 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. However, challenges in the medical education environment may hinder the adoption and implementation of curricular interventions to foster these competencies. Intervention: Our medical education community engaged in curriculum evaluation and subsequently developed a sexual and gender minority topical sequence to promote student development of these competencies. This process was guided by explicit principles and curriculum development practices. Context: This work began at the Yale University School of Medicine in 2014, shortly after the Association of American Medical Colleges published sexual and gender minority health competencies and amidst the development and implementation of a new curriculum at the institution. Impact: We identified core principles and practices to guide the development of an integrated sexual and gender minority health sequence. This process resulted in successful creation of an integrated curricular sequence. At this time, 9 new or enhanced curricular components have been adopted through our process-5 in preclinical, 3 in the clinical, and 1 in the elective, curricula-in addition to the 13 preexisting components that have been updated as appropriate. Feedback about the process from students and faculty has been overwhelmingly positive. Evaluation of curricular components and their effectiveness as an integrated sequence is ongoing. Lessons Learned: Core principles consisted of including a wide range of stakeholders to build consensus, establishing complementary student and faculty roles, using the "language of collaboration" rather than the "language of demand," presenting sexual and gender minority content in an intersectional manner whenever possible, and balancing sexual and gender minority components across the curriculum. Key practices included mapping curriculum to identify gaps; developing curriculum "pitches"; identifying early and potential later "adopters" among faculty; focusing on faculty ownership of curriculum to facilitate institutionalization; and performing ongoing tracking, assessment, and revision of curriculum.