Evaluation of Depression and Anxiety in a Diverse Population With Thyroid Eye Disease Using the Nationwide NIH All of Us Database.

PURPOSE: To evaluate the prevalence of depression and anxiety among individuals with thyroid eye disease (TED) and identify sociodemographic risk factors using the NIH All of Us database. METHODS: Three hundred ninety eight cases with TED were compared with 1,592 controls with demographics matching the 2020 US Census. Primary outcomes were diagnosed depression or anxiety; Patient Health Questionnaire-9 (PHQ-9) scores and General Anxiety Disorder-7 (GAD-7) scores were included as secondary outcomes. We performed multivariable logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between TED and depression and anxiety. RESULTS: Patients with TED were more likely to have depression (OR 2.72, 95% CI 2.08-3.56, p < 0.001) and anxiety (OR 2.82, 95% CI 2.16-3.70, p < 0.001) than controls. In patients with TED, female gender was an independent risk factor for both depression (OR 1.72; 95% CI 1.00-5.07, p = 0.05) and anxiety (OR 2.17, 95% CI 1.25-3.85, p = 0.006). Unemployment (OR 1.72, 95% CI 1.03-2.94, p = 0.04) and lower income (OR 0.88 for income as a continuous variable, 95% CI 0.79-0.99, p = 0.03) were risk factors for anxiety. Risk factors for more severe depression as assessed by PHQ-9 included lower income (OR 0.70, 95% CI 0.57-0.85, p < 0.001), and protective factors included Black race (OR 0.12, 95% CI 0.02-0.45, p = 0.002). Lower income was associated with more severe anxiety as assessed by GAD-7 (OR 0.77, 95% CI 0.64-0.94, p = 0.009). CONCLUSIONS: Patients with TED were more likely to develop depression and anxiety compared with controls. This study highlights the psychosocial impact of TED and associated socioeconomic risk factors.

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic.

OBJECTIVE: The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response. MATERIALS AND METHODS: We designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series. RESULTS: Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows. DISCUSSION: Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones. CONCLUSIONS: CI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic.

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions.

BACKGROUND: Electronic health record (EHR) vendors now offer "off-the-shelf" artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR. OBJECTIVES: The aim is to conduct a case study centered on identifying barriers to uptake/utilization. METHODS: A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies. RESULTS: We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services). CONCLUSION: AI models for risk stratification, even if "off-the-shelf" by design, are unlikely to be "plug-and-play" in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.

Fronto-limbic function in unaffected offspring at familial risk for bipolar disorder during an emotional working memory paradigm.

Evidence from neuroimaging studies indicate that individuals with bipolar disorder (BD) exhibit altered functioning of fronto-limbic systems implicated in voluntary emotion regulation. Few studies, however, have examined the extent to which unaffected youth at familial risk for BD exhibit such alterations. Using an fMRI emotional working memory paradigm, we investigated the functioning of fronto-limbic systems in fifteen healthy bipolar offspring (8-17 years old) with at least one parent diagnosed with BD (HBO), and 16 age-matched healthy control (HC) participants. Neural activity and functional connectivity analyses focused on a priori neural regions supporting emotion processing (amygdala and ventral striatum) and voluntary emotion regulation (ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC)). Relative to HC, HBO exhibited greater right VLPFC (BA47) activation in response to positive emotional distracters and reduced VLPFC modulation of the amygdala to both the positive and negative emotional distracters; there were no group differences in connectivity for the neutral distracters. These findings suggest that alterations in the functioning of fronto-limbic systems implicated in voluntary emotion regulation are present in unaffected bipolar offspring. Future longitudinal studies are needed to determine the extent to which such alterations represent neurodevelopmental markers of risk for future onset of BD.