Objectives An understanding of mental health symptoms during the coronavirus disease 2019 (COVID-19) pandemic is critical to ensure that health policies adequately address the mental health needs of people in the United States. The objective of this study was to examine mental health symptoms among US adults in an early stage of the COVID-19 pandemic. Methods We conducted a cross-sectional study in late March 2020 with a national sample of 963 US adults using an online research platform. Participants self-reported state of residence, psychosocial characteristics, and levels of anxiety, depression, anger, cognitive function, and fatigue in the context of COVID-19 using validated patient-reported outcomes scales in the Patient-Reported Outcome Measurement Information System measures. We used analysis of variance and multivariate linear regression to evaluate correlates of mental health symptoms. Results Overall, participants reported high levels of anxiety (mean [SD], 57.2 [9.3]) and depression (mean [SD], 54.2 [9.5]). Levels of anger, anxiety, cognitive function, depression, and fatigue were significantly higher among the Millennial Generation and Generation X (vs Baby Boomers), those with not enough or enough (vs more than enough) financial resources, females vs males), those with self-reported disability (vs no self-reported disability), and those with inadequate (vs adequate) health literacy. In adjusted models, being in Generation X and the Millennial Generation (vs Baby Boomer), having not enough or enough vs more than enough) financial resources, and having inadequate (vs adequate) health literacy were most strongly correlated with worse mental health symptoms. Conclusions Results suggest that mental health symptoms during the early stages of the COVID-19 pandemic were prevalent nationally, regardless of state of residence and especially among young, psychosocially vulnerable groups.

Objectives Early hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission. Methods We extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual-and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients. Results The Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual-and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015–2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

Objectives Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs. Materials and Methods We conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4) text plus line graph. Each participant viewed every condition, and we used counterbalancing (systematic randomization) to control for potential order effects. We assessed objective comprehension using the International Organization for Standardization protocol. Secondary outcomes included response times, preferences, risk perceptions, and behavioral intentions. Results Overall, 63% correctly comprehended the text-only condition and 60% comprehended the line graph condition, compared with 83% for the visual analogy and 70% for the number line (P = .05) conditions. Participants comprehended the visual analogy significantly better than the text-only (P = .02) and line graph (P = .02) conditions. Of participants who comprehended at least 1 condition, 14% preferred a condition that they did not comprehend. Low comprehension was associated with worse cognition (P < .001), lower education level (P = .02), and fewer financial resources (P = .03). Conclusions The results support using visual analogies rather than text to display longitudinal PROs but caution against relying on graphs, which is consistent with the known high prevalence of inadequate graph literacy. The discrepancies between comprehension and preferences suggest factors other than comprehension influence preferences, and that future researchers should assess comprehension rather than preferences to guide presentation decisions.

Objectives As personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data. Methods We searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized. Results In 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation. Conclusion In this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.

OBJECTIVES Patient‐Reported Outcomes Measurement Information System (PROMIS) measures can monitor patients with chronic illnesses outside of healthcare settings. Unfortunately, few applications that collect electronic PROMIS measures are designed using inclusive design principles that ensure wide accessibility and usability, thus limiting use by older adults with chronic illnesses. Our aim was to establish the feasibility of using an inclusively designed mobile application tailored to older adults to report PROMIS measures by examining (1) PROMIS scores collected with the application, (2) patient‐reported usability of the application, and (3) differences in usability by age. DESIGN Cross‐sectional feasibility study. SETTING Inpatient and outpatient cardiac units at an urban academic medical center. PARTICIPANTS A total of 168 English‐ and Spanish‐speaking older adults with heart failure. INTERVENTION Participants used an inclusively designed mobile application to self‐report PROMIS measures. MEASUREMENTS Eleven PROMIS Short‐Form questionnaires (Anxiety, Ability to Participate in Social Roles and Activities, Applied Cognition‐Abilities, Depression, Emotional Distress‐Anger, Fatigue, Global Mental Health, Global Physical Health; Pain Interference, Physical Function, Sleep Disturbance), and a validated health technology usability survey measuring Perceived Ease‐of‐Use and Usefulness of the application. RESULTS Overall, 27% of participants were between 65 and 74 years of age, 10% were 75 years or older, 63% were male, 32% were white, and 96% had two or more medical conditions. There was no missing PROMIS data, and mean PROMIS scores showed the greatest burden of pain, fatigue, and physical function in the sample. Usability scores were high and not associated with age (Perceived Ease‐of‐Use P = .77; Perceived Usefulness P = .91). CONCLUSION It is feasible for older adults to use an inclusively designed application to report complete PROMIS data with high perceived usability. To ensure data completeness and the opportunity to study multiple domains of physical, mental, and social health, future work should use inclusive design principles for applications collecting PROMIS measures among older adults. J Am Geriatr Soc 68:1313–1318, 2020.

BackgroundDepression and anxiety in patients with atrial fibrillation (AF) and/or atrial flutter may influence the effectiveness of cardioversion and ablation. There is a lack of knowledge related to depressive symptoms and anxiety at the time of these procedures.ObjectiveWe aimed to describe the prevalence and explore potential covariates of depressive symptoms and anxiety in patients with AF at the time of cardioversion or ablation. We further explored the influence of depressive symptoms and anxiety on quality of life at the time of procedure and 6-month AF recurrence.MethodsDepressive symptoms, anxiety, and quality of life were collected at the time of cardioversion or ablation using the Patient Health Questionnaire-9, State-Trait Anxiety Inventory, and Atrial Fibrillation Effect on Quality of Life questionnaire. Presence of AF recurrence within 6 months post procedure was evaluated.ResultsParticipants (N = 171) had a mean (SD) age of 61.20 (11.23) years and were primarily male (80.1%) and white, non-Hispanic (81.4%). Moderate to severe depressive symptoms (17.2%) and clinically significant state (30.2%) and trait (23.6%) anxiety were reported. Mood/anxiety disorder diagnosis was associated with all 3 symptoms. Atrial fibrillation symptom severity was associated with both depressive symptoms and trait anxiety. Heart failure diagnosis and digoxin use were also associated with depressive symptoms. Trends toward significance between state and trait anxiety and participant race/ethnicity as well as depressive symptoms and body mass index were observed. Study findings support associations between symptoms and quality of life, but not 6-month AF recurrence.ConclusionDepressive symptoms and anxiety are common in patients with AF. Healthcare providers should monitor patients with AF for depressive symptoms and anxiety at the time of procedures and intervene when indicated. Additional investigations on assessment, prediction, treatment, and outcome of depressive symptoms and anxiety in patients with AF are warranted.

The Ambulatory and Hospital Care Statistics Branch is pleased to release the most current nationally representative data on ambulatory care visits to physician offices in the United States. Statistics are presented on physician practices as well as patient and visit characteristics using data collected in the 2015 National Ambulatory Medical Care Survey (NAMCS). NAMCS is an annual nationally representative sample survey of visits to nonfederal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists. Visit estimates for the following 16 states that were targeted for separate estimation are included in the summary tables: