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Importance: Clinician attitudes toward telehealth may impact utilization rates, and findings may differ based on specialty., Objective: To determine whether clinician beliefs regarding telehealth quality and ease of use were associated with the proportion of care delivered via video, phone, and in-person across specialties., Design, Setting, and Participants: This survey study used a voluntary, anonymous survey conducted from August to September 2021 in the Department of Veterans Affairs New England Healthcare System (VANEHS). Mental health (MH), primary care (PC), and specialty care (SC) clinicians were invited to participate. Data were analyzed from October 2021 to January 2022., Exposures: Participation in a 32-item survey., Main Outcomes and Measures: The main outcomes were clinicians' views on relative quality of video, phone, and in-person care; factors contributing to clinicians' modality choice; telehealth challenges; and clinician modality preferences and utilization when treating new and established patients., Results: There were 866 survey respondents (estimated 64% response rate); 52 respondents reported no video or phone telehealth use in the 3 months prior to survey completion and were excluded, resulting in a final sample of 814 respondents. Respondents were divided among MH (403 respondents [49.5%]), PC (153 respondents [18.8%]), and SC (258 respondents [31.7%]). Compared with PC and SC clinicians, MH clinicians rated the quality of video care the highest (eg, compared with in-person care with masks when treating new patients: χ2 = 147.8; P < .001) and were more likely to prefer video over phone when treating both new (χ2 = 26.6; P < .001) and established (χ2 = 100.4; P < .001) patients remotely. PC and SC clinicians were more likely to rate phone care as being at least equivalent in quality to video for both new (χ2 = 26.3; P < .001) and established (χ2 = 33.5; P < .001) patients. PC and SC clinicians were also more likely to endorse challenges of video care, including patient barriers and the inability to conduct a physical examination (χ2 = 292.0; P < .001). Most PC and SC clinicians either had no preference (46 PC respondents [36.2%]; 59 SC respondents [28.4%]) or preferred phone (36 PC respondents [28.3%]; 67 SC respondents [32.2%]) for remote care of established patients. Findings aligned with utilization rates within VANEHS, with MH clinicians conducting significantly more of their encounters via video (36 734 encounters [40.3%]) than PC (3201 encounters [3.9%]) and SC (1157 encounters [4.9%]) clinicians., Conclusions and Relevance: These findings suggest that clinician attitudes regarding telehealth quality and ease of use were associated with utilization rates. Moving forward, clinician use of telehealth may be impacted by additional data regarding the relative effectiveness of modalities as well as improvements in video telehealth workflows.

Importance: Compared with the general population, veterans are at high risk for COVID-19 and have a complex relationship with the government. This potentially affects their attitudes toward receiving COVID-19 vaccines., Objective: To assess veterans' attitudes toward and intentions to receive COVID-19 vaccines., Design, Setting, and Participants: This cross-sectional web-based survey study used data from the Department of Veterans Affairs (VA) Survey of Healthcare Experiences of Patients' Veterans Insight Panel, fielded between March 12 and 28, 2021. Of 3420 veterans who were sent a link to complete a 58-item web-based survey, 1178 veterans (34%) completed the survey. Data were analyzed from April 1 to August 25, 2021., Exposures: Veterans eligible for COVID-19 vaccines., Main Outcomes and Measures: The outcomes of interest were veterans' experiences with COVID-19, vaccination status and intention groups, reasons for receiving or not receiving a vaccine, self-reported health status, and trusted and preferred sources of information about COVID-19 vaccines. Reasons for not getting vaccinated were classified into categories of vaccine deliberation, dissent, distrust, indifference, skepticism, and policy and processes., Results: Among 1178 respondents, 974 (83%) were men, 130 (11%) were women, and 141 (12%) were transgender or nonbinary; 58 respondents (5%) were Black, 54 veterans (5%) were Hispanic or Latino, and 987 veterans (84%) were non-Hispanic White. The mean (SD) age of respondents was 66.7 (10.1) years. A total of 817 respondents (71%) self-reported being vaccinated against COVID-19. Of 339 respondents (29%) who were not vaccinated, those unsure of getting vaccinated were more likely to report fair or poor overall health (32 respondents [43%]) and mental health (33 respondents [44%]) than other nonvaccinated groups (overall health: range, 20%-32%; mental health: range, 18%-40%). Top reasons for not being vaccinated were skepticism (120 respondents [36%] were concerned about side effects; 65 respondents [20%] preferred using few medications; 63 respondents [19%] preferred gaining natural immunity), deliberation (74 respondents [22%] preferred to wait because vaccine is new), and distrust (61 respondents [18%] did not trust the health care system). Among respondents who were vaccinated, preventing oneself from getting sick (462 respondents [57%]) and contributing to the end of the COVID-19 pandemic (453 respondents [56%]) were top reasons for getting vaccinated. All veterans reported the VA as 1 of their top trusted sources of information. The proportion of respondents trusting their VA health care practitioner as a source of vaccine information was higher among those unsure about vaccination compared with those who indicated they would definitely not or probably not get vaccinated (18 respondents [26%] vs 15 respondents [15%]). There were no significant associations between vaccine intention groups and age (χ24 = 5.90; P = .21) or gender (χ22 = 3.99; P = .14)., Conclusions and Relevance: These findings provide information needed to develop trusted messages used in conversations between VA health care practitioners and veterans addressing specific vaccine hesitancy reasons, as well as those in worse health. Conversations need to emphasize societal reasons for getting vaccinated and benefits to one's own health.

Importance: Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use., Objective: To compare different machine learning methods in predicting overall mortality in cirrhosis and to use machine learning to select easily scored clinical variables for a novel cirrhosis prognostic model., Design, Setting, and Participants: This prognostic study used a retrospective cohort of adult patients with cirrhosis or its complications seen in 130 hospitals and affiliated ambulatory clinics in the integrated, national Veterans Affairs health care system from October 1, 2011, to September 30, 2015. Patients were followed up through December 31, 2018. Data were analyzed from October 1, 2017, to May 31, 2020., Exposures: Potential predictors included demographic characteristics; liver disease etiology, severity, and complications; use of health care resources; comorbid conditions; and comprehensive laboratory and medication data. Patients were randomly selected for model development (66.7%) and validation (33.3%). Three different statistical and machine learning methods were evaluated: gradient descent boosting, logistic regression with least absolute shrinkage and selection operator (LASSO) regularization, and logistic regression with LASSO constrained to select no more than 10 predictors (partial pathway model). Predictor inclusion and model performance were evaluated in a 5-fold cross-validation. Last, the predictors identified in the most parsimonious (the partial path) model were refit using maximum-likelihood estimation (Cirrhosis Mortality Model [CiMM]), and its predictive performance was compared with that of the widely used Model for End Stage Liver Disease with sodium (MELD-Na) score., Main Outcomes and Measures: All-cause mortality., Results: Of the 107 939 patients with cirrhosis (mean [SD] age, 62.7 [9.6] years; 96.6% male; 66.3% white, 18.4% African American), the annual mortality rate ranged from 8.8% to 15.3%. In total, 32.7% of patients died within 3 years, and 46.2% died within 5 years after the index date. Models predicting 1-year mortality had good discrimination for the gradient descent boosting (area under the receiver operating characteristics curve [AUC], 0.81; 95% CI, 0.80-0.82), logistic regression with LASSO regularization (AUC, 0.78; 95% CI, 0.77-0.79), and the partial path logistic model (AUC, 0.78; 95% CI, 0.76-0.78). All models showed good calibration. The final CiMM model with machine learning-derived clinical variables offered significantly better discrimination than the MELD-Na score, with AUCs of 0.78 (95% CI, 0.77-0.79) vs 0.67 (95% CI, 0.66-0.68) for 1-year mortality, respectively (DeLong z = 17.00; P < .001)., Conclusions and Relevance: In this study, simple machine learning techniques performed as well as the more advanced ensemble gradient boosting. Using the clinical variables identified from simple machine learning in a cirrhosis mortality model produced a new score more transparent than machine learning and more predictive than the MELD-Na score.

Context: Deficits in advance care planning leave many patients and their physicians unprepared for decisions about end-of-life care. Even though the prognosis has improved for many persons with human immunodeficiency virus (HIV) infection, a need for planning remains., Objective: To evaluate prevalence of end-of-life discussions, use of advance directives, and preferences concerning end-of-life care and their relationship with patient demographics, clinical status, psychosocial variables, and practitioner characteristics among HIV-infected persons., Design, Setting, and Patients: Cross-sectional survey of a US probability sample of 2864, which represents 231 400 adults receiving care for HIV, conducted from January 1996 to April 1997., Main Outcome Measures: Communication with physician regarding end-of-life issues, completion of an advance directive, preference for aggressiveness of care, and willingness to tolerate future permanent adverse health states., Results: A total of 1432 patients (50%) discussed some aspect of end-of-life care with their practitioner and 1088 (38%) completed an advance directive. Patients were more likely to complete an advance directive after a physician discussion (odds ratio [OR], 5.82; 95% confidence interval [CI], 4.50-7.52). Practitioners discussed end-of-life care less with blacks (OR, 0.57; 95% CI, 0.39-0.83) and Latinos (OR, 0.74; 95% CI, 0.55-0.98) than with whites. Women (OR, 1.39; 95% CI, 1.05-1.84) and patients with children in the household (OR, 1.53; 95% CI, 1.12-2.10) communicated the most with practitioners about end-of-life issues. Patients infected with HIV via injection drug use (OR, 0.64; 95% CI, 0.45-0.89) and those with less education communicated the least with physicians about end-of-life issues. Less denial, greater trust in one's practitioner, and longer patient-practitioner relationship were associated with more advance care planning., Conclusions: Half of all persons infected with HIV are at risk of making end-of-life decisions without prior discussions with their health care practitioners. Blacks, Latinos, intravenous drug users, and less educated individuals need advance care planning interventions in clinical HIV programs.