Overcoming Obstacles: Barriers to Virtual Care Use Among Video-Enabled Tablet Recipients in the Veterans Health Administration.

INTRODUCTION: The Veterans Health Administration (VHA) distributes video-enabled tablets to individuals with barriers to accessing care. Data suggests that many tablets are under-used. We surveyed Veterans who received a tablet to identify barriers that are associated with lower use, and evaluated the impact of a telephone-based orientation call on reported barriers and future video use. METHODS: We used a national survey to assess for the presence of 13 barriers to accessing video-based care, and then calculated the prevalence of the barriers stratified by video care utilization in the 6 months after survey administration. We used multivariable modeling to examine the association between each barrier and video-based care use and evaluated whether a telephone-based orientation modified this association. RESULTS: The most prevalent patient-reported barriers to video-based care were not knowing how to schedule a visit, prior video care being rescheduled/canceled, and past problems using video care. Following adjustment, individuals who reported vision or hearing difficulties and those who reported that video care does not provide high-quality care had a 19% and 12% lower probability of future video care use, respectively. Individuals who reported no interest in video care, or did not know how to schedule a video care visit, had an 11% and 10% lower probability of being a video care user, respectively. A telephone-based orientation following device receipt did not improve the probability of being a video care user. DISCUSSION: Barriers to engaging in virtual care persist despite access to video-enabled devices. Targeted interventions beyond telephone-based orientation are needed to facilitate adoption and engagement in video visits.

Research Priorities to Expand Virtual Care Access for Patients in the Veterans Affairs Health Care System.

The rapid expansion of virtual care is driving demand for equitable, high-quality access to technologies that are required to utilize these services. While the Department of Veterans Affairs (VA) is seen as a national leader in the implementation of telehealth, there remain gaps in evidence about the most promising strategies to expand access to virtual care. To address these gaps, in 2022, the VA's Health Services Research and Development service and Office of Connected Care held a "state-of-the-art" (SOTA) conference to develop research priorities for advancing the science, clinical practice, and implementation of virtual care. One workgroup within the SOTA focused on access to virtual care and addressed three questions: (1) Based on the existing evidence about barriers that impede virtual care access in digitally vulnerable populations, what additional research is needed to understand these factors? (2) Based on the existing evidence about digital inclusion strategies, what additional research is needed to identify the most promising strategies? and (3) What additional research beyond barriers and strategies is needed to address disparities in virtual care access? Here, we report on the workgroup's discussions and recommendations for future research to improve and optimize access to virtual care. Effective implementation of these recommendations will require collaboration among VA operational leadership, researchers, Human Factors Engineering experts and front-line clinicians as they develop, implement, and evaluate the spread of virtual care access strategies.

Cost-Related Access Barriers, Medical Debt, and Dissatisfaction with Care Among Privately Insured Americans.

BACKGROUND: Understanding experiences with private important to improving the quality of health care coverage. OBJECTIVE: To examine the association of health with cost-related access barriers, medical debt, and dissatisfaction with care among privately insured Americans. DESIGN: We classified Americans with private insurance by self-reported health status into five groups (excellent, very good, good, fair, and poor health). We examined self-reported difficulty seeing a doctor due to costs, not taking medications due to costs, medical debt, and dissatisfaction with care among individuals with differing health status. We used logistic regression to examine the association of health status with individuals' experiences after accounting for baseline characteristics. The analysis was repeated among individuals with different forms of private insurance. Odds ratios were converted to risk ratios to improve ease of interpretation of the results. SETTING: Behavioral Risk Factor Surveillance System of Americans in 17 states RESULTS: The sample included 82,494 US adults with private insurance. Following adjustment, compared to individuals with excellent health those in very good health, good health, fair health, and poor health reported increasingly higher risks of difficulty seeing a doctor due to costs with risk ratios of 1.02 (95% CI 1.01, 1.03), 1.07 (95% CI 1.06, 1.08), 1.18 (95% CI 1.17, 1.20), and 1.29 (95% CI 1.27, 1.31), respectively. Compared to individuals with excellent health, those in very good health, good health, fair health, and poor health reported increasingly higher risks of not taking medication due to costs, outstanding medical debt, and dissatisfaction with care. Similar relationships were seen across individually purchased and employer-sponsored insurance. CONCLUSION: Cost-related access barriers, medical debt, and dissatisfaction with care were common among individuals with private insurance and most pronounced among those with fair and poor health who likely need and use their health insurance the most.

Association of Social Determinants of Health and Their Cumulative Impact on Hospitalization Among a National Sample of Community-Dwelling US Adults.

IMPORTANCE: While the association between Social Determinants of Health (SDOH) and health outcomes is well known, few studies have explored the impact of SDOH on hospitalization. OBJECTIVE: Examine the independent association and cumulative effect of six SDOH domains on hospitalization. DESIGN: Using cross-sectional data from the 2016-2018 National Health Interview Surveys (NHIS), we used multivariable logistical regression models controlling for sociodemographics and comorbid conditions to assess the association of each SDOH and SDOH burden (i.e., cumulative number of SDOH) with hospitalization. SETTING: National survey of community-dwelling individuals in the US PARTICIPANTS: Adults ≥18 years who responded to the NHIS survey EXPOSURE: Six SDOH domains (economic instability, lack of community, educational deficits, food insecurity, social isolation, and inadequate access to medical care) MEASURES: Hospitalization within 1 year RESULTS: Among all 55,186 respondents, most were ≤50 years old (54.2%), female (51.7%, 95% CI 51.1-52.3), non-Hispanic (83.9%, 95% CI 82.4-84.5), identified as White (77.9%, 95% CI 76.8-79.1), and had health insurance (90%, 95% CI 88.9-91.9). Hospitalized individuals (n=5506; 8.7%) were more likely to be ≥50 years old (61.2%), female (60.7%, 95% CI 58.9-62.4), non-Hispanic (87%, 95% CI 86.2-88.4), and identify as White (78.5%, 95% CI 76.7-80.3), compared to those who were not hospitalized. Hospitalized individuals described poorer overall health, reporting higher incidence of having ≥5 comorbid conditions (38.9%, 95% CI 37.1-40.1) compared to those who did not report a hospitalization (15.9%, 95% CI 15.4-16.5). Hospitalized respondents reported higher rates of economic instability (33%), lack of community (14%), educational deficits (67%), food insecurity (14%), social isolation (34%), and less access to health care (6%) compared to non-hospitalized individuals. In adjusted analysis, food insecurity (OR: 1.36, 95% CI 1.22-1.52), social isolation (OR: 1.17, 95% CI 1.08-1.26), and lower educational attainment (OR: 1.12, 95% CI 1.02-1.25) were associated with hospitalization, while a higher SDOH burden was associated with increased odds of hospitalization (3-4 SDOH [OR: 1.25, 95% CI 1.06-1.49] and ≥5 SDOH [OR: 1.72, 95% CI 1.40-2.06]) compared to those who reported no SDOH. CONCLUSIONS: Among community-dwelling US adults, three SDOH domains: food insecurity, social isolation, and low educational attainment increase an individual's risk of hospitalization. Additionally, risk of hospitalization increases as SDOH burden increases.

Examining the association of social risk with heart failure readmission in the Veterans Health Administration.

BACKGROUND: Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance. METHODS: We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62). CONCLUSIONS: While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

The visual abstract: A social media fad or the future of dissemination.

This editorial discusses the emergence of visual abstracts within journals to disseminate findings. Published alongside Aggarwal's retrospective study reporting that visual abstracts do not increase impact scores more than conventional abstracts of clinical research, it is suggested that visual abstracts may have a greater impact for smaller, specialty journals.

Comparison of Pharmacy Database Methods for Determining Prevalent Chronic Medication Use.

BACKGROUND: Pharmacy dispensing data are frequently used to identify prevalent medication use as a predictor or covariate in observational research studies. Although several methods have been proposed for using pharmacy dispensing data to identify prevalent medication use, little is known about their comparative performance. OBJECTIVES: The authors sought to compare the performance of different methods for identifying prevalent outpatient medication use. RESEARCH DESIGN: Outpatient pharmacy fill data were compared with medication reconciliation notes denoting prevalent outpatient medication use at the time of hospital admission for a random sample of 207 patients drawn from a national cohort of patients admitted to Veterans Affairs hospitals. Using reconciliation notes as the criterion standard, we determined the test characteristics of 12 pharmacy database algorithms for determining prevalent use of 11 classes of cardiovascular and diabetes medications. RESULTS: The best-performing algorithms included a 180-day fixed look-back period approach (sensitivity, 93%; specificity, 97%; and positive predictive value, 89%) and a medication-on-hand approach with a grace period of 60 days (sensitivity, 91%; specificity, 97%; and positive predictive value, 91%). Algorithms that have been commonly used in previous studies, such as defining prevalent medications to include any medications filled in the prior year or only medications filled in the prior 30 days, performed less well. Algorithm performance was less accurate among patients recently receiving hospital or nursing facility care. CONCLUSION: Pharmacy database algorithms that balance recentness of medication fills with grace periods performed better than more simplistic approaches and should be considered for future studies which examine prevalent chronic medication use.