Opportunities to Enhance the Implementation of Veterans Affairs Video-Based Care: Qualitative Perspectives of Providers from Diverse Specialties.

BACKGROUND: Increasing the adoption of digital care tools, including video visits, is a long-term goal for the US Department of Veterans Affairs (VA). While previous work has highlighted patient-specific barriers to the use of video visits, few have examined how clinicians view such barriers and how they have overcome them during the rapid uptake of web-based care. OBJECTIVE: This study sought input from providers, given their role as critical participants in video visit implementation, to qualitatively describe successful strategies providers used to adapt their practices to a web-based care setting. METHODS: We conducted interviews with 28 VA providers (physicians and nurse practitioners) from 4 specialties that represent diverse clinical services: primary care (n=11), cardiology (n=7), palliative care (n=5), and spinal cord injury (n=5). All interviews were audio recorded and transcribed, and transcripts were reviewed and coded according to an iteratively created codebook. To identify themes, codes were grouped together into categories, and participant comments were reviewed for repetition and emphasis on specific points. Finally, themes were mapped to Expert Recommendations for Implementing Change (ERIC) strategies to identify evidence-based opportunities to support video visit uptake in the VA. RESULTS: Interviewees were mostly female (57%, 16/28), with an average age of 49 years and with 2-20 years of experience working in the VA across 16 unique VA facilities. Most providers (82%, 23/28) worked in urban facilities. Many interviewees (78%, 22/28) had some experience with video visits prior to the COVID-19 pandemic, though a majority (61%, 17/28) had conducted fewer than 50 video visits in the quarter prior to recruitment. We identified four primary themes related to how providers adapt their practices to a web-based care setting: (1) peer-based learning and support improved providers' perceived value of and confidence in video visits, (2) providers developed new and refined existing communication and clinical skills to optimize video visits, (3) providers saw opportunities to revisit and refine team roles to optimize the value of video visits for their care teams, and (4) implementing and sustaining web-based care requires institutional and organizational support. We identified several ERIC implementation strategies to support the use of video visits across the individual-, clinic-, and system-levels that correspond to these themes: (1) individual-level strategies include the development of educational materials and conducting education meetings, (2) clinic-level strategies include identifying champions and revising workflows and professional roles, and (3) system-level strategies include altering incentive structures, preparing implementation blueprints, developing and implementing tools for quality monitoring, and involving executive leadership to encourage adoption. CONCLUSIONS: This work highlights strategies to support video visits that align with established ERIC implementation constructs, which can be used by health care systems to improve video visit implementation.

Variation in initial and continued use of primary, mental health, and specialty video care among Veterans.

OBJECTIVE: To identify which Veteran populations are routinely accessing video-based care. DATA SOURCES AND STUDY SETTING: National, secondary administrative data from electronic health records at the Veterans Health Administration (VHA), 2019-2021. STUDY DESIGN: This retrospective cohort analysis identified patient characteristics associated with the odds of using any video care; and then, among those with a previous video visit, the annual rate of video care utilization. Video care use was reported overall and stratified into care type (e.g., primary, mental health, and specialty video care) between March 10, 2020 and February 28, 2021. DATA COLLECTION: Veterans active in VA health care (>1 outpatient visit between March 11, 2019 and March 10, 2020) were included in this study. PRINCIPAL FINDINGS: Among 5,389,129 Veterans in this evaluation, approximately 27.4% of Veterans had at least one video visit. We found differences in video care utilization by type of video care: 14.7% of Veterans had at least one primary care video visit, 10.6% a mental health video visit, and 5.9% a specialty care video visit. Veterans with a history of housing instability had a higher overall rate of video care driven by their higher usage of video for mental health care compared with Veterans in stable housing. American Indian/Alaska Native Veterans had reduced odds of video visits, yet similar rates of video care when compared to White Veterans. Low-income Veterans had lower odds of using primary video care yet slightly elevated rates of primary video care among those with at least one video visit when compared to Veterans enrolled at VA without special considerations. CONCLUSIONS: Variation in video care utilization patterns by type of care identified Veteran populations that might require greater resources and support to initiate and sustain video care use. Our data support service specific outreach to homeless and American Indian/Alaska Native Veterans.

Crossing the digital divide: a veteran affairs program to distribute video-enabled devices to patients in a supportive housing program

Objective Evaluate an initiative to distribute video-enabled tablets and cell phones to individuals enrolled in Veterans Health Affairs supportive housing program during the COVID-19 pandemic. Materials and Methods In September 2020, individuals in the Veteran Health Affairs (VA) Housing and Urban Development-VA Supportive Housing (HUD-VASH) program were offered either a video-enabled tablet or cellphone to support their communication and health care engagement needs. We examined sociodemographic and clinical characteristics of device recipients, and compared engagement in in-person, telephone, and video-based visits (categorized as primary care, specialty care, rehabilitation, HUD-VASH, mental health care, and other) for 6 months prior to (March 1, 2020–August 31, 2020) and following (September 1, 2020–July 30, 2021) device receipt. Results In total, 5127 Veterans received either a tablet (n = 4454) or a cellphone (n = 673). Compared to the 6 months prior to device receipt, in the 6 months following receipt, in-person and video engagement increased by an average of 1.4 visits (8%) and 3.4 visits (125%), respectively, while telephone engagement decreased (−5.2 visits;−27%). Both tablet and cellphone recipients had increased in-person visits (+1.3 visits [8%] and +2.1 visits [13%], respectively);while tablet users had a substantially larger increase in video-based engagement (+3.2 visits [+110%] vs. +0.9 [+64%]). Similar trends were noted across all assessed types of care. Discussion Providing video-enabled devices to Veterans in a supportive housing program may facilitate engagement in health care. Conclusions and Relevance VA’s device distribution program offers a model for expanding access to health-related technology and telemedicine to individuals in supportive housing programs.

Factors Influencing How Providers Assess the Appropriateness of Video Visits: Interview Study With Primary and Specialty Health Care Providers.

BACKGROUND: The rapid implementation of virtual care (ie, telephone or video-based clinic appointments) during the COVID-19 pandemic resulted in many providers offering virtual care with little or no formal training and without clinical guidelines and tools to assist with decision-making. As new guidelines for virtual care provision take shape, it is critical that they are informed by an in-depth understanding of how providers make decisions about virtual care in their clinical practices. OBJECTIVE: In this paper, we sought to identify the most salient factors that influence how providers decide when to offer patients video appointments instead of or in conjunction with in-person care. METHODS: We conducted semistructured interviews with 28 purposefully selected primary and specialty health care providers from the US Department of Veteran's Affairs health care system. We used an inductive approach to identify factors that impact provider decision-making. RESULTS: Qualitative analysis revealed distinct clinical, patient, and provider factors that influence provider decisions to initiate or continue with virtual visits. Clinical factors include patient acuity, the need for additional tests or labs, changes in patients' health status, and whether the patient is new or has no recent visit. Patient factors include patients' ability to articulate symptoms or needs, availability and accessibility of technology, preferences for or against virtual visits, and access to caregiver assistance. Provider factors include provider comfort with and acceptance of virtual technology as well as virtual physical exam skills and training. CONCLUSIONS: Providers within the US Department of Veterans Affairs health administration system consider a complex set of factors when deciding whether to offer or continue a video or telephone visit. These factors can inform the development and further refinement of decision tools, guides, and other policies to ensure that virtual care expands access to high-quality care.

Digital Health Skillsets and Digital Preparedness: Comparison of Veterans Health Administration Users and Other Veterans Nationally.

BACKGROUND: As health care systems shift to greater use of telemedicine and digital tools, an individual's digital health literacy has become an important skillset. The Veterans Health Administration (VA) has invested resources in providing digital health care; however, to date, no study has compared the digital health skills and preparedness of veterans receiving care in the VA to veterans receiving care outside the VA. OBJECTIVE: The goal of the research was to describe digital health skills and preparedness among veterans who receive care within and outside the VA health care system and examine whether receiving care in the VA is associated with digital preparedness (reporting more than 2 digital health skills) after accounting for demographic and social risk factors. METHODS: We used cross-sectional data from the 2016-2018 National Health Interview Survey to identify veterans (aged over 18 years) who obtain health care either within or outside the VA health care system. We used multivariable logistic regression models to examine the association of sociodemographic (age, sex, race, ethnicity), social risk factors (economic instability, disadvantaged neighborhood, low educational attainment, and social isolation), and health care delivery location (VA and non-VA) with digital preparedness. RESULTS: Those who received health care within the VA health care system (n=3188) were younger (age 18-49 years: 33.3% [95% CI 30.7-36.0] vs 24.2% [95% CI 21.9-26.5], P<.01), were more often female (34.7% [95% CI 32.0-37.3] vs 6.6% [95% CI 5.5-7.6], P<.01) and identified as Black (13.1% [95% CI 11.2-15.0] vs 10.2% [95% CI 8.7-11.8], P<.01), and reported greater economic instability (8.3% [95% CI 6.9-9.8] vs 5.5% [95% CI 4.6-6.5], P<.01) and social isolation (42.6% [95% CI 40.3-44.9] vs 35.4% [95% CI 33.4-37.5], P<.01) compared to veterans who received care outside the VA (n=3393). Veterans who obtained care within the VA reported more digital health skills than those who obtained care outside the VA, endorsing greater rates of looking up health information on the internet (51.8% [95% CI 49.2-54.4] vs 45.0% [95% CI 42.6-47.3], P<.01), filling a prescription using the internet (16.2% [95% CI 14.5-18.0] vs 11.3% [95% CI 9.6-13.0], P<.01), scheduling a health care appointment on the internet (14.1% [95% CI 12.4-15.8] vs 11.6% [95% CI 10.1-13.1], P=.02), and communicating with a health care provider by email (18.0% [95% CI 16.1-19.8] vs 13.3% [95% CI 11.6-14.9], P<.01). Following adjustment for sociodemographic and social risk factors, receiving health care from the VA was the only characteristic associated with higher odds (adjusted odds ratio [aOR] 1.36, 95% CI 1.12-1.65) of being digitally prepared. CONCLUSIONS: Despite these demographic disadvantages to digital uptake, veterans who receive care in the VA reported more digital health skills and appear more digitally prepared than veterans who do not receive care within the VA, suggesting a positive, system-level influence on this cohort.

Association of Social and Behavioral Risk Factors With Mortality Among US Veterans With COVID-19.

Importance: The US Department of Veterans Affairs (VA) offers programs that reduce barriers to care for veterans and those with housing instability, poverty, and substance use disorder. In this setting, however, the role that social and behavioral risk factors play in COVID-19 outcomes is unclear. Objective: To examine whether social and behavioral risk factors were associated with mortality among US veterans with COVID-19 and whether this association might be modified by race/ethnicity. Design, Setting, and Participants: This cohort study obtained data from the VA Corporate Data Warehouse to form a cohort of veterans who received a positive COVID-19 test result between March 2 and September 30, 2020, in a VA health care facility. All veterans who met the inclusion criteria were eligible to participate in the study, and participants were followed up for 30 days after the first SARS-CoV-2 or COVID-19 diagnosis. The final follow-up date was October 31, 2020. Exposures: Social risk factors included housing problems and financial hardship. Behavioral risk factors included current tobacco use, alcohol use, and substance use. Main Outcomes and Measures: The primary outcome was all-cause mortality in the 30-day period after the SARS-CoV-2 or COVID-19 diagnosis date. Multivariable logistic regression was used to estimate odds ratios, clustering for health care facilities and adjusting for age, sex, race, ethnicity, marital status, clinical factors, and month of COVID-19 diagnosis. Results: Among 27 640 veterans with COVID-19 who were included in the analysis, 24 496 were men (88.6%) and the mean (SD) age was 57.2 (16.6) years. A total of 3090 veterans (11.2%) had housing problems, 4450 (16.1%) had financial hardship, 5358 (19.4%) used alcohol, and 3569 (12.9%) reported substance use. Hospitalization occurred in 7663 veterans (27.7%), and 1230 veterans (4.5%) died. Housing problems (adjusted odds ratio [AOR], 0.96; 95% CI, 0.77-1.19; P = .70), financial hardship (AOR, 1.13; 95% CI, 0.97-1.31; P = .11), alcohol use (AOR, 0.82; 95% CI, 0.68-1.01; P = .06), current tobacco use (AOR, 0.85; 95% CI, 0.68-1.06; P = .14), and substance use (AOR, 0.90; 95% CI, 0.71-1.15; P = .41) were not associated with higher mortality. Interaction analyses by race/ethnicity did not find associations between mortality and social and behavioral risk factors. Conclusions and Relevance: Results of this study showed that, in an integrated health system such as the VA, social and behavioral risk factors were not associated with mortality from COVID-19. Further research is needed to substantiate the potential of an integrated health system to be a model of support services for households with COVID-19 and populations who are at risk for the disease.