ABSTRACT
BACKGROUND: The COVID-19 pandemic highlighted the importance of telemedicine in health care. However, video telemedicine requires adequate broadband internet speeds. As video-based telemedicine grows, variations in broadband access must be accurately measured and characterized. OBJECTIVE: This study aims to compare the Federal Communications Commission (FCC) and Microsoft US broadband use data sources to measure county-level broadband access among veterans receiving mental health care from the Veterans Health Administration (VHA). METHODS: Retrospective observational cohort study using administrative data to identify mental health visits from January 1, 2019, to December 31, 2020, among 1161 VHA mental health clinics. The exposure is county-level broadband percentages calculated as the percentage of the county population with access to adequate broadband speeds (ie, download >25 megabits per second) as measured by the FCC and Microsoft. All veterans receiving VHA mental health services during the study period were included and categorized based on their use of video mental health visits. Broadband access was compared between and within data sources, stratified by video versus no video telemedicine use. RESULTS: Over the 2-year study period, 1,474,024 veterans with VHA mental health visits were identified. Average broadband percentages varied by source (FCC mean 91.3%, SD 12.5% vs Microsoft mean 48.2%, SD 18.1%; P<.001). Within each data source, broadband percentages generally increased from 2019 to 2020. Adjusted regression analyses estimated the change after pandemic onset versus before the pandemic in quarterly county-based mental health visit counts at prespecified broadband percentages. Using FCC model estimates, given all other covariates are constant and assuming an FCC percentage set at 70%, the incidence rate ratio (IRR) of county-level quarterly mental video visits during the COVID-19 pandemic was 6.81 times (95% CI 6.49-7.13) the rate before the pandemic. In comparison, the model using Microsoft data exhibited a stronger association (IRR 7.28; 95% CI 6.78-7.81). This relationship held across all broadband access levels assessed. CONCLUSIONS: This study found FCC broadband data estimated higher and less variable county-level broadband percentages compared to those estimated using Microsoft data. Regardless of the data source, veterans without mental health video visits lived in counties with lower broadband access, highlighting the need for accurate broadband speeds to prioritize infrastructure and intervention development based on the greatest community-level impacts. Future work should link broadband access to differences in clinical outcomes.
Subject(s)
COVID-19 , Mental Health Services , Telemedicine , Veterans , Humans , Retrospective Studies , Telemedicine/statistics & numerical data , United States , COVID-19/epidemiology , Veterans/statistics & numerical data , Mental Health Services/statistics & numerical data , United States Department of Veterans Affairs , Male , Internet Access/statistics & numerical data , Mental Health , Female , PandemicsSubject(s)
Internal Medicine , Periodicals as Topic , Social Media , Social Media/trends , Humans , Internal Medicine/methodsABSTRACT
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.
Subject(s)
Telemedicine , Veterans , United States , Humans , United States Department of Veterans Affairs , Delivery of Health Care , Health Services Research , Veterans HealthABSTRACT
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.
Subject(s)
Telemedicine , Veterans , Humans , Veterans Health , Surveys and Questionnaires , TabletsSubject(s)
Osteopathic Medicine , Physicians , Humans , United States , Health Personnel , Quality of Health CareABSTRACT
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.
Subject(s)
Health Services Accessibility , Insurance, Health , Adult , Humans , United States/epidemiology , Behavioral Risk Factor Surveillance System , Health Status , Logistic Models , Insurance Coverage , Medically UninsuredSubject(s)
American Indian or Alaska Native , Health Services Accessibility , Insurance, Health , Patient Satisfaction , Adult , Humans , American Indian or Alaska Native/statistics & numerical data , Health Services Accessibility/economics , Health Services Accessibility/statistics & numerical data , Indians, North American/statistics & numerical data , Personal Satisfaction , United States/epidemiology , Insurance, Health/economics , Insurance, Health/statistics & numerical data , Patient Satisfaction/economics , Patient Satisfaction/ethnology , Patient Satisfaction/statistics & numerical dataABSTRACT
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.
Subject(s)
Medicine , Veterans , Humans , United States , Veterans/psychology , Mental Health , Retrospective Studies , Delivery of Health Care , United States Department of Veterans Affairs , Veterans HealthABSTRACT
The use of emergency departments for non-emergent issues has led to overcrowding and decreased the quality of care. Telemedicine may be a mechanism to decrease overutilization of this expensive resource. From April to September 2020, we assessed (a) the impact of a multi-center tele-urgent care program on emergency department referral rates and (b) the proportion of individuals who had a subsequent emergency department visit within 72 h of tele-urgent care evaluation when they were not referred to the emergency department. We then performed a chart review to assess whether patients presented to the emergency department for the same reason as was stated for their tele-urgent care evaluation, whether subsequent hospitalization was needed during that emergency department visit, and whether death occurred. Among the 2510 patients who would have been referred to in-person emergency department care, but instead received tele-urgent care assessment, one in five (21%; n = 533) were subsequently referred to the emergency department. Among those not referred following tele-urgent care, 1 in 10 (11%; n = 162) visited the emergency department within 72 h. Among these 162 individuals, most (91%) returned with the same or similar complaint as what was assessed during their tele-urgent care visit, with one in five requiring hospitalization (19%, n = 31) with one individual (0.01%) dying. In conclusion, tele-urgent care may safely decrease emergency department utilization.