Development and Validation of a Measure to Assess Patient Experiences With Video Care Encounters.

Importance: As video-based care expands in many clinical settings, assessing patient experiences with this care modality will help optimize health care quality, safety, and communication. Objective: To develop and assess the psychometric properties of the video visit user experience (VVUE) measure, a patient-reported measure of experiences with video-based technology. Design, Setting, and Participants: In this survey study, veterans completed a web-based, mail, or telephone survey about their use of Veterans Healthcare Administration (VHA) virtual care between September 2021 and January 2022. The survey was completed by patients who reported having a VHA video visit on their own device or a VHA-issued device and linked to VHA utilization data for the 6 months following the survey. Data analysis was performed from March 2022 to February 2023. Main Outcomes and Measures: The survey included 19 items about experiences with video visits that were rated using a 4-point Likert-type scale (strongly disagree to strongly agree). First, an exploratory factor analysis was conducted to determine the factor structure and parsimonious set of items, using the McDonald Omega test to assess internal consistency reliability. Then, a confirmatory factor analysis was conducted to test structural validity, and bivariate correlations between VVUE and VHA health care engagement were calculated to test concurrent validity. Finally, predictive validity was assessed using logistic regression to determine whether VVUE was associated with future VHA video visit use. Results: Among 1887 respondents included in the analyses, 83.2% (95% CI, 81.5%-84.8%) were male, 41.0% (95% CI, 38.8%-43.1%) were aged 65 years or older, and the majority had multiple chronic medical and mental health conditions. The exploratory factor analysis identified a 10-item single-factor VVUE measure (including questions about satisfaction, user-centeredness, technical quality, usefulness, and appropriateness), explaining 96% of the total variance, with acceptable internal consistency reliability (ω = 0.95). The confirmatory factor analysis results confirmed a single-factor solution (standardized root mean squared residual = 0.04). VVUE was positively associated with VHA health care engagement (ρ = 0.47; P < .001). Predictive validity models demonstrated that higher VVUE measure scores were associated with future use of video visits, where each 1-point increase on VVUE was associated with greater likelihood of having a video visit in subsequent 6 months (adjusted odds ratio, 1.04; 95% CI, 1.02-1.06). Conclusions and Relevance: The findings of this study of veterans using video visits suggest that a brief measure is valid to capture veterans' experiences receiving VHA virtual care.

Patterns and predictors of mental health service use among older veterans with alcohol use disorder who received a video-enabled tablet.

Objectives: Video-based telehealth may expand access to mental health services among older veterans with alcohol use disorder (AUD). We examined the modalities through which mental health services were rendered, and predictors of video visits before and after video-enabled tablet receipt from the Veterans Health Administration. Method: 11,210 veterans aged 60 or older with a diagnosis of AUD who received a tablet between 1 April 2020 and 25 October 2021 were identified. The electronic health record was used to characterized encounters by modality of mental health care delivery in the six months pre/post tablet receipt. Logistic regression examined predictors of a video visit for mental health. Results: Phone was the most common modality; however, the proportion of video encounters increased from 8.7% to 26.2% after tablet receipt. Individuals who were older, male, and had more physical health conditions, were less likely to have a video visit. Individuals who were married, resided in urban areas, had a history of housing instability, and had more mental health conditions, were more likely to have a video visit. Conclusion: Video-enabled tablets may help older adults with AUD overcome access barriers to mental health services, although targeted support for certain groups may be necessary.

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.

Association between patient-reported social and behavioral risks and health care costs in high-risk Veterans health administration patients.

OBJECTIVE: Social risks complicate patients' ability to manage their conditions and access healthcare, but their association with health expenditures is not well established. To identify patient-reported social risk, behavioral, and health factors associated with health expenditures in Veterans Affairs (VA) patients at high risk for hospitalization or death. DATA SOURCES, STUDY SETTING, AND STUDY DESIGN: Prospective cohort study among high-risk Veterans obtaining VA care. Patient-reported social risk, function, and other measures derived from a 2018 survey sent to 10,000 VA patients were linked to clinical and demographic characteristics extracted from VA data. Response-weighted generalized linear and marginalized two-part models were used to examine VA expenditures (total, outpatient, medication, inpatient) 1 year after survey completion in adjusted models. PRINCIPAL FINDINGS: Among 4680 survey respondents, the average age was 70.9 years, 6.3% were female, 16.7% were African American, 20% had body mass index ≥35, 42.4% had difficulty with two or more basic or instrumental activities of daily living, 19.3% reported transportation barriers, 12.5% reported medication insecurity and 21.8% reported food insecurity. Medication insecurity was associated with lower outpatient expenditures (-$1859.51 per patient per year, 95% confidence interval [CI]: -3200.77 to -518.25) and lower total expenditures (-$4304.99 per patient per year, 95% CI: -7564.87 to -1045.10). Transportation barriers were negatively associated with medication expenditures (-$558.42, 95% CI: -1087.93 to -31.91). Patients with one functional impairment had higher outpatient expenditures ($2997.59 per patient year, 95% CI: 1185.81-4809.36) than patients without functional impairments. No social risks were associated with inpatient expenditures. CONCLUSIONS: In this study of VA patients at high risk for hospitalization and mortality, few social and functional measures were independently associated with the costs of VA care. Individuals with functional limitations and those with barriers to accessing medications and transportation may benefit from targeted interventions to ensure that they are receiving the services that they need.

Associations Between Social Risks and Primary Care Utilization Among Medically Complex Veterans.

BACKGROUND: Social risks contribute to poor health outcomes, especially for patients with complex medical needs. These same risks may impact access to primary care services. OBJECTIVE: To study associations between social risks and primary care utilization among patients with medical complexity. DESIGN: Prospective cohort study of respondents to a 2018 mailed survey, followed up to 2 years after survey completion. PARTICIPANTS: Nationally representative sample of 10,000 primary care patients in the Veterans Affairs (VA) health care system, with high (≥ 75th percentile) 1-year risk of hospitalization or death. MAIN MEASURES: Survey-based exposures were low social support, no family member/friend involved in health care, unemployment, transportation problem, food insecurity, medication insecurity, financial strain, low medical literacy, and less than high school graduate. Electronic health record-based outcomes were number of primary care provider (PCP) encounters, number of primary care team encounters (PCP, nurse, clinical pharmacist, and social worker), and having ≥ 1 social work encounter. KEY RESULTS: Among 4680 respondents, mean age was 70.3, 93.7% were male, 71.8% White non-Hispanic, and 15.8% Black non-Hispanic. Unemployment was associated with fewer PCP and primary care team encounters (incident rate ratio 0.77, 95% CI 0.65-0.91; p = 0.002 and 0.75, 0.59-0.95; p = 0.02, respectively), and low medical literacy was associated with more primary care team encounters (1.17, 1.05-1.32; p = 0.006). Among those with one or more social risks, 18.4% had ≥ 1 social work encounter. Low medical literacy (OR 1.95, 95% CI 1.45-2.61; p < 0.001), transportation problem (1.42, 1.10-1.83; p = 0.007), and low social support (1.31, 1.06-1.63; p = 0.01) were associated with higher odds of  ≥ 1 social work encounter. CONCLUSIONS: We found few differences in PCP and primary care team utilization among medically complex VA patients by social risk. However, social work use was low, despite its central role in addressing social risks. More work is needed to understand barriers to social work utilization.

Distinct health care use patterns of patients with chronic gastrointestinal diseases.

OBJECTIVES: Patients with complex chronic conditions have varying multidisciplinary care needs and utilization patterns, which limit the effectiveness of initiatives designed to improve continuity of care (COC) and reduce utilization. Our objective was to categorize patients with complex chronic conditions into distinct groups by pattern of outpatient care use and COC to tailor interventions. STUDY DESIGN: Observational cohort study from 2014 to 2015. METHODS: We identified patients whose 1-year hospitalization risk was in at least the 90th percentile in 2014 who had a chronic gastrointestinal disease (cirrhosis, inflammatory bowel disease, chronic pancreatitis) as case examples of complex chronic disease. We described frequency of office visits, number of outpatient providers, and 2 COC measures (usual provider of care, Bice-Boxerman COC indices) over 12 months. We used latent profile analysis, a statistical method for identifying distinct subgroups, to categorize patients based on overall, primary care, gastroenterology, and mental health continuity patterns. RESULTS: The 26,751 veterans in the cohort had a mean (SD) of 13.3 (8.6) office visits and 7.2 (3.8) providers in 2014. Patients were classified into 5 subgroups: (1) high gastroenterology-specific COC with mental health use; (2) high gastroenterology-specific COC without mental health use; (3) high overall utilization with mental health use; (4) low overall COC with mental health use; and (5) low overall COC without mental health use. These groups varied in their sociodemographic characteristics and risk for hospitalization, emergency department use, and mortality. CONCLUSIONS: Patients at high risk for health care utilization with specialty care needs can be grouped by varying propensity for health care continuity patterns.

Association between patient-reported social, behavioral, and health factors and emergency department visits in high-risk VA patients.

RESEARCH OBJECTIVE: To identify patient-reported social risk, behavioral, and health factors associated with emergency department (ED) utilization in high-risk Veterans Affairs (VA) patients. DATA SOURCES: Patient survey, VA, Medicare data. STUDY DESIGN: Prospective cohort study using multivariable logistic regression to identify patient-reported factors associated with all-cause and ambulatory care sensitive condition (ACSC)-related ED visits among VA patients at high risk for hospitalization or death. DATA EXTRACTION METHODS: Patient-reported measures derived from a 2018 survey sent to 10,000 VA patients; clinical and demographic characteristics derived from VA data; ED visits derived from VA and Medicare claims. PRINCIPAL FINDINGS: Among 4680 survey respondents, 52.5% and 16.3% experienced an all-cause or ACSC-related ED visit in the following year, respectively. An ED visit was more likely among individuals with functional status limitations (6.0% points (Confidence Interval [CI] 0.017-0.103)) and transportation barriers (5.2% points [CI 0.005-0.099]). An ACSC-related ED visit was more likely among individuals with functional status limitations (3.2% points [CI 0.003-0.062]) and self-rated poorer health (7.4% points (CI 0.030-0.119) poor; 6.2% points (CI 0.029-0.096) fair; 4.1% points (CI 0.009-0.073) good; compared with excellent/very good). CONCLUSIONS: Patient-reported factors not present in most electronic health records were significantly associated with future ED visits in high-risk VA patients.

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.

Disparities in virtual cardiology visits among Veterans Health Administration patients during the COVID-19 pandemic.

Objective: In response to the coronavirus disease 2019 (COVID-19) pandemic, the Veterans Health Administration (VA) rapidly expanded virtual care (defined as care delivered by video and phone), raising concerns about technology access disparities (ie, the digital divide). Virtual care was somewhat established in primary care and mental health care prepandemic, but video telehealth implementation was new for most subspecialties, including cardiology. We sought to identify patient characteristics of virtual and video-based care users in VA cardiology clinics nationally during the first year of the COVID-19 pandemic. Materials and Methods: Cohort study of Veteran patients across all VA facilities with a cardiology visit January 1, 2019-March 10, 2020, with follow-up January 1, 2019-March 10, 2021. Main measures included cardiology visits by visit type and likelihood of receiving cardiology-related virtual care, calculated with a repeated event survival model. Results: 416 587 Veterans with 1 689 595 total cardiology visits were analyzed; average patient age was 69.6 years and 4.3% were female. Virtual cardiology care expanded dramatically early in the COVID-19 pandemic from 5% to 70% of encounters. Older, lower-income, and rural-dwelling Veterans and those experiencing homelessness were less likely to use video care (adjusted hazard ratio for ages 75 and older 0.80, 95% confidence interval (CI) 0.75-0.86; for highly rural residents 0.77, 95% CI 0.68-0.87; for low-income status 0.94, 95% CI 0.89-0.98; for homeless Veterans 0.85, 95% CI 0.80-0.92). Conclusion: The pandemic worsened the digital divide for cardiology care for many vulnerable patients to the extent that video visits represent added value over phone visits. Targeted interventions may be necessary for equity in COVID-19-era access to virtual cardiology care.

Outpatient Care Fragmentation and Acute Care Utilization in Veterans Affairs Home-Based Primary Care.

Importance: Veterans Affairs (VA) Home-Based Primary Care (HBPC) provides comprehensive, interdisciplinary primary care at home to patients with complex, chronic, disabling disease, but little is known about care fragmentation patterns and consequences among these patients. Objective: To examine outpatient care fragmentation patterns and subsequent acute care among HBPC-engaged patients at high risk of hospitalization or death. Design, Setting, and Participants: This retrospective cohort study included VA patients aged at least 65 years who were enrolled in the VA and Medicare, whose risk of hospitalization or death was in the top 10%, and who had at least 4 outpatient visits between October 1, 2013, and September 30, 2014. HBPC engagement was defined as having at least 2 HBPC encounters between July 1, 2014, and September 30, 2014. Data were analyzed from March 2020 to March 2022. Exposures: Two indices of outpatient care fragmentation: practitioner count and the Usual Provider Continuity Index (UPC), based on VA and non-VA health care use from October 1, 2013, to September 30, 2014. All care delivered by HBPC clinicians was analyzed as coming from a single practitioner. Main Outcomes and Measures: Emergency department (ED) visits and hospitalizations for ambulatory care sensitive conditions (ACSC) from VA records and Medicare claims from October 1, 2014, to September 30, 2015. Results: Among 8908 identified HBPC patients, 8606 (96.6%) were male, 1562 (17.5%) were Black, 249 (2.8%) were Hispanic, 6499 (73.0%) were White, 157 (1.8%) were other race or ethnicity, and 441 (5.0%) had unknown race or ethnicity; the mean (SD) age was 80.0 (9.02) years; patients had a mean (SD) of 11.25 (3.87) chronic conditions, and commonly had disabling conditions such as dementia (38.8% [n = 3457]). In adjusted models, a greater number of practitioners was associated with increased odds of an ED visit (adjusted odds ratio [aOR], 1.05 [95% CI, 1.03-1.07]) and hospitalization for an ACSC (aOR, 1.04 [95% CI, 1.02-1.06]), whereas more concentrated care with a higher UPC was associated with reduced odds of these outcomes (highest vs lowest tertile of UPC: aOR for ED visit, 0.77 [95% CI, 0.67-0.88], aOR for ACSC hospitalization, 0.78 [95% CI, 0.68-0.88]). Conclusions and Relevance: Among patients in HBPC, fragmented care was associated with more ED visits and ACSC hospitalizations. These findings suggest that consolidating or coordinating fragmented care may be a target for reducing preventable acute care.

Association Between Mental Health Conditions and Outpatient Care Fragmentation: a National Study of Older High-Risk Veterans.

BACKGROUND: Healthcare fragmentation may lead to adverse consequences and may be amplified among older, sicker patients with mental health (MH) conditions. OBJECTIVE: To determine whether older Veterans with MH conditions have more fragmented outpatient non-MH care, compared with older Veterans with no MH conditions. DESIGN: Retrospective cohort study using FY2014 Veterans Health Administration (VHA) administrative data linked to Medicare data. PARTICIPANTS: 125,481 VHA patients ≥ 65 years old who were continuously enrolled in Medicare Fee-for-Service Parts A and B and were at high risk for hospitalization. MAIN OUTCOME AND MEASURES: The main outcome was non-MH care fragmentation as measured by (1) non-MH provider count and (2) Usual Provider of Care (UPC), the proportion of care with the most frequently seen non-MH provider. We tested the association between no vs. any MH conditions and outcomes using Poisson regression and fractional regression with logit link, respectively. We also compared Veterans with no MH condition with each MH condition and combinations of MH conditions, adjusting for sociodemographics, comorbidities, and drive-time to VHA specialty care. KEY RESULTS: In total, 47.3% had at least one MH condition. Compared to those without MH conditions, Veterans with MH conditions had less fragmented care, with fewer non-MH providers (IRR = 0.96; 95% CI: 0.96-0.96) and more concentrated care with their usual provider (OR = 1.08 for a higher UPC; 95% CI: 1.07, 1.09) in adjusted models. Secondary analyses showed that those with individual MH conditions (e.g., depression) had fewer non-MH providers (IRR range: 0.86-0.98) and more concentrated care (OR range: 1.04-1.20). A similar pattern was observed when examining combinations of MH conditions (IRR range: 0.80-0.90; OR range: 1.16-1.30). CONCLUSIONS: Contrary to expectations, having a MH condition was associated with less fragmented non-MH care among older, high-risk Veterans. Further research will determine if this is due to different needs, underuse, or appropriate use of healthcare.

Anticipating VA/non-VA care coordination demand for Veterans at high risk for hospitalization.

ABSTRACT: U.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination.

Outpatient care fragmentation in Veterans Affairs patients at high-risk for hospitalization.

OBJECTIVE: To examine outpatient care fragmentation and its association with future hospitalization among patients at high risk for hospitalization. DATA SOURCES: Veterans Affairs (VA) and Medicare data. STUDY DESIGN: We conducted a longitudinal study, using logistic regression to examine how outpatient care fragmentation in FY14 (as measured by number of unique providers, Breslau's Usual Provider of Care (UPC), Bice-Boxerman's Continuity of Care Index (COCI), and Modified Modified Continuity Index (MMCI)) was associated with all-cause hospitalizations and hospitalizations related to ambulatory care sensitive conditions (ACSC) in FY15. We also examined how fragmentation varied by patient's age, gender, race, ethnicity, marital status, rural status, history of homelessness, number of chronic conditions, Medicare utilization, and mental health care utilization. DATA EXTRACTION METHODS: We extracted data for 130,704 VA patients ≥65 years old with a hospitalization risk ≥90th percentile and ≥ four outpatient visits in the baseline year. PRINCIPAL FINDINGS: The mean (SD) of FY14 outpatient visits was 13.2 (8.6). Fragmented care (more providers, less care with a usual provider, more dispersed care based on COCI) was more common among patients with more chronic conditions and those receiving mental health care. In adjusted models, most fragmentation measures were not associated with all-cause hospitalization, and patients with low levels of fragmentation (more concentrated care based on UPC, COCI, and MMCI) had a higher likelihood of an ACSC-related hospitalization (AOR, 95% CI = 1.21 (1.09-1.35), 1.27 (1.14-1.42), and 1.28 (1.18-1.40), respectively). CONCLUSIONS: Contrary to expectations, outpatient care fragmentation was not associated with elevated all-cause hospitalization rates among VA patients in the top 10th percentile for risk of admission; in fact, fragmented care was linked to lower rates of hospitalization for ACSCs. In integrated settings such as the VA, multiple providers, and dispersed care might offer access to timely or specialized care that offsets risks of fragmentation, particularly for conditions that are sensitive to ambulatory care.

Organizational and External Factors Associated with Video Telehealth Use in the Veterans Health Administration Before and During the COVID-19 Pandemic.

Objectives: To identify organizational and external factors associated with medical center video telehealth uptake (i.e., the proportion of patients using telemedicine) before and early in the coronavirus disease 2019 (COVID-19) pandemic. Materials and Methods: We conducted a retrospective, observational study using cross-sectional data for all 139 U.S. Veterans Affairs Medical Centers (VAMCs). We used logistic regression analyses to identify factors that predicted whether a VAMC was in the top quartile of VA Video Connect (VVC) telehealth uptake for primary care and mental health care. Results: All 139 VAMCs increased their VVC uptake at least 2-fold early in the pandemic, with most increasing uptake between 5- and 10-fold. Pre-COVID-19, higher VVC uptake in primary care was weakly and positively associated with having more high-risk patients, negatively associated with having more long-distance patients, and positively associated with the prior fiscal year's VVC uptake. During COVID-19, the positive association with high-risk patients and the negative association with long-distance patients strengthened, while weaker broadband coverage was negatively associated with VVC uptake. For mental health care, having more long-distance patients was positively associated with higher VVC uptake pre-COVID-19, but this relationship reversed during COVID-19. Discussion: Despite the marked increase in VVC uptake early in the COVID-19 pandemic, significant VAMC-level variation indicates that VVC adoption was more difficult for some medical centers, particularly those with poorer broadband coverage and less prior VVC experience. Conclusions and Relevance: These findings highlight opportunities for medical centers, VA Central Office, and other federal entities to ensure equitable access to video telehealth.

Improving Prediction of Long-Term Care Utilization Through Patient-Reported Measures: Cross-Sectional Analysis of High-Need U.S. Veterans Affairs Patients.

This article examines the relative merit of augmenting an electronic health record (EHR)-derived predictive model of institutional long-term care (LTC) use with patient-reported measures not commonly found in EHRs. We used survey and administrative data from 3,478 high-risk Veterans aged ≥65 in the U.S. Department of Veterans Affairs, comparing a model based on a Veterans Health Administration (VA) geriatrics dashboard, a model with additional EHR-derived variables, and a model that added survey-based measures (i.e., activities of daily living [ADL] limitations, social support, and finances). Model performance was assessed via Akaike information criteria, C-statistics, sensitivity, and specificity. Age, a dementia diagnosis, Nosos risk score, social support, and ADL limitations were consistent predictors of institutional LTC use. Survey-based variables significantly improved model performance. Although demographic and clinical characteristics found in many EHRs are predictive of institutional LTC, patient-reported function and partnership status improve identification of patients who may benefit from home- and community-based services.

Subgroups of High-Risk Veterans Affairs Patients Based on Social Determinants of Health Predict Risk of Future Hospitalization.

OBJECTIVE: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.

The association of alcohol use with all-cause and cardiovascular disease-related hospitalizations or death in older, high-risk Veterans.

BACKGROUND: The prevalence of alcohol misuse among older adults has grown dramatically in the past decade, yet little is known about the association of alcohol misuse with hospitalization and death in this patient population. METHODS: We examined the association between alcohol use (measured by a screening instrument in primary care) and rates of all-cause and cardiovascular disease (CVD)-related 6-month hospitalization or death via electronic health records (EHRs) in a nationally representative sample of older, high-risk Veterans. Models were adjusted for sociodemographic and clinical characteristics, including frailty and comorbid conditions. RESULTS: The all-cause hospitalization or death rate at 6 months was 14.9%, and the CVD-related hospitalization or death rate was 1.8%. In adjusted analyses, all-cause hospitalization or death was higher in older Veterans who were nondrinkers or harmful use drinkers compared to moderate use drinkers, but CVD-related hospitalization or death was similar in all categories of drinking. CONCLUSIONS: These findings suggest that the complex association between alcohol and all-cause acute healthcare utilization found in the broader population is similar in older, high-risk Veteran patients. These findings do not support an association between alcohol consumption and CVD-specific hospitalizations.

Virtual care expansion in the Veterans Health Administration during the COVID-19 pandemic: clinical services and patient characteristics associated with utilization.

OBJECTIVES: To describe the shift from in-person to virtual care within Veterans Affairs (VA) during the early phase of the COVID-19 pandemic and to identify at-risk patient populations who require greater resources to overcome access barriers to virtual care. MATERIALS AND METHODS: Outpatient encounters (N = 42 916 349) were categorized by care type (eg, primary, mental health, etc) and delivery method (eg, in-person, video). For 5 400 878 Veterans, we used generalized linear models to identify patient sociodemographic and clinical characteristics associated with: 1) use of virtual (phone or video) care versus no virtual care and 2) use of video care versus no video care between March 11, 2020 and June 6, 2020. RESULTS: By June, 58% of VA care was provided virtually compared to only 14% prior. Patients with lower income, higher disability, and more chronic conditions were more likely to receive virtual care during the pandemic. Yet, Veterans aged 45-64 and 65+ were less likely to use video care compared to those aged 18-44 (aRR 0.80 [95% confidence interval (CI) 0.79, 0.82] and 0.50 [95% CI 0.48, 0.52], respectively). Rural and homeless Veterans were 12% and 11% less likely to use video care compared to urban (0.88 [95% CI 0.86, 0.90]) and nonhomeless Veterans (0.89 [95% CI 0.86, 0.92]). DISCUSSION: Veterans with high clinical or social need had higher likelihood of virtual service use early in the COVID-19 pandemic; however, older, homeless, and rural Veterans were less likely to have video visits, raising concerns for access barriers. CONCLUSIONS AND RELEVANCE: While virtual care may expand access, access barriers must be addressed to avoid exacerbating disparities.

Patient-Reported Social and Behavioral Determinants of Health and Estimated Risk of Hospitalization in High-Risk Veterans Affairs Patients.

Importance: Despite recognition of the association between individual social and behavioral determinants of health (SDH) and patient outcomes, little is known regarding the value of SDH in explaining variation in outcomes for high-risk patients. Objective: To describe SDH factors among veterans who are at high risk for hospitalization, and to determine whether adding patient-reported SDH measures to electronic health record (EHR) measures improves estimation of 90-day and 180-day all-cause hospital admission. Design, Setting, and Participants: A survey was mailed between April 16 and June 29, 2018, to a nationally representative sample of 10 000 Veterans Affairs (VA) patients whose 1-year risk of hospitalization or death was in the 75th percentile or higher based on a VA EHR-derived risk score. The survey included multiple SDH measures, such as resilience, social support, health literacy, smoking status, transportation barriers, and recent life stressors. Main Outcomes and Measures: The EHR-based characteristics of survey respondents and nonrespondents were compared using standardized differences. Estimation of 90-day and 180-day hospital admission risk was assessed for 3 logistic regression models: (1) a base model of all prespecified EHR-based covariates, (2) a restricted model of EHR-based covariates chosen via forward selection based on minimizing Akaike information criterion (AIC), and (3) a model of EHR- and survey-based covariates chosen via forward selection based on AIC minimization. Results: In total, 4685 individuals (response rate 46.9%) responded to the survey. Respondents were comparable to nonrespondents in most characteristics, but survey respondents were older (eg, >80 years old, 881 [18.8%] vs 800 [15.1%]), comprised a higher percentage of men (4391 [93.7%] vs 4794 [90.2%]), and were composed of more White non-Hispanic individuals (3366 [71.8%] vs 3259 [61.3%]). Based on AIC, the regression model with survey-based covariates and EHR-based covariates better estimated hospital admission at 90 days (AIC, 1947.7) and 180 days (AIC, 2951.9) than restricted models with only EHR-based covariates (AIC, 1980.2 at 90 days; AIC, 2981.9 at 180 days). This result was due to inclusion of self-reported measures such as marital or partner status, health-related locus of control, resilience, smoking status, health literacy, and medication insecurity. Conclusions and Relevance: Augmenting EHR data with patient-reported social information improved estimation of 90-day and 180-day hospitalization risk, highlighting specific SDH factors that might identify individuals who are at high risk for hospitalization.

Identification of Emergency Care-Sensitive Conditions and Characteristics of Emergency Department Utilization.

Importance: Monitoring emergency care quality requires understanding which conditions benefit most from timely, quality emergency care. Objectives: To identify a set of emergency care-sensitive conditions (ECSCs) that are treated in most emergency departments (EDs), are associated with a spectrum of adult age groups, and represent common reasons for seeking emergency care and to provide benchmark national estimates of ECSC acute care utilization. Design, Setting, and Participants: A modified Delphi method was used to identify ECSCs. In a cross-sectional analysis, ECSC-associated visits by adults (aged ≥18 years) were identified based on International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes and analyzed with nationally representative data from the 2016 US Nationwide Emergency Department Sample. Data analysis was conducted from January 2018 to December 2018. Main Outcomes and Measures: Identification of ECSCs and ECSC-associated ED utilization patterns, length of stay, and charges. Results: An expert panel rated 51 condition groups as emergency care sensitive. Emergency care-sensitive conditions represented 16 033 359 of 114 323 044 ED visits (14.0%) in 2016. On average, 8 535 261 of 17 886 220 ED admissions (47.7%) were attributed to ECSCs. The most common ECSC ED visits were for sepsis (1 716 004 [10.7%]), chronic obstructive pulmonary disease (1 273 319 [7.9%]), pneumonia (1 263 971 [7.9%]), asthma (970 829 [6.1%]), and heart failure (911 602 [5.7%]) but varied by age group. Median (interquartile range) length of stay for ECSC ED admissions was longer than non-ECSC ED admissions (3.2 [1.7-5.8] days vs 2.7 [1.4-4.9] days; P < .001). In 2016, median (interquartile range) ED charges per visit for ECSCs were $2736 ($1684-$4605) compared with $2179 ($1118-$4359) per visit for non-ECSC ED visits (P < .001). Conclusions and Relevance: This comprehensive list of ECSCs can be used to guide indicator development for pre-ED, intra-ED, and post-ED care and overall assessment of the adult, non-mental health, acute care system. Health care utilization and costs among patients with ECSCs are substantial and warrant future study of validation, variations in care, and outcomes associated with ECSCs.