Purpose: We describe an approach to rapidly adapt and implement an education and skills improvement intervention to address the needs of family caregivers of functionally impaired veterans-Helping Invested Families Improve Veterans' Experience Study (HI-FIVES). Design: Prior to implementation in eight sites, a multidisciplinary study team made systematic adaptations to the curriculum content and delivery process using input from the original randomized controlled trial (RCT); a stakeholder advisory board comprised of national experts in caregiver education, nursing, and implementation; and a veteran/caregiver engagement panel. To address site-specific implementation barriers in diverse settings, we applied the Replicating Effective Programs implementation framework. Findings: Adaptations to HI-FIVES content and delivery included identifying core/noncore curriculum components, reducing instruction time, and simplifying caregiver recruitment for clinical settings. To enhance curriculum flexibility and potential uptake, site personnel were able to choose which staff would deliver the intervention and whether to offer class sessions in person or remotely. Curriculum materials were standardized and packaged to reduce the time required for implementation and to promote fidelity to the intervention.

BACKGROUND: Virtual care is critical to Veterans Health Administration (VHA) efforts to expand veterans' access to care. Health care policies such as the Veterans Access, Choice, and Accountability (CHOICE) Act and the Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act impact how the VHA provides care. Research on ways to refine virtual care delivery models to meet the needs of veterans, clinicians, and VHA stakeholders is needed. OBJECTIVE: Given the importance of virtual approaches for increasing access to high-quality VHA care, in December 2019, we convened a Think Tank, Accelerating Implementation of Virtual Care in VHA Practice, to consider challenges to virtual care research and practice across the VHA, discuss novel approaches to using and evaluating virtual care, assess perspectives on virtual care, and develop priorities to enhance virtual care in the VHA. METHODS: We used a participatory approach to develop potential priorities for virtual care research and activities at the VHA. We refined these priorities through forceranked prioritization and group discussion, and developed solutions for selected priorities. RESULTS: Think Tank attendees (n = 18) consisted of VHA stakeholders, including operations partners (e.g., Office of Rural Health, Office of Nursing Services, Health Services Research and Development), clinicians (e.g., physicians, nurses, psychologists, physician assistants), and health services researchers. We identified an initial list of fifteen potential priorities and narrowed these down to four. The four priorities were (1) scaling evidence-based practices, (2) centralizing virtual care, (3) creating highvalue care within the VHA with virtual care, and (4) identifying appropriate patients for virtual care. CONCLUSION: Our Think Tank took an important step in setting a partnered research agenda to optimize the use of virtual care within the VHA. We brought together research and operations stakeholders and identified possibilities, partnerships, and potential solutions for virtual care.

Background Most efforts to identify caregivers for research use passive approaches such as self-nomination. We describe an approach in which electronic health records (EHRs) can help identify, recruit, and increase diverse representations of family and other unpaid caregivers. Objective Few health systems have implemented systematic processes for identifying caregivers. This study aimed to develop and evaluate an EHR-driven process for identifying veterans likely to have unpaid caregivers in a caregiver survey study. We additionally examined whether there were EHR-derived veteran characteristics associated with veterans having unpaid caregivers. Methods We selected EHR home- and community-based referrals suggestive of veterans’ need for supportive care from friends or family. We identified veterans with these referrals across the 8 US Department of Veteran Affairs medical centers enrolled in our study. Phone calls to a subset of these veterans confirmed whether they had a caregiver, specifically an unpaid caregiver. We calculated the screening contact rate for unpaid caregivers of veterans using attempted phone screening and for those who completed phone screening. The veteran characteristics from the EHR were compared across referral and screening groups using descriptive statistics, and logistic regression was used to compare the likelihood of having an unpaid caregiver among veterans who completed phone screening. Results During the study period, our EHR-driven process identified 12,212 veterans with home- and community-based referrals; 2134 (17.47%) veteran households were called for phone screening. Among the 2134 veterans called, 1367 (64.06%) answered the call, and 813 (38.1%) veterans had a caregiver based on self-report of the veteran, their caregiver, or another person in the household. The unpaid caregiver identification rate was 38.1% and 59.5% among those with an attempted phone screening and completed phone screening, respectively. Veterans had increased odds of having an unpaid caregiver if they were married (adjusted odds ratio [OR] 2.69, 95% CI 1.68-4.34), had respite care (adjusted OR 2.17, 95% CI 1.41-3.41), or had adult day health care (adjusted OR 3.69, 95% CI 1.60-10.00). Veterans with a dementia diagnosis (adjusted OR 1.37, 95% CI 1.00-1.89) or veteran-directed care referral (adjusted OR 1.95, 95% CI 0.97-4.20) were also suggestive of an association with having an unpaid caregiver. Conclusions The EHR-driven process to identify veterans likely to have unpaid caregivers is systematic and resource intensive. Approximately 60% (813/1367) of veterans who were successfully screened had unpaid caregivers. In the absence of discrete fields in the EHR, our EHR-driven process can be used to identify unpaid caregivers; however, incorporating caregiver identification fields into the EHR would support a more efficient and systematic identification of caregivers. Trial Registration ClincalTrials.gov NCT03474380; https://clinicaltrials.gov/ct2/show/NCT03474380

Background Caregivers FIRST is an evidence-based program addressing gaps in caregivers’ skills. In 2020, the Veterans Health Administration Caregiver Support Program (CSP) nationally endorsed Caregivers FIRST, offering credit in leadership performance plans to encourage all VA medical centers (VAMCs) to implement locally. This study examines the association of organizational readiness with VAMC adoption of Caregivers FIRST. Methods In a cohort observational study, we surveyed CSP managers about their facilities’ readiness to implement using the Organizational Readiness for Implementing Change (ORIC) instrument and compared change commitment and change efficacy domains among VAMCs “adopters” defined as delivering Caregivers FIRST within 1 year of the national announcement to those that did not (“non-adopters”). Within “adopters,” we categorized time to adoption based on Rogers’ diffusion of innovation theory including “innovators,” “early adopters,” “early majority,” “late adopters,” and “laggards.” Organizational readiness and site characteristics (facility complexity, staffing levels, volume of applications for caregiver assistance services) were compared between “adopters,” “non-adopters,” and between time to adoption subcategories. Separate logistic regression models were used to assess whether ORIC and site characteristics were associated with early adoption among “adopters.” Results Fifty-one of 63 (81%) VAMCs with CSP manager survey respondents adopted Caregivers FIRST during the first year. ORIC change commitment and efficacy were similar for “adopters” and “non-adopters.” However, sites that adopted earlier (innovators and early adopters) had higher ORIC change commitment and efficacy scores than the rest of the “adopters.” Logistic regression results indicated that higher ORIC change commitment (odds ratio [OR] = 2.57; 95% confidence interval [CI], 1.11–5.95) and ORIC change efficacy (OR = 2.60; 95% CI, 1.12–6.03) scores were associated with increased odds that a VAMC was an early adopter (categorized as an “innovator,” “early adopter”, or “early majority”). Site-level characteristics were not associated with Caregivers FIRST early adoption. Conclusions To our knowledge, this study is the first to prospectively assess organizational readiness and the timing of subsequent program adoption. Early adoption was associated with higher ORIC change commitment and change efficacy and not site-level characteristics. These findings yield insights into the role of organizational readiness to accelerate program adoption. Trial registration ClinicalTrials.gov, NCT03474380. Registered on March 22, 2018

Critically needed programs designed to support family caregivers have shown inconsistent reductions in stress and burden. To explore drivers of improvement in caregiver outcomes after participation in a support intervention we analyzed data from a one-on-one, tailored problem-solving intervention targeting caregiver wellbeing (2015–2019, n = 503). We explored data patterns across 21 individual, household, and program-level variables using elastic net regression to identify drivers of improvements, and their relative importance. Baseline subjective burden, baseline depressive symptom scores, baseline caregiver problem solving, African American race, and site and coach fixed effects were the most consistent drivers of changes across the explored caregiver outcomes. Caregiver and program characteristics may be promising avenues to target to decrease distress and burden during intervention design. Interventions focusing on highly distressed caregivers may lead to greater improvements. More research is needed to identify how site or interventionists characteristics drive positive intervention effects.