Supporting the Implementation of Connected Care Technologies in the Veterans Health Administration: Cross-Sectional Survey Findings from the Veterans Engagement with Technology Collaborative (VET-C) Cohort

BACKGROUND: Widespread adoption, use, and integration of patient-facing technologies into the workflow of health care systems has been slow, thus limiting the realization of their potential. A growing body of work has focused on how best to promote adoption and use of these technologies and measure their impacts on processes of care and outcomes. This body of work currently suffers from limitations (eg, cross-sectional analyses, limited patient-generated data linked with clinical records) and would benefit from institutional infrastructure to enhance available data and integrate the voice of the patient into implementation and evaluation efforts. OBJECTIVE: The Veterans Health Administration (VHA) has launched an initiative called the Veterans Engagement with Technology Collaborative cohort to directly address these challenges. This paper reports the process by which the cohort was developed and describes the baseline data being collected from cohort members. The overarching goal of the Veterans Engagement with Technology Collaborative cohort is to directly engage veterans in the evaluation of new VHA patient-facing technologies and in so doing, to create new infrastructure to support related quality improvement and evaluation activities. METHODS: Inclusion criteria for veterans to be eligible for membership in the cohort included being an active user of VHA health care services, having a mobile phone, and being an established user of existing VHA patient-facing technologies as represented by use of the secure messaging feature of VHA\u27s patient portal. Between 2017 and 2018, we recruited veterans who met these criteria and administered a survey to them over the telephone. RESULTS: The majority of participants (N=2727) were male (2268/2727, 83.2%), White (2226/2727, 81.6%), living in their own apartment or house (2519/2696, 93.4%), and had completed some college (1176/2701, 43.5%) or an advanced degree (1178/2701, 43.6%). Cohort members were 59.9 years old, on average. The majority self-reported their health status as being good (1055/2725, 38.7%) or very good (524/2725, 19.2%). Most cohort members owned a personal computer (2609/2725, 95.7%), tablet computer (1616/2716, 59.5%), and/or smartphone (2438/2722, 89.6%). CONCLUSIONS: The Veterans Engagement with Technology Collaborative cohort is an example of a VHA learning health care system initiative designed to support the data-driven implementation of patient-facing technologies into practice and measurement of their impacts. With this initiative, VHA is building capacity for future, rapid, rigorous evaluation and quality improvement efforts to enhance understanding of the adoption, use, and impact of patient-facing technologies

The impact of mobile health (mhealth) technology on family caregiver's burden levels and an assessment of variations in mhealth tool use

Mobile health (mHealth) technologies exhibit promise for offering patients and their Caregivers point-of-need tools for health self-management. The goal of this research was to expand upon the limited body of knowledge that exists regarding mHealth by assessing the impact of a suite of mobile health applications (apps) on Veterans Administration (VA) Family Caregiver burden levels, and to identify factors and characteristics of Veterans and their caregivers that predict use of mHealth apps in this complex population. The research involved the dissemination of iPads® containing a suite of mHealth apps to Family Caregivers of Veterans who receive care at the VA and have serious physical or mental injuries resulting from the post-09/11 wars. Family Caregivers enrolled in VA's Comprehensive Assistance for Family Caregiver program (N=4501) were invited to participate in the VA's Family Caregiver Mobile Health Pilot program. The program distributed government-furnished iPads to participants (N=881) loaded with seven VA mHealth apps. The suite of apps was designed by the VA to assist the caregiver in managing Veteran PTSD and pain, provide support with healthcare-related tasks, and help Caregivers manage their own stress. This mobile health pilot program ran for three months and consisted of two studies. The Zarit Burden study was designed as a pretest-posttest non-random control study that involved an assessment of changes in Caregiver Zarit burden (4-question) scores experienced by the treatment group receiving the iPad /mHealth intervention, as compared with a control group who did not have the intervention. The mHealth Use study involved quantifying the use of the mHealth apps and predicting their use based on Veteran and Caregiver characteristics. A subset of the treatment group receiving the iPads completed three surveys assessing Caregiver Preparedness, Caregiver Traits, and Caregiver Zarit Burden Inventory baseline surveys. App use was modeled as a binary outcome (use vs. nonuse) using a multivariable logistic regression model, and also as a count outcome based on the frequency of use and modeled using a negative binomial model. In the Zarit Burden study, no statistically significant difference in Caregiver burden change scores could be detected between the treatment and control groups. This is consistent with other studies showing mixed results in demonstrating changes in Caregiver burden with a technological intervention. In the mHealth Use study, the most frequently used apps were those that provided access to VA health information and provided medication support. Initial use of the apps was positively associated with increasing computer skills, being a spouse, living in a rural location, and Veterans having a mental health diagnosis. Use of the apps was negatively associated with Caregiver preparedness and Veteran age. This mobile health Family Caregiver research study effectively established the VA's first patient-facing mobile health applications that are integrated within the VA data system

Successful operational integration of healthcare analytics at Seattle Children's

Abstract Introduction As the quantity and complexity of health data grows, it is critical for healthcare organizations to devise analytic strategies that power data innovation so they can take advantage of new opportunities and improve outcomes. Seattle Children's Healthcare System (Seattle Children's) is an example of an organization that has built an operating model that integrates analytics into their business and daily operations. We present a roadmap for how Seattle Children's consolidated its fragmented analytics operations into a unified cohesive ecosystem capable of supporting advanced analytics capabilities and operational integration to transform care and accelerate research. Methods In‐depth interviews were conducted with ten leaders at Seattle Children's who have been instrumental in developing their enterprise analytics program. Interviews included the following leadership roles: Chief Data & Analytics Officer, Director of Research Informatics, Principal Systems Architect, Manager of Bioinformatics and High Throughput Analytics, Director of Neurocritical Care, Strategic Program Manager & Neuron Product Development Lead, Director of Dev Ops,Director of Clinical Analytics, Data Science Manager, and Advance Analytics Product Engineer. The interviews were unstructured and consisted of conversations intended to gather information from leadership about their experiences in building out Enterprise Analytics at Seattle Children's. Results Seattle Children's has built an advanced enterprise analytics ecosystem that is integrated into its daily operations by applying an entrepreneurial mindset and agile development practices that are common in a startup environment. Analytics efforts were approached iteratively by selecting high‐value projects that were delivered through Multidisciplinary Delivery Teams that were integrated into service lines. Service line leadership, in partnership with the Delivery Team leads, were responsible for the success of the team by setting project priorities, determining project budgets, and maintaining overall governance of their analytics endeavors. This organizational structure has led to the development of a wide range of analytic products that have been used to improve both operations and clinical care at Seattle Children's. Conclusions Seattle Children's has demonstrated how a leading healthcare system can successfully create a robust, scalable, near real‐time analytics ecosystem‐ one that delivers significant value to the organization from the ever‐expanding volume of health data we see today

Health-Related Goal Setting and Achievement Among Veterans with High Technology Adoption

BACKGROUND: There is increasing recognition of the importance of supporting patients in their health-related goals. Patient-provider discussions and health-related mobile applications (apps) can support patients to pursue health goals; however, their impact on patient goal setting and achievement is not well understood. OBJECTIVE: To examine the relationships between the following: (1) patient demographics, patient-provider discussions, and health-related goal setting and achievement, and (2) patient mobile health app use and goal achievement. DESIGN: Cross-sectional survey. PARTICIPANTS: Veterans who receive Veterans Health Administration (VA) healthcare and are users of VA patient-facing technology. MAIN MEASURES: Veteran demographics, goal-related behaviors, and goal achievement. METHODS: Veterans were invited to participate in a telephone survey. VA administrative data were linked to survey data for additional health and demographic information. Logistic regression models were run to identify factors that predict health-related goal setting and achievement. KEY RESULTS: Among respondents (n=2552), 75% of patients indicated having set health goals in the preceding 6 months and approximately 42% reported achieving their goal. Men (vs. women) had lower odds of setting goals (OR: 0.71; CI95: 0.53-0.97), as did individuals with worse (vs. better) health (OR: 0.18; CI95: 0.04-0.88). Individuals with advanced education-some college/college degrees, and post-college degrees (vs. no college education)-demonstrated higher odds of setting goals (OR: 1.35; CI95: 1.01-1.79; OR: 1.71; CI95: 1.28-2.28, respectively). Those who reported having discussed their goals with their providers were more likely to set goals (OR: 3.60; CI95: 2.97-4.35). Patient mobile health app use was not statistically associated with goal achievement. CONCLUSIONS: Efforts to further promote patient-led goal setting should leverage the influence of patient-provider conversations. Use of patient-facing technologies, specifically mobile health apps, may facilitate goal-oriented care, but further work is needed to examine the potential benefits of apps to support patient goals, particularly if providers discuss and endorse use of those apps with patients