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.

Veteran Experiences With an mHealth App to Support Measurement-Based Mental Health Care: Results From a Mixed Methods Evaluation.

BACKGROUND: Mental health conditions are highly prevalent among US veterans. The Veterans Health Administration (VHA) is committed to enhancing mental health care through the integration of measurement-based care (MBC) practices, guided by its Collect-Share-Act model. Incorporating the use of remote mobile apps may further support the implementation of MBC for mental health care. OBJECTIVE: This study aims to evaluate veteran experiences with Mental Health Checkup (MHC), a VHA mobile app to support remote MBC for mental health. METHODS: Our mixed methods sequential explanatory evaluation encompassed mailed surveys with veterans who used MHC and follow-up semistructured interviews with a subset of survey respondents. We analyzed survey data using descriptive statistics. We then compared responses between veterans who indicated having used MHC for ≥3 versus <3 months using χ2 tests. We analyzed interview data using thematic analysis. RESULTS: We received 533 surveys (533/2631, for a 20% response rate) and completed 20 interviews. Findings from these data supported one another and highlighted 4 key themes. (1) The MHC app had positive impacts on care processes for veterans: a majority of MHC users overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed or strongly agreed that using MHC helped them be more engaged in their health and health care (169/262, 65%), make decisions about their treatment (157/262, 60%), and set goals related to their health and health care (156/262, 60%). Similarly, interviewees described that visualizing progress through graphs of their assessment data over time motivated them to continue therapy and increased self-awareness. (2) A majority of respondents overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed/strongly agreed that using MHC enhanced their communication (112/164, 68% versus 51/98, 52%; P=.009) and rapport (95/164, 58% versus 42/98, 43%; P=.02) with their VHA providers. Likewise, interviewees described how MHC helped focus therapy time and facilitated trust. (3) However, veterans also endorsed some challenges using MHC. Among respondents overall, these included difficulty understanding graphs of their assessment data (102/245, 42%), not receiving enough training on the app (73/259, 28%), and not being able to change responses to assessment questions (72/256, 28%). (4) Interviewees offered suggestions for improving the app (eg, facilitating ease of log-in, offering additional reminder features) and for increasing adoption (eg, marketing the app and its potential advantages for veterans receiving mental health care). CONCLUSIONS: Although experiences with the MHC app varied, veterans were positive overall about its use. Veterans described associations between the use of MHC and engagement in their own care, self-management, and interactions with their VHA mental health providers. Findings support the potential of MHC as a technology capable of supporting the VHA's Collect-Share-Act model of MBC.

Successful operational integration of healthcare analytics at Seattle Children's.

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.

Understanding Adoption and Preliminary Effectiveness of a Mobile App for Chronic Pain Management Among US Military Veterans: Pre-Post Mixed Methods Evaluation.

BACKGROUND: The Veterans Health Administration Pain Coach mobile health app was developed to support veterans with chronic pain. OBJECTIVE: Our objective was to evaluate early user experiences with the Pain Coach app and preliminary impacts of app use on pain-related outcomes. METHODS: Following a sequential, explanatory, mixed methods design, we mailed surveys to veterans at 2 time points with an outreach program in between and conducted semistructured interviews with a subsample of survey respondents. We analyzed survey data using descriptive statistics among veterans who completed both surveys and examined differences in key outcomes using paired samples t tests. We analyzed semistructured interview data using thematic analysis. RESULTS: Of 1507 veterans invited and eligible to complete the baseline survey, we received responses from 393 (26.1%). These veterans received our outreach program; 236 (236/393, 60.1%) completed follow-up surveys. We conducted interviews with 10 app users and 10 nonusers. Among survey respondents, 10.2% (24/236) used Pain Coach, and 58% (14/24) reported it was easy to use, though interviews identified various app usability issues. Veterans who used Pain Coach reported greater pain self-efficacy (mean 23.1 vs mean 16.6; P=.01) and lower pain interference (mean 34.6 vs mean 31.8; P=.03) after (vs before) use. The most frequent reason veterans reported for not using the app was that their health care team had not discussed it with them (96/212, 45.3%). CONCLUSIONS: Our findings suggest that future efforts to increase adoption of Pain Coach and other mobile apps among veterans should include health care team endorsement. Our findings regarding the impact of Pain Coach use on outcomes warrant further study.

Factors Associated With Self-reported Use of Web and Mobile Health Apps Among US Military Veterans: Cross-sectional Survey.

BACKGROUND: Despite their prevalence and reported patient interest in their use, uptake of health-related apps is limited. The Veterans Health Administration (VHA) has developed a variety of apps to support veterans; however, uptake remains low nationally. OBJECTIVE: We examined the prevalence of VHA health-related app use and how veterans learned about these apps in order to identify factors associated with their use. METHODS: As part of a VHA quality improvement initiative, we recruited a national cohort of veterans to obtain feedback on their use of technology for health and collected data from them via a cross-sectional survey. The survey data were supplemented with VHA administrative data. We used descriptive statistics to examine demographic and health characteristics, health-related technology use, and how veterans learned about apps. We assessed factors associated with app use using bivariate analyses and multiple logistic regression models. RESULTS: We had complete data on 1259 veterans. A majority of the sample was male (1069/1259, 84.9%), aged older than 65 years (740/1259, 58.8%), White (1086/1259, 86.3%), and non-Hispanic (1218/1259, 96.7%). Most respondents (1125/1259, 89.4%) reported being very comfortable and confident using computers, over half (675/1259, 53.6%) reported being an early adopter of technology, and almost half (595/1259, 47.3%) reported having used a VHA health-related app. Just over one-third (435/1259, 34.6%) reported that their VHA care team members encouraged them to use health-related apps. Respondents reported learning about available VHA health-related apps by reading about them on the VHA's patient portal (468/1259, 37.2%), being told about them by their VHA health care team (316/1259, 25.1%), and reading about them on the VHA's website (139/1259, 11%). Veterans who self-reported having used VHA health-related apps were more likely to receive care at the VHA (OR [odds ratio] 1.3, 95% CI 1.0-1.7), be in worse health (as assessed by Hierarchical Condition Community score; OR 1.1, 95% CI 1.0-1.2), report owning a desktop or laptop computer (OR 1.8, 95% CI 1.1-3.1), have posttraumatic stress disorder (OR 1.4, 95% CI 1.1-1.9), and report having VHA health care team members encourage them to use the apps (OR 2.7, 95% CI 2.1-3.4). CONCLUSIONS: We found strong associations between self-reported use by veterans of VHA health-related apps and multiple variables in our survey. The strongest association was observed between a veteran self-reporting app use and having received encouragement from their VHA health care team to use the apps. Veterans who reported receiving encouragement from their VHA care team members had nearly 3 times higher odds of using VHA apps than veterans who did not report receiving such encouragement. Our results add to growing evidence suggesting that endorsement of apps by a health care system or health care team can positively impact patient uptake and use.

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's 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.

Veterans' response to an automated text messaging protocol during the COVID-19 pandemic.

The US Department of Veterans Affairs (VA) is using an automated short message service application named "Annie" as part of its coronavirus disease 2019 (COVID-19) response with a protocol for coronavirus precautions, which can help the veteran monitor symptoms and can advise the veteran when to contact his or her VA care team or a nurse triage line. We surveyed 1134 veterans on their use of the Annie application and coronavirus precautions protocol. Survey results support what is likely a substantial resource savings for the VA, as well as non-VA community healthcare. Moreover, the majority of veterans reported at least 1 positive sentiment (felt more connected to VA, confident, or educated and/or felt less anxious) by receiving the protocol messages. The findings from this study have implications for other healthcare systems to help manage a patient population during the coronavirus pandemic.

Variations in the Use of mHealth Tools: The VA Mobile Health Study.

BACKGROUND: Mobile health (mHealth) technologies exhibit promise for offering patients and their caregivers point-of-need tools for health self-management. This research study involved the dissemination of iPads containing a suite of mHealth apps to family caregivers of veterans who receive care from the Veterans Affairs (VA) Health Administration and have serious physical or mental injuries. OBJECTIVE: The goal of the study was to identify factors and characteristics of veterans and their family caregivers that predict the use of mHealth apps. METHODS: Veteran/family caregiver dyads (N=882) enrolled in VA's Comprehensive Assistance for Family Caregivers program were recruited to participate in an mHealth pilot program. Veterans and caregivers who participated and received an iPad agreed to have their use of the apps monitored and were asked to complete a survey assessing Caregiver Preparedness, Caregiver Traits, and Caregiver Zarit Burden Inventory baseline surveys. RESULTS: Of the 882 dyads, 94.9% (837/882) of caregivers were women and 95.7% (844/882) of veteran recipients were men. Mean caregiver age was 40 (SD 10.2) years and mean veteran age was 39 (SD 9.15) years, and 39.8% (351/882) lived in rural locations. Most (89%, 788/882) of the caregivers were spouses. Overall, the most frequently used app was Summary of Care, followed by RX Refill, then Journal, Care4Caregivers, VA Pain Coach, and last, VA PTSD Coach. App use was significantly predicted by the caregiver being a spouse, increased caregiver computer skills, a rural living location, lower levels of caregiver preparedness, veteran mental health diagnosis (other than posttraumatic stress disorder), and veteran age. CONCLUSIONS: This mHealth Family Caregiver pilot project effectively establishes the VA's first patient-facing mHealth apps that are integrated within the VA data system. Use varied considerably, and apps that were most used were those that assisted them in their caregiving responsibilities.

Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications.

Electronic health record content is created by clinicians and is driven largely by intermittent and brief encounters with patients. Collecting data directly from patients in the form of patient-generated data (PGD) provides an unprecedented opportunity to capture personal, contextual patient information that can supplement clinical data and enhance patients' self-care. The US Department of Veterans Affairs (VA) is striving to implement the enterprise-wide capability to collect and use PGD in order to partner with patients in their care, improve the patient healthcare experience, and promote shared decision making. Through knowledge gained from Veterans' and healthcare teams' perspectives, VA created a taxonomy and an evolving framework on which to design and develop applications that capture and help physicians utilize PGD. Ten recommendations for effectively collecting and integrating PGD into patient care are discussed, addressing health system culture, data value, architecture, policy, data standards, clinical workflow, data visualization, and analytics and population reach.

The effect of physician panel size on health care outcomes.

An inadequate supply of primary care providers is leading to a crisis in access. Pressures are being placed on primary care practices to increase panel sizes. The impact of these pressures on clinical processes, patient satisfaction and waiting times is largely unknown, although evidence from recent literature shows that longer waiting time results in higher mortality rates and other adverse outcomes. FY2004, Department of Veterans Affairs primary care patient data are used. GLIMMIX and other generalized linear model models illustrate how expanded panel sizes are correlated with clinical process indicators, patient satisfaction and waiting times, controlling for practice, provider and patient characteristics. We generally find that larger panel sizes are related to statistically significant increases in waiting time. However, larger panel sizes appear to have generally small effects on patient process indicators and satisfaction. Panels with more support staff have lower waiting times and small, improved outcomes. We find panels with older and clinically riskier patients have, on average, slightly lower waiting times and increased likelihoods of positive outcomes than panels with younger, healthier veterans. Female veterans appear to have reduced likelihoods of positive outcomes. Higher priority and female veterans also have lower satisfaction. Further study is needed to analyse the impact of potential panel size endogeneity in this system.