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BackgroundThe Department of Veterans Affairs (VA) has developed health information technologies (HIT) and resources to improve veteran access to health care programs and services, and to support a patient-centered approach to health care delivery. To improve VA HIT access and meaningful use by veterans, it is necessary to understand their preferences for interacting with various HIT resources to accomplish health management related tasks and to exchange information. ObjectiveThe objective of this paper was to describe a novel protocol for: (1) developing a HIT Digital Health Matrix Model; (2) conducting an Analytic Hierarchy Process called pairwise comparison to understand how and why veterans want to use electronic health resources to complete tasks related to health management; and (3) developing visual modeling simulations that depict veterans’ preferences for using VA HIT to manage their health conditions and exchange health information. MethodsThe study uses participatory research methods to understand how veterans prefer to use VA HIT to accomplish health management tasks within a given context, and how they would like to interact with HIT interfaces (eg, look, feel, and function) in the future. This study includes two rounds of veteran focus groups with self-administered surveys and visual modeling simulation techniques. This study will also convene an expert panel to assist in the development of a VA HIT Digital Health Matrix Model, so that both expert panel members and veteran participants can complete an Analytic Hierarchy Process, pairwise comparisons to evaluate and rank the applicability of electronic health resources for a series of health management tasks. ResultsThis protocol describes the iterative, participatory, and patient-centered process for: (1) developing a VA HIT Digital Health Matrix Model that outlines current VA patient-facing platforms available to veterans, describing their features and relevant contexts for use; and (2) developing visual model simulations based on direct veteran feedback that depict patient preferences for enhancing the synchronization, integration, and standardization of VA patient-facing platforms. Focus group topics include current uses, preferences, facilitators, and barriers to using electronic health resources; recommendations for synchronizing, integrating, and standardizing VA HIT; and preferences on data sharing and delegation within the VA system. ConclusionsThis work highlights the practical, technological, and personal factors that facilitate and inhibit use of current VA HIT, and informs an integrated system redesign. The Digital Health Matrix Model and visual modeling simulations use knowledge of veteran preferences and experiences to directly inform enhancements to VA HIT and provide a more holistic and integrated user experience. These efforts are designed to support the adoption and sustained use of VA HIT to support patient self-management and clinical care coordination in ways that are directly aligned with veteran preferences.

Background: All federal agencies are required to support appropriation requests with evidence and evaluation (US Public Law 115-435; the Evidence Act). The StrAtegic PoLicy EvIdence-Based Evaluation CeNTer (SALIENT) is 1 of 6 centers that help the Department of Veterans Affairs (VA) meet this requirement., Objective: Working with the existing VA evaluation structure, SALIENT evaluations will contribute to (1) optimize policies and programs for veteran populations; (2) improve outcomes regarding health, equity, cost, and provider well-being; (3) advance the science of dissemination and knowledge translation; and (4) expand the implementation and dissemination science workforce., Methods: We leverage the Lean Sprint methodology (iterative, incremental, rule-governed approach to clearly defined, and time-boxed work) and 3 cores to develop our evaluation plans collaboratively with operational partners and key stakeholders including veterans, policy experts, and clinicians. The Operations Core will work with evaluation teams to develop timelines, facilitate work, monitor progress, and guide quality improvement within SALIENT. The Methods Core will work with evaluation teams to identify the most appropriate qualitative, quantitative, and mixed methods approaches to address each evaluation, ensure that the analyses are conducted appropriately, and troubleshoot when problems with data acquisition and analysis arise. The Knowledge Translation (KT) Core will target key partners and decision makers using a needs-based market segmentation approach to ensure that needs are incorporated in the dissemination of knowledge. The KT Core will create communications briefs, playbooks, and other materials targeted at these market segments to facilitate implementation of evidence-based practices and maximize the impact of evaluation results., Results: The SALIENT team has developed a center infrastructure to support high-priority evaluations, often to be responsive to shifting operational needs and priorities. Our team has engaged in our core missions and operations to rapidly evaluate a high-priority areas, develop a comprehensive Lean Sprint systems redesign approach to training, and accelerate rapid knowledge translation., Conclusions: With an array of interdisciplinary expertise, operational partnerships, and integrated resources, SALIENT has an established and evolving infrastructure to rapidly develop and implement high-impact evaluations. Projects are developed with sustained efficiency approaches that can pivot to new priorities as needed and effectively translate knowledge for key stakeholders and policy makers, while creating a learning health system infrastructure to foster the next generation of evaluation and implementation scientists., International Registered Report Identifier (irrid): PRR1-10.2196/59830., (©Mary Jo Pugh, Jolie N Haun, P Jon White, Gerald Cochran, April F Mohanty, Lisa M McAndrew, Adam J Gordon, Richard E Nelson, Megan E Vanneman, Diana E Naranjo, Rachel C Benzinger, Audrey L Jones, Jacob Kean, Susan L Zickmund, Angela Fagerlin. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 19.09.2024.)

Background: The responsibility of care for Veterans and Service Members (V/SMs) with traumatic brain injury (TBI) often defaults to informal family caregivers. Caregiving demands considerable knowledge, skill, and support to facilitate the health and well-being of V/SMs and themselves. Persistent and common TBI caregiver issues include strain, depression, and anxiety. While evidence-based, brief interventions have been developed and implemented for family caregivers in Veteran neurodegenerative populations, few interventions have been developed, adapted, or tested to support the unique needs of caregivers of V/SMs with TBI., Objective: This study will adapt and test an evidence-based, personalized, 6-session telehealth caregiver intervention, "Resources for Enhancing All Caregivers' Health" (REACH), to meet the unique needs of caregivers of V/SMs with TBI. If successful, a community-based participatory research team will develop an implementation plan to roll out REACH TBI across the national Veterans Affairs Polytrauma System of Care., Methods: This mixed methods, crossover waitlist control clinical trial will use a Type 1 Hybrid Effectiveness-Implementation approach to adapt and then test the effects of REACH TBI on key TBI caregiver outcomes., Results: This study was funded by the Department of Defense in September 2023. Participant enrollment and data collection will begin in 2024., Conclusions: If effective, REACH TBI will be the first evidence-based intervention for caregivers of V/SMs with TBI that can be scaled to implement across the Veterans Affairs Polytrauma System of Care and fill a notable gap in clinical services., International Registered Report Identifier (irrid): PRR1-10.2196/57692., (©Paul B Perrin, Jolie N Haun, Daniel W Klyce, Christine Melillo, Risa Nakase-Richardson, Ronald T Seel, Jennifer Martindale-Adams, Linda O Nichols, Robert A Perera, Bridget Xia, Bridget Hahm, Jeffrey Zuber. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 15.08.2024.)