Study protocol: Type III hybrid effectiveness-implementation study implementing age-friendly evidence-based practices in the VA to improve outcomes in older adults

Abstract: Background: Unmet care needs among older adults accelerate cognitive and functional decline and increase medical harms, leading to poorer quality of life, more frequent hospitalizations, and premature nursing home admission. The Department of Veterans Affairs (VA) is invested in becoming an “Age-Friendly Health System” to better address four tenets associated with reduced harm and improved outcomes among the 4 million Veterans aged 65 and over receiving VA care. These four tenets focus on “4Ms” that are fundamental to the care of older adults, including (1) what Matters (ensuring that care is consistent with each person’s goals and preferences); (2) Medications (only using necessary medications and ensuring that they do not interfere with what matters, mobility, or mentation); (3) Mentation (preventing, identifying, treating, and managing dementia, depression, and delirium); and (4) Mobility (promoting safe movement to maintain function and independence). The Safer Aging through Geriatrics-Informed Evidence-Based Practices (SAGE) Quality Enhancement Research Initiative (QUERI) seeks to implement four evidence-based practices (EBPs) that have shown efficacy in addressing these core tenets of an “Age-Friendly Health System,” leading to reduced harm and improved outcomes in older adults. Methods: We will implement four EBPs in 9 VA medical centers and associated outpatient clinics using a type III hybrid effectiveness-implementation stepped-wedge trial design. We selected four EBPs that align with Age-Friendly Health System principles: Surgical Pause, EMPOWER (Eliminating Medications Through Patient Ownership of End Results), TAP (Tailored Activities Program), and CAPABLE (Community Aging in Place – Advancing Better Living for Elders). Guided by the Pragmatic Robust Implementation and Sustainability Model (PRISM), we are comparing implementation as usual vs. active facilitation. Reach is our primary implementation outcome, while “facility-free days” is our primary effectiveness outcome across evidence-based practice interventions. Discussion: To our knowledge, this is the first large-scale randomized effort to implement “Age-Friendly” aligned evidence-based practices. Understanding the barriers and facilitators to implementing these evidence-based practices is essential to successfully help shift current healthcare systems to become Age-Friendly. Effective implementation of this project will improve the care and outcomes of older Veterans and help them age safely within their communities.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed