Center of Innovation on Disability & Rehab Research (CINDRR)
Focus Area 1: Community Reintegration and Caregiving
Develop, test, and implement innovations that maximize activity and participation of Veterans with disabilities and their families. Through innovative engagement strategies improve Veterans’ and families’ experiences of rehabilitation and Veterans’ outcomes including, but not limited to community (re)integration, function, safety, quality of life, and access. Studies include VA-community partnerships, patient and family engagement, employment, and meaningful activities.
Spanish Online & Telephone Intervention for Caregivers of Veterans with Stroke (HSR&D IIR 15-117, I. Magaly Freytes, PhD, PI) 2017-2021. Previous research has found that family caregivers, particularly Hispanics, have high rates of depression and burden when their stroke survivors return home. Providing caregivers with culturally-appropriate information, support, and skills has the potential to reduce negative caregiver outcomes (i.e., burden and depression) and increase the likelihood that stroke survivors remain in the community. The study’s main goal is to test the efficacy of a brief, telephone and online problem-solving intervention, using the Spanish version of the VA RESCUE stroke caregiver website. Our long-term goal is to partner with leaders to implement a culturally relevant, accessible, and cost-effective intervention for caregivers of Veterans post-stroke throughout the VHA.
Veterans with mTBI: Barriers to Community Reintegration (HSR&D SDR 13-228, Risa Richardson, PhD, PI), 2016-2018. Brief description of project goals: Conduct secondary analyses of VA TBI Model System database to determine differences in community reintegration outcomes in Veterans and Service Members with Mild TBI versus Moderate to Severe TBI. Specific aims: (1) Determine the prevalence of community participation limitations [PART-O; Employment, Driving Status] in a consecutive cohort of participants with TBI enrolled in the VHA TBI Model System Program, (2) Compare differences in community participation limitations across injury severity groups (mild TBI versus moderate/severe TBI), (3) Describe the moderating effects of demographic [age, marital status, race/ethnicity], TBI severity, recurrent TBIs, psychological comorbidities (PTSD [PCL], Depression [PHQ-9], Anxiety [GAD-7]), neurobehavioral functioning [NSI] and substance abuse/dependence [CAGE] on community participation outcomes at 1 and 2 years post-injury.
Focus Area 2: Rehabilitation Analytics
Develop analytic systems and predictive analytics to proactively identify patients at risk for adverse events [informatics]; and develop and test patient-reported outcomes to increase engagement in care and evaluate outcomes of rehab interventions [measurement].
- Patient-reported outcomes
- Natural language processing
- Text mining
- Network / Bayesian techniques
Pain Care Quality and Integrated and Complementary Health Approaches (HSR&D, IIR 14-438, Stephen Luther, PhD, PI), 2015-2020. The project extends prior research by the investigator team using Natural Language Processing (NLP) and Machine Learning (ML) to automate a previously validated approach to identify and quantify key dimensions of Pain Care Quality. Using functional assessment, integrated treatment plans, reassessment (outcomes), and patient education from the EHR, the team will search the Musculoskeletal Diagnoses Cohort (MSD), (CRE 12-012) a database of nearly 6 million Veterans, to identify a representative sample of Veterans with MSD. Three separate annotation tasks will be done in support of this study. The first annotation task will support the creation of a ML model to help select progress notes that are rich in information about pain management for subsequent annotation efforts. The second and third annotation tasks will provide specific examples of text to develop and refine the NLP system. Once this automated solution is validated, the team will apply it to a national sample to test important questions about Pain Care Quality among Veterans with comorbid mental health conditions, access to CHA, and the SCM-PM.
Focus Area 3: Rehabilitation Technology
Develop, test, and implement rehabilitation technology innovations including assistive technology and virtual medical modalities (e.g., virtual world, virtual reality, HealtheVet, mobile applications, kiosks, and telerehabilitation) to improve:
1. Veterans’ experiences of and participation in rehabilitation
2. Access to rehabilitation care
3. Veterans’ outcomes including, but not limited to, function, safety, community (re)integration, quality of life, and access
4. Equitable access to assistive technology
Virtual Medical Modality Implementation Strategies for Patient Aligned Care Teams to Promote Veteran Centered Care (HSR&D IIR 15-443, Jolie N. Haun, PhD EdS, PI) 2017-2020. The Veterans Health Administration (VHA) is currently expanding integration efforts for virtual medical modalities (VMM) such as My HealtheVet, Vet Link kiosks, telehealth, and mobile applications to increase consumer use and optimize function. Integrated use of VMM can increase efficiency, maximize resources, and enhance Veteran outcomes by promoting personalized, proactive patient-driven care for Veterans. Data sources will be triangulated to increase the knowledge base of PACT team experiences using VMM and advance implementation science by evaluating locally delivered strategies to support subsequent implementation efforts at regional and national levels.